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Long Y, Wang C, Liu C, Li H, Pu A, Dong Z, Wei X, Wan X. Molecular mechanisms controlling grain size and weight and their biotechnological breeding applications in maize and other cereal crops. J Adv Res 2024; 62:27-46. [PMID: 37739122 PMCID: PMC11331183 DOI: 10.1016/j.jare.2023.09.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2023] [Revised: 09/03/2023] [Accepted: 09/18/2023] [Indexed: 09/24/2023] Open
Abstract
BACKGROUND Cereal crops are a primary energy source for humans. Grain size and weight affect both evolutionary fitness and grain yield of cereals. Although studies on gene mining and molecular mechanisms controlling grain size and weight are constantly emerging in cereal crops, only a few systematic reviews on the underlying molecular mechanisms and their breeding applications are available so far. AIM OF REVIEW This review provides a general state-of-the-art overview of molecular mechanisms and targeted strategies for improving grain size and weight of cereals as well as insights for future yield-improving biotechnology-assisted breeding. KEY SCIENTIFIC CONCEPTS OF REVIEW In this review, the evolution of research on grain size and weight over the last 20 years is traced based on a bibliometric analysis of 1158 publications and the main signaling pathways and transcriptional factors involved are summarized. In addition, the roles of post-transcriptional regulation and photosynthetic product accumulation affecting grain size and weight in maize and rice are outlined. State-of-the-art strategies for discovering novel genes related to grain size and weight in maize and other cereal crops as well as advanced breeding biotechnology strategies being used for improving yield including marker-assisted selection, genomic selection, transgenic breeding, and genome editing are also discussed.
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Affiliation(s)
- Yan Long
- Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100083, China; Industry Research Institute of Biotechnology Breeding, Yili Normal University, Yining 835000, China; Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Cheng Wang
- Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100083, China; Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Chang Liu
- Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100083, China; Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Huangai Li
- Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100083, China; Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Aqing Pu
- Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100083, China; Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Zhenying Dong
- Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100083, China; Industry Research Institute of Biotechnology Breeding, Yili Normal University, Yining 835000, China; Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Xun Wei
- Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100083, China; Industry Research Institute of Biotechnology Breeding, Yili Normal University, Yining 835000, China; Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China
| | - Xiangyuan Wan
- Research Institute of Biology and Agriculture, University of Science and Technology Beijing, Beijing 100083, China; Industry Research Institute of Biotechnology Breeding, Yili Normal University, Yining 835000, China; Beijing Engineering Laboratory of Main Crop Bio-Tech Breeding, Beijing International Science and Technology Cooperation Base of Bio-Tech Breeding, Zhongzhi International Institute of Agricultural Biosciences, Beijing 100192, China.
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Awada L, Phillips PWB, Bodan AM. The evolution of plant phenomics: global insights, trends, and collaborations (2000-2021). FRONTIERS IN PLANT SCIENCE 2024; 15:1410738. [PMID: 39104843 PMCID: PMC11298374 DOI: 10.3389/fpls.2024.1410738] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Accepted: 07/08/2024] [Indexed: 08/07/2024]
Abstract
Introduction Phenomics, an interdisciplinary field that investigates the relationships between genomics and environmental factors, has significantly advanced plant breeding by offering comprehensive insights into plant traits from molecular to physiological levels. This study examines the global evolution, geographic distribution, collaborative efforts, and primary research hubs in plant phenomics from 2000 to 2021, using data derived from patents and scientific publications. Methods The study utilized data from the EspaceNet and Lens databases for patents, and Web of Science (WoS) and Scopus for scientific publications. The final datasets included 651 relevant patents and 7173 peer-reviewed articles. Data were geocoded to assign country-level geographical coordinates and underwent multiple processing and cleaning steps using Python, Excel, R, and ArcGIS. Social network analysis (SNA) was conducted to assess collaboration patterns using Pajek and UCINET. Results Research activities in plant phenomics have increased significantly, with China emerging as a major player, filing nearly 70% of patents from 2010 to 2021. The U.S. and EU remain significant contributors, accounting for over half of the research output. The study identified around 50 global research hubs, mainly in the U.S. (36%), Western Europe (34%), and China (16%). Collaboration networks have become more complex and interdisciplinary, reflecting a strategic approach to solving research challenges. Discussion The findings underscore the importance of global collaboration and technological advancement in plant phenomics. China's rise in patent filings highlights its growing influence, while the ongoing contributions from the U.S. and EU demonstrate their continued leadership. The development of complex collaborative networks emphasizes the scientific community's adaptive strategies to address multifaceted research issues. These insights are crucial for researchers, policymakers, and industry stakeholders aiming to innovate in agricultural practices and improve crop varieties.
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Affiliation(s)
- Lana Awada
- Centre for the Study of Science and Innovation Policy, Johnson Shoyama Graduate School of Public Policy, University of Saskatchewan, Saskatoon, SK, Canada
| | - Peter W. B. Phillips
- Centre for the Study of Science and Innovation Policy, Johnson Shoyama Graduate School of Public Policy, University of Saskatchewan, Saskatoon, SK, Canada
| | - Ana Maria Bodan
- Canadian Hub for Applied and Social Research, University of Saskatchewan, Saskatoon, SK, Canada
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Liu D, Mao X, Zhang G, He L, Wang L, Zhang F, Wang Q, Zhou L. Antifungal Activity and Mechanism of Physcion against Sclerotium rolfsii, the Causal Agent of Peanut Southern Blight. JOURNAL OF AGRICULTURAL AND FOOD CHEMISTRY 2024; 72:15601-15612. [PMID: 38950526 DOI: 10.1021/acs.jafc.4c02519] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/03/2024]
Abstract
Peanut southern blight, caused by the soil-borne pathogen Sclerotium rolfsii, is a widespread and devastating epidemic. Frequently, it is laborious to effectively control by labor-intensive foliar sprays of agrochemicals due to untimely find. In the present study, seed treatment with physcion (PHY) at doses of 0.08, 0.16, and 0.32 g AI kg-1 seed significantly improved the growth and photosynthetic activity of peanuts. Furthermore, PHY seed treatment resulted in an elevated enzymatic activity of key enzymes in peanut roots, including peroxidase, superoxide dismutase, polyphenol oxidase, catalase, lipoxygenase, and phenylalanine ammonia-lyase, as well as an increase in callus accumulation and lignin synthesis at the infection site, ultimately enhancing the root activity. This study revealed that PHY seed treatment could promote the accumulation of reactive oxygen species, salicylic acid (SA), and jasmonic acid (JA)/ethylene (ET) in peanut roots, while also decreasing the content of malondialdehyde levels in response to S. rolfsii infection. The results were further confirmed by transcriptome data and metabolomics. These findings suggest that PHY seed treatment activates the plant defense pathways mediated by SA and JA/ET in peanut roots, enhancing the resistance of peanut plants to S. rolfsii. In short, PHY is expected to be developed into a new plant-derived immunostimulant or fungicide to increase the options and means for peanut disease control.
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Affiliation(s)
- Di Liu
- College of Plant Protection, Henan Agricultural University, Zhengzhou 450046, China
- Henan Key Laboratory of Creation and Application of New Pesticide, Henan Agricultural University, No. 63, Agricultural Road, Zhengzhou 450046, China
- Henan Research Center of Green Pesticide Engineering and Technology, Henan Agricultural University, Zhengzhou 450046, China
| | - Xuewei Mao
- College of Plant Protection, Henan Agricultural University, Zhengzhou 450046, China
- Henan Key Laboratory of Creation and Application of New Pesticide, Henan Agricultural University, No. 63, Agricultural Road, Zhengzhou 450046, China
- Henan Research Center of Green Pesticide Engineering and Technology, Henan Agricultural University, Zhengzhou 450046, China
| | - Guoyan Zhang
- Plant Protection and Quarantine Station of Henan Province, Zhengzhou 450002, China
| | - Leiming He
- College of Plant Protection, Henan Agricultural University, Zhengzhou 450046, China
- Henan Key Laboratory of Creation and Application of New Pesticide, Henan Agricultural University, No. 63, Agricultural Road, Zhengzhou 450046, China
- Henan Research Center of Green Pesticide Engineering and Technology, Henan Agricultural University, Zhengzhou 450046, China
| | - Li Wang
- College of Plant Protection, Henan Agricultural University, Zhengzhou 450046, China
- Henan Key Laboratory of Creation and Application of New Pesticide, Henan Agricultural University, No. 63, Agricultural Road, Zhengzhou 450046, China
- Henan Research Center of Green Pesticide Engineering and Technology, Henan Agricultural University, Zhengzhou 450046, China
| | - Fulong Zhang
- Inner Mongolia Kingbo Biotech. Co., Ltd, Bayannur 015200, China
| | - Qinqin Wang
- College of Plant Protection, Henan Agricultural University, Zhengzhou 450046, China
- Henan Key Laboratory of Creation and Application of New Pesticide, Henan Agricultural University, No. 63, Agricultural Road, Zhengzhou 450046, China
- Henan Research Center of Green Pesticide Engineering and Technology, Henan Agricultural University, Zhengzhou 450046, China
| | - Lin Zhou
- College of Plant Protection, Henan Agricultural University, Zhengzhou 450046, China
- Henan Key Laboratory of Creation and Application of New Pesticide, Henan Agricultural University, No. 63, Agricultural Road, Zhengzhou 450046, China
- Henan Research Center of Green Pesticide Engineering and Technology, Henan Agricultural University, Zhengzhou 450046, China
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Ndikumana JN, Lee U, Yoo JH, Yeboah S, Park SH, Lee TS, Yeoung YR, Kim HS. Development of a deep-learning phenotyping tool for analyzing image-based strawberry phenotypes. FRONTIERS IN PLANT SCIENCE 2024; 15:1418383. [PMID: 39077512 PMCID: PMC11284602 DOI: 10.3389/fpls.2024.1418383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 06/24/2024] [Indexed: 07/31/2024]
Abstract
Introduction In strawberry farming, phenotypic traits (such as crown diameter, petiole length, plant height, flower, leaf, and fruit size) measurement is essential as it serves as a decision-making tool for plant monitoring and management. To date, strawberry plant phenotyping has relied on traditional approaches. In this study, an image-based Strawberry Phenotyping Tool (SPT) was developed using two deep-learning (DL) architectures, namely "YOLOv4" and "U-net" integrated into a single system. We aimed to create the most suitable DL-based tool with enhanced robustness to facilitate digital strawberry plant phenotyping directly at the natural scene or indirectly using captured and stored images. Methods Our SPT was developed primarily through two steps (subsequently called versions) using image data with different backgrounds captured with simple smartphone cameras. The two versions (V1 and V2) were developed using the same DL networks but differed by the amount of image data and annotation method used during their development. For V1, 7,116 images were annotated using the single-target non-labeling method, whereas for V2, 7,850 images were annotated using the multitarget labeling method. Results The results of the held-out dataset revealed that the developed SPT facilitates strawberry phenotype measurements. By increasing the dataset size combined with multitarget labeling annotation, the detection accuracy of our system changed from 60.24% in V1 to 82.28% in V2. During the validation process, the system was evaluated using 70 images per phenotype and their corresponding actual values. The correlation coefficients and detection frequencies were higher for V2 than for V1, confirming the superiority of V2. Furthermore, an image-based regression model was developed to predict the fresh weight of strawberries based on the fruit size (R2 = 0.92). Discussion The results demonstrate the efficiency of our system in recognizing the aforementioned six strawberry phenotypic traits regardless of the complex scenario of the environment of the strawberry plant. This tool could help farmers and researchers make accurate and efficient decisions related to strawberry plant management, possibly causing increased productivity and yield potential.
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Affiliation(s)
- Jean Nepo Ndikumana
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung, Republic of Korea
- Department of Plant Science, Gangneung-Wonju National University, Gangneung, Republic of Korea
| | - Unseok Lee
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung, Republic of Korea
| | - Ji Hye Yoo
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung, Republic of Korea
| | - Samuel Yeboah
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung, Republic of Korea
- Department of Plant Science, Gangneung-Wonju National University, Gangneung, Republic of Korea
| | - Soo Hyun Park
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung, Republic of Korea
| | - Taek Sung Lee
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung, Republic of Korea
| | - Young Rog Yeoung
- Department of Plant Science, Gangneung-Wonju National University, Gangneung, Republic of Korea
| | - Hyoung Seok Kim
- Smart Farm Research Center, Korea Institute of Science and Technology (KIST), Gangneung, Republic of Korea
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Morton M, Fiene G, Ahmed HI, Rey E, Abrouk M, Angel Y, Johansen K, Saber NO, Malbeteau Y, Al-Mashharawi S, Ziliani MG, Aragon B, Oakey H, Berger B, Brien C, Krattinger SG, Mousa MAA, McCabe MF, Negrão S, Tester M, Julkowska MM. Deciphering salt stress responses in Solanum pimpinellifolium through high-throughput phenotyping. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2024. [PMID: 38970620 DOI: 10.1111/tpj.16894] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2023] [Accepted: 06/03/2024] [Indexed: 07/08/2024]
Abstract
Soil salinity is a major environmental stressor affecting agricultural productivity worldwide. Understanding plant responses to salt stress is crucial for developing resilient crop varieties. Wild relatives of cultivated crops, such as wild tomato, Solanum pimpinellifolium, can serve as a useful resource to further expand the resilience potential of the cultivated germplasm, S. lycopersicum. In this study, we employed high-throughput phenotyping in the greenhouse and field conditions to explore salt stress responses of a S. pimpinellifolium diversity panel. Our study revealed extensive phenotypic variations in response to salt stress, with traits such as transpiration rate, shoot mass, and ion accumulation showing significant correlations with plant performance. We found that while transpiration was a key determinant of plant performance in the greenhouse, shoot mass strongly correlated with yield under field conditions. Conversely, ion accumulation was the least influential factor under greenhouse conditions. Through a Genome Wide Association Study, we identified candidate genes not previously associated with salt stress, highlighting the power of high-throughput phenotyping in uncovering novel aspects of plant stress responses. This study contributes to our understanding of salt stress tolerance in S. pimpinellifolium and lays the groundwork for further investigations into the genetic basis of these traits, ultimately informing breeding efforts for salinity tolerance in tomato and other crops.
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Affiliation(s)
- Mitchell Morton
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Gabriele Fiene
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Hanin Ibrahim Ahmed
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Elodie Rey
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Michael Abrouk
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Yoseline Angel
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
- Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
| | - Kasper Johansen
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Noha O Saber
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Yoann Malbeteau
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Samir Al-Mashharawi
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Matteo G Ziliani
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Hydrosat S.à r.l., 9 Rue du Laboratoire, Luxembourg City, 1911, Luxembourg
| | - Bruno Aragon
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Helena Oakey
- Robinson Institute, University of Adelaide, Adelaide, Australia
| | - Bettina Berger
- Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, Australia
| | - Chris Brien
- Australian Plant Phenomics Facility, University of Adelaide, Urrbrae, Australia
| | - Simon G Krattinger
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Magdi A A Mousa
- Department of Agriculture, Faculty of Environmental Sciences, King Abdulaziz University, Jeddah, 80208, Saudi Arabia
- Department of Vegetable Crops, Faculty of Agriculture, Assiut University, Assiut, 71526, Egypt
| | - Matthew F McCabe
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Sónia Negrão
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- University College, Dublin, Republic of Ireland
| | - Mark Tester
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Magdalena M Julkowska
- Plant Science Program, Biological and Environmental Science and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
- Boyce Thompson Institute, Ithaca, New York, USA
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Alrajhi A, Alharbi S, Beecham S, Alotaibi F. Regulation of root growth and elongation in wheat. FRONTIERS IN PLANT SCIENCE 2024; 15:1397337. [PMID: 38835859 PMCID: PMC11148372 DOI: 10.3389/fpls.2024.1397337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/07/2024] [Accepted: 05/06/2024] [Indexed: 06/06/2024]
Abstract
Currently, the control of rhizosphere selection on farms has been applied to achieve enhancements in phenotype, extending from improvements in single root characteristics to the dynamic nature of entire crop systems. Several specific signals, regulatory elements, and mechanisms that regulate the initiation, morphogenesis, and growth of new lateral or adventitious root species have been identified, but much more work remains. Today, phenotyping technology drives the development of root traits. Available models for simulation can support all phenotyping decisions (root trait improvement). The detection and use of markers for quantitative trait loci (QTLs) are effective for enhancing selection efficiency and increasing reproductive genetic gains. Furthermore, QTLs may help wheat breeders select the appropriate roots for efficient nutrient acquisition. Single-nucleotide polymorphisms (SNPs) or alignment of sequences can only be helpful when they are associated with phenotypic variation for root development and elongation. Here, we focus on major root development processes and detail important new insights recently generated regarding the wheat genome. The first part of this review paper discusses the root morphology, apical meristem, transcriptional control, auxin distribution, phenotyping of the root system, and simulation models. In the second part, the molecular genetics of the wheat root system, SNPs, TFs, and QTLs related to root development as well as genome editing (GE) techniques for the improvement of root traits in wheat are discussed. Finally, we address the effect of omics strategies on root biomass production and summarize existing knowledge of the main molecular mechanisms involved in wheat root development and elongation.
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Affiliation(s)
- Abdullah Alrajhi
- King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
- Sustainable Infrastructure and Resource Management, University of South Australia, University of South Australia Science, Technology, Engineering, and Mathematics (UniSA STEM), Mawson Lakes, SA, Australia
| | - Saif Alharbi
- The National Research and Development Center for Sustainable Agriculture (Estidamah), Riyadh, Saudi Arabia
| | - Simon Beecham
- Sustainable Infrastructure and Resource Management, University of South Australia, University of South Australia Science, Technology, Engineering, and Mathematics (UniSA STEM), Mawson Lakes, SA, Australia
| | - Fahad Alotaibi
- King Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia
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Bouillon P, Fanciullino AL, Belin E, Bréard D, Boisard S, Bonnet B, Hanteville S, Bernard F, Celton JM. Image analysis and polyphenol profiling unveil red-flesh apple phenotype complexity. PLANT METHODS 2024; 20:71. [PMID: 38755652 PMCID: PMC11100172 DOI: 10.1186/s13007-024-01196-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 04/28/2024] [Indexed: 05/18/2024]
Abstract
BACKGROUND The genetic basis of colour development in red-flesh apples (Malus domestica Borkh) has been widely characterised; however, current models do not explain the observed variations in red pigmentation intensity and distribution. Available methods to evaluate the red-flesh trait rely on the estimation of an average overall colour using a discrete class notation index. However, colour variations among red-flesh cultivars are continuous while development of red colour is non-homogeneous and genotype-dependent. A robust estimation of red-flesh colour intensity and distribution is essential to fully capture the diversity among genotypes and provide a basis to enable identification of loci influencing the red-flesh trait. RESULTS In this study, we developed a multivariable approach to evaluate the red-flesh trait in apple. This method was implemented to study the phenotypic diversity in a segregating hybrid F1 family (91 genotypes). We developed a Python pipeline based on image and colour analysis to quantitatively dissect the red-flesh pigmentation from RGB (Red Green Blue) images and compared the efficiency of RGB and CIEL*a*b* colour spaces in discriminating genotypes previously classified with a visual notation. Chemical destructive methods, including targeted-metabolite analysis using ultra-high performance liquid chromatography with ultraviolet detection (UPLC-UV), were performed to quantify major phenolic compounds in fruits' flesh, as well as pH and water contents. Multivariate analyses were performed to study covariations of biochemical factors in relation to colour expression in CIEL*a*b* colour space. Our results indicate that anthocyanin, flavonol and flavanol concentrations, as well as pH, are closely related to flesh pigmentation in apple. CONCLUSTION Extraction of colour descriptors combined to chemical analyses helped in discriminating genotypes in relation to their flesh colour. These results suggest that the red-flesh trait in apple is a complex trait associated with several biochemical factors.
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Affiliation(s)
- Pierre Bouillon
- Univ Angers, Institut Agro, INRAE, IRHS, SFR QUASAV, F-49000 , Angers, France
- IFO, 49140, Seiches sur le Loir, France
| | | | - Etienne Belin
- Univ Angers, Institut Agro, INRAE, IRHS, SFR QUASAV, F-49000 , Angers, France
| | - Dimitri Bréard
- SONAS, SFR QUASAVUniv Angers, SONAS, SFR QUASAV, Univ Angers, F-49000, Angers, France
| | - Séverine Boisard
- SONAS, SFR QUASAVUniv Angers, SONAS, SFR QUASAV, Univ Angers, F-49000, Angers, France
| | - Béatrice Bonnet
- Univ Angers, Institut Agro, INRAE, IRHS, SFR QUASAV, F-49000 , Angers, France
| | - Sylvain Hanteville
- Univ Angers, Institut Agro, INRAE, IRHS, SFR QUASAV, F-49000 , Angers, France
| | | | - Jean-Marc Celton
- Univ Angers, Institut Agro, INRAE, IRHS, SFR QUASAV, F-49000 , Angers, France.
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8
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Jadhav Y, Thakur NR, Ingle KP, Ceasar SA. The role of phenomics and genomics in delineating the genetic basis of complex traits in millets. PHYSIOLOGIA PLANTARUM 2024; 176:e14349. [PMID: 38783512 DOI: 10.1111/ppl.14349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 04/22/2024] [Accepted: 04/26/2024] [Indexed: 05/25/2024]
Abstract
Millets, comprising a diverse group of small-seeded grains, have emerged as vital crops with immense nutritional, environmental, and economic significance. The comprehension of complex traits in millets, influenced by multifaceted genetic determinants, presents a compelling challenge and opportunity in agricultural research. This review delves into the transformative roles of phenomics and genomics in deciphering these intricate genetic architectures. On the phenomics front, high-throughput platforms generate rich datasets on plant morphology, physiology, and performance in diverse environments. This data, coupled with field trials and controlled conditions, helps to interpret how the environment interacts with genetics. Genomics provides the underlying blueprint for these complex traits. Genome sequencing and genotyping technologies have illuminated the millet genome landscape, revealing diverse gene pools and evolutionary relationships. Additionally, different omics approaches unveil the intricate information of gene expression, protein function, and metabolite accumulation driving phenotypic expression. This multi-omics approach is crucial for identifying candidate genes and unfolding the intricate pathways governing complex traits. The review highlights the synergy between phenomics and genomics. Genomically informed phenotyping targets specific traits, reducing the breeding size and cost. Conversely, phenomics identifies promising germplasm for genomic analysis, prioritizing variants with superior performance. This dynamic interplay accelerates breeding programs and facilitates the development of climate-smart, nutrient-rich millet varieties and hybrids. In conclusion, this review emphasizes the crucial roles of phenomics and genomics in unlocking the genetic enigma of millets.
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Affiliation(s)
- Yashoda Jadhav
- International Crops Research Institutes for the Semi-Arid Tropics, Patancheru, TS, India
| | - Niranjan Ravindra Thakur
- International Crops Research Institutes for the Semi-Arid Tropics, Patancheru, TS, India
- Vasantrao Naik Marathwada Agricultural University, Parbhani, MS, India
| | | | - Stanislaus Antony Ceasar
- Division of Plant Molecular Biology and Biotechnology, Department of Biosciences, Rajagiri College of Social Sciences, Kochi, KL, India
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Herrmann PSDP, dos Santos Luccas M, Ferreira EJ, Torre Neto A. Application of electronic nose and machine learning used to detect soybean gases under water stress and variability throughout the daytime. FRONTIERS IN PLANT SCIENCE 2024; 15:1323296. [PMID: 38645391 PMCID: PMC11026621 DOI: 10.3389/fpls.2024.1323296] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 03/18/2024] [Indexed: 04/23/2024]
Abstract
The development of non-invasive methods and accessible tools for application to plant phenotyping is considered a breakthrough. This work presents the preliminary results using an electronic nose (E-Nose) and machine learning (ML) as affordable tools. An E-Nose is an electronic system used for smell global analysis, which emulates the human nose structure. The soybean (Glycine Max) was used to conduct this experiment under water stress. Commercial E-Nose was used, and a chamber was designed and built to conduct the measurement of the gas sample from the soybean. This experiment was conducted for 22 days, observing the stages of plant growth during this period. This chamber is embedded with relative humidity [RH (%)], temperature (°C), and CO2 concentration (ppm) sensors, as well as the natural light intensity, which was monitored. These systems allowed intermittent monitoring of each parameter to create a database. The soil used was the red-yellow dystrophic type and was covered to avoid evapotranspiration effects. The measurement with the electronic nose was done daily, during the morning and afternoon, and in two phenological situations of the plant (with the healthful soy irrigated with deionized water and underwater stress) until the growth V5 stage to obtain the plant gases emissions. Data mining techniques were used, through the software "Weka™" and the decision tree strategy. From the evaluation of the sensors database, a dynamic variation of plant respiration pattern was observed, with the two distinct behaviors observed in the morning (~9:30 am) and afternoon (3:30 pm). With the initial results obtained with the E-Nose signals and ML, it was possible to distinguish the two situations, i.e., the irrigated plant standard and underwater stress, the influence of the two periods of daylight, and influence of temporal variability of the weather. As a result of this investigation, a classifier was developed that, through a non-invasive analysis of gas samples, can accurately determine the absence of water in soybean plants with a rate of 94.4% accuracy. Future investigations should be carried out under controlled conditions that enable early detection of the stress level.
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Janni M, Maestri E, Gullì M, Marmiroli M, Marmiroli N. Plant responses to climate change, how global warming may impact on food security: a critical review. FRONTIERS IN PLANT SCIENCE 2024; 14:1297569. [PMID: 38250438 PMCID: PMC10796516 DOI: 10.3389/fpls.2023.1297569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Accepted: 12/14/2023] [Indexed: 01/23/2024]
Abstract
Global agricultural production must double by 2050 to meet the demands of an increasing world human population but this challenge is further exacerbated by climate change. Environmental stress, heat, and drought are key drivers in food security and strongly impacts on crop productivity. Moreover, global warming is threatening the survival of many species including those which we rely on for food production, forcing migration of cultivation areas with further impoverishing of the environment and of the genetic variability of crop species with fall out effects on food security. This review considers the relationship of climatic changes and their bearing on sustainability of natural and agricultural ecosystems, as well as the role of omics-technologies, genomics, proteomics, metabolomics, phenomics and ionomics. The use of resource saving technologies such as precision agriculture and new fertilization technologies are discussed with a focus on their use in breeding plants with higher tolerance and adaptability and as mitigation tools for global warming and climate changes. Nevertheless, plants are exposed to multiple stresses. This study lays the basis for the proposition of a novel research paradigm which is referred to a holistic approach and that went beyond the exclusive concept of crop yield, but that included sustainability, socio-economic impacts of production, commercialization, and agroecosystem management.
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Affiliation(s)
- Michela Janni
- Institute of Bioscience and Bioresources (IBBR), National Research Council (CNR), Bari, Italy
- Institute of Materials for Electronics and Magnetism (IMEM), National Research Council (CNR), Parma, Italy
| | - Elena Maestri
- Department of Chemistry, Life Sciences and Environmental Sustainability, Interdepartmental Centers SITEIA.PARMA and CIDEA, University of Parma, Parma, Italy
| | - Mariolina Gullì
- Department of Chemistry, Life Sciences and Environmental Sustainability, Interdepartmental Centers SITEIA.PARMA and CIDEA, University of Parma, Parma, Italy
| | - Marta Marmiroli
- Department of Chemistry, Life Sciences and Environmental Sustainability, Interdepartmental Centers SITEIA.PARMA and CIDEA, University of Parma, Parma, Italy
| | - Nelson Marmiroli
- Consorzio Interuniversitario Nazionale per le Scienze Ambientali (CINSA) Interuniversity Consortium for Environmental Sciences, Parma/Venice, Italy
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11
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Bömer J, Esser F, Marks E, Rosu RA, Behnke S, Klingbeil L, Kuhlmann H, Stachniss C, Mahlein AK, Paulus S. A 3D printed plant model for accurate and reliable 3D plant phenotyping. Gigascience 2024; 13:giae035. [PMID: 38897734 PMCID: PMC11186670 DOI: 10.1093/gigascience/giae035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2024] [Revised: 04/12/2024] [Accepted: 05/24/2024] [Indexed: 06/21/2024] Open
Abstract
BACKGROUND This study addresses the importance of precise referencing in 3-dimensional (3D) plant phenotyping, which is crucial for advancing plant breeding and improving crop production. Traditionally, reference data in plant phenotyping rely on invasive methods. Recent advancements in 3D sensing technologies offer the possibility to collect parameters that cannot be referenced by manual measurements. This work focuses on evaluating a 3D printed sugar beet plant model as a referencing tool. RESULTS Fused deposition modeling has turned out to be a suitable 3D printing technique for creating reference objects in 3D plant phenotyping. Production deviations of the created reference model were in a low and acceptable range. We were able to achieve deviations ranging from -10 mm to +5 mm. In parallel, we demonstrated a high-dimensional stability of the reference model, reaching only ±4 mm deformation over the course of 1 year. Detailed print files, assembly descriptions, and benchmark parameters are provided, facilitating replication and benefiting the research community. CONCLUSION Consumer-grade 3D printing was utilized to create a stable and reproducible 3D reference model of a sugar beet plant, addressing challenges in referencing morphological parameters in 3D plant phenotyping. The reference model is applicable in 3 demonstrated use cases: evaluating and comparing 3D sensor systems, investigating the potential accuracy of parameter extraction algorithms, and continuously monitoring these algorithms in practical experiments in greenhouse and field experiments. Using this approach, it is possible to monitor the extraction of a nonverifiable parameter and create reference data. The process serves as a model for developing reference models for other agricultural crops.
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Affiliation(s)
- Jonas Bömer
- Institute of Sugar Beet Research (IfZ), Göttingen 37079, Germany
| | - Felix Esser
- Institute for Geodesy and Geoinformation (IGG), University of Bonn, Bonn 53115, Germany
- Center for Robotics, University of Bonn, Bonn 53115, Germany
| | - Elias Marks
- Institute for Geodesy and Geoinformation (IGG), University of Bonn, Bonn 53115, Germany
- Center for Robotics, University of Bonn, Bonn 53115, Germany
| | - Radu Alexandru Rosu
- Institute for Computer Science VI–Autonomous Intelligent Systems (AIS), University of Bonn, Bonn 53115, Germany
| | - Sven Behnke
- Center for Robotics, University of Bonn, Bonn 53115, Germany
- Institute for Computer Science VI–Autonomous Intelligent Systems (AIS), University of Bonn, Bonn 53115, Germany
| | - Lasse Klingbeil
- Institute for Geodesy and Geoinformation (IGG), University of Bonn, Bonn 53115, Germany
- Center for Robotics, University of Bonn, Bonn 53115, Germany
| | - Heiner Kuhlmann
- Institute for Geodesy and Geoinformation (IGG), University of Bonn, Bonn 53115, Germany
- Center for Robotics, University of Bonn, Bonn 53115, Germany
| | - Cyrill Stachniss
- Institute for Geodesy and Geoinformation (IGG), University of Bonn, Bonn 53115, Germany
- Center for Robotics, University of Bonn, Bonn 53115, Germany
| | | | - Stefan Paulus
- Institute of Sugar Beet Research (IfZ), Göttingen 37079, Germany
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Gómez-Candón D, Bellvert J, Pelechá A, Lopes MS. A Remote Sensing Approach for Assessing Daily Cumulative Evapotranspiration Integral in Wheat Genotype Screening for Drought Adaptation. PLANTS (BASEL, SWITZERLAND) 2023; 12:3871. [PMID: 38005768 PMCID: PMC10675030 DOI: 10.3390/plants12223871] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Revised: 11/06/2023] [Accepted: 11/14/2023] [Indexed: 11/26/2023]
Abstract
This study considers critical aspects of water management and crop productivity in wheat cultivation, specifically examining the daily cumulative actual evapotranspiration (ETa). Traditionally, ETa surface energy balance models have provided estimates at discrete time points, lacking a holistic integrated approach. Field trials were conducted with 22 distinct wheat varieties, grown under both irrigated and rainfed conditions over a two-year span. Leaf area index prediction was enhanced through a robust multiple regression model, incorporating data acquired from an unmanned aerial vehicle using an RGB sensor, and resulting in a predictive model with an R2 value of 0.85. For estimation of the daily cumulative ETa integral, an integrated approach involving remote sensing and energy balance models was adopted. An examination of the relationships between crop yield and evapotranspiration (ETa), while considering factors like year, irrigation methods, and wheat cultivars, unveiled a pronounced positive asymptotic pattern. This suggests the presence of a threshold beyond which additional water application does not significantly enhance crop yield. However, a genetic analysis of the 22 wheat varieties showed no correlation between ETa and yield. This implies opportunities for selecting resource-efficient wheat varieties while minimizing water use. Significantly, substantial disparities in water productivity among the tested wheat varieties indicate the possibility of intentionally choosing lines that can optimize grain production while minimizing water usage within breeding programs. The results of this research lay the foundation for the development of resource-efficient agricultural practices and the cultivation of crop varieties finely attuned to water-scarce regions.
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Affiliation(s)
- David Gómez-Candón
- Efficient Use of Water in Agriculture Program, Institute of Agrifood Research and Technology (IRTA), Fruitcentre, Parc AgroBiotech, 25003 Lleida, Spain; (J.B.); (A.P.)
| | - Joaquim Bellvert
- Efficient Use of Water in Agriculture Program, Institute of Agrifood Research and Technology (IRTA), Fruitcentre, Parc AgroBiotech, 25003 Lleida, Spain; (J.B.); (A.P.)
| | - Ana Pelechá
- Efficient Use of Water in Agriculture Program, Institute of Agrifood Research and Technology (IRTA), Fruitcentre, Parc AgroBiotech, 25003 Lleida, Spain; (J.B.); (A.P.)
| | - Marta S. Lopes
- Field Crops Program, Institute for Food and Agricultural Research and Technology (IRTA), 251981 Lleida, Spain;
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Cudjoe DK, Virlet N, Castle M, Riche AB, Mhada M, Waine TW, Mohareb F, Hawkesford MJ. Field phenotyping for African crops: overview and perspectives. FRONTIERS IN PLANT SCIENCE 2023; 14:1219673. [PMID: 37860243 PMCID: PMC10582954 DOI: 10.3389/fpls.2023.1219673] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 09/07/2023] [Indexed: 10/21/2023]
Abstract
Improvements in crop productivity are required to meet the dietary demands of the rapidly-increasing African population. The development of key staple crop cultivars that are high-yielding and resilient to biotic and abiotic stresses is essential. To contribute to this objective, high-throughput plant phenotyping approaches are important enablers for the African plant science community to measure complex quantitative phenotypes and to establish the genetic basis of agriculturally relevant traits. These advances will facilitate the screening of germplasm for optimum performance and adaptation to low-input agriculture and resource-constrained environments. Increasing the capacity to investigate plant function and structure through non-invasive technologies is an effective strategy to aid plant breeding and additionally may contribute to precision agriculture. However, despite the significant global advances in basic knowledge and sensor technology for plant phenotyping, Africa still lags behind in the development and implementation of these systems due to several practical, financial, geographical and political barriers. Currently, field phenotyping is mostly carried out by manual methods that are prone to error, costly, labor-intensive and may come with adverse economic implications. Therefore, improvements in advanced field phenotyping capabilities and appropriate implementation are key factors for success in modern breeding and agricultural monitoring. In this review, we provide an overview of the current state of field phenotyping and the challenges limiting its implementation in some African countries. We suggest that the lack of appropriate field phenotyping infrastructures is impeding the development of improved crop cultivars and will have a detrimental impact on the agricultural sector and on food security. We highlight the prospects for integrating emerging and advanced low-cost phenotyping technologies into breeding protocols and characterizing crop responses to environmental challenges in field experimentation. Finally, we explore strategies for overcoming the barriers and maximizing the full potential of emerging field phenotyping technologies in African agriculture. This review paper will open new windows and provide new perspectives for breeders and the entire plant science community in Africa.
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Affiliation(s)
- Daniel K. Cudjoe
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, United Kingdom
| | - Nicolas Virlet
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - March Castle
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Andrew B. Riche
- Sustainable Soils and Crops, Rothamsted Research, Harpenden, United Kingdom
| | - Manal Mhada
- AgroBiosciences Department, Mohammed VI Polytechnic University (UM6P), Benguérir, Morocco
| | - Toby W. Waine
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, United Kingdom
| | - Fady Mohareb
- School of Water, Energy and Environment, Cranfield University, Cranfield, Bedfordshire, United Kingdom
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14
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Zhang P, Huang J, Ma Y, Wang X, Kang M, Song Y. Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0091. [PMID: 37780969 PMCID: PMC10538623 DOI: 10.34133/plantphenomics.0091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/26/2023] [Indexed: 10/03/2023]
Abstract
Observable morphological traits are widely employed in plant phenotyping for breeding use, which are often the external phenotypes driven by a chain of functional actions in plants. Identifying and phenotyping inherently functional traits for crop improvement toward high yields or adaptation to harsh environments remains a major challenge. Prediction of whole-plant performance in functional-structural plant models (FSPMs) is driven by plant growth algorithms based on organ scale wrapped up with micro-environments. In particular, the models are flexible for scaling down or up through specific functions at the organ nexus, allowing the prediction of crop system behaviors from the genome to the field. As such, by virtue of FSPMs, model parameters that determine organogenesis, development, biomass production, allocation, and morphogenesis from a molecular to the whole plant level can be profiled systematically and made readily available for phenotyping. FSPMs can provide rich functional traits representing biological regulatory mechanisms at various scales in a dynamic system, e.g., Rubisco carboxylation rate, mesophyll conductance, specific leaf nitrogen, radiation use efficiency, and source-sink ratio apart from morphological traits. High-throughput phenotyping such traits is also discussed, which provides an unprecedented opportunity to evolve FSPMs. This will accelerate the co-evolution of FSPMs and plant phenomics, and thus improving breeding efficiency. To expand the great promise of FSPMs in crop science, FSPMs still need more effort in multiscale, mechanistic, reproductive organ, and root system modeling. In summary, this study demonstrates that FSPMs are invaluable tools in guiding functional trait phenotyping at various scales and can thus provide abundant functional targets for phenotyping toward crop improvement.
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Affiliation(s)
- Pengpeng Zhang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Jingyao Huang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing 100094, China
| | - Xiujuan Wang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Mengzhen Kang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Youhong Song
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
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15
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Heuermann MC, Knoch D, Junker A, Altmann T. Natural plant growth and development achieved in the IPK PhenoSphere by dynamic environment simulation. Nat Commun 2023; 14:5783. [PMID: 37723146 PMCID: PMC10507097 DOI: 10.1038/s41467-023-41332-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 08/31/2023] [Indexed: 09/20/2023] Open
Abstract
In plant science, the suboptimal match of growing conditions hampers the transfer of knowledge from controlled environments in glasshouses or climate chambers to field environments. Here we present the PhenoSphere, a plant cultivation infrastructure designed to simulate field-like environments in a reproducible manner. To benchmark the PhenoSphere, the effects on plant growth of weather conditions of a single maize growing season and of an averaged season over three years are compared to those of a standard glasshouse and of four years of field trials. The single season simulation proves superior to the glasshouse and the averaged season in the PhenoSphere: The simulated weather regime of the single season triggers plant growth and development progression very similar to that observed in the field. Hence, the PhenoSphere enables detailed analyses of performance-related trait expression and causal biological mechanisms in plant populations exposed to weather conditions of current and anticipated future climate scenarios.
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Affiliation(s)
- Marc C Heuermann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, 06466, Seeland OT Gatersleben, Germany.
| | - Dominic Knoch
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, 06466, Seeland OT Gatersleben, Germany
| | - Astrid Junker
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, 06466, Seeland OT Gatersleben, Germany
- Syngenta Seeds GmbH, Zum Knipkenbach 20, 32107, Bad Salzuflen, Germany
| | - Thomas Altmann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, 06466, Seeland OT Gatersleben, Germany
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16
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Ashwath MN, Lavale SA, Santhoshkumar AV, Mohapatra SR, Bhardwaj A, Dash U, Shiran K, Samantara K, Wani SH. Genome-wide association studies: an intuitive solution for SNP identification and gene mapping in trees. Funct Integr Genomics 2023; 23:297. [PMID: 37700096 DOI: 10.1007/s10142-023-01224-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 04/26/2023] [Accepted: 08/31/2023] [Indexed: 09/14/2023]
Abstract
Analysis of natural diversity in wild/cultivated plants can be used to understand the genetic basis for plant breeding programs. Recent advancements in DNA sequencing have expanded the possibilities for genetically altering essential features. There have been several recently disclosed statistical genetic methods for discovering the genes impacting target qualities. One of these useful methods is the genome-wide association study (GWAS), which effectively identifies candidate genes for a variety of plant properties by examining the relationship between a molecular marker (such as SNP) and a target trait. Conventional QTL mapping with highly structured populations has major limitations. The limited number of recombination events results in poor resolution for quantitative traits. Only two alleles at any given locus can be studied simultaneously. Conventional mapping approach fails to work in perennial plants and vegetatively propagated crops. These limitations are sidestepped by association mapping or GWAS. The flexibility of GWAS comes from the fact that the individuals being examined need not be linked to one another, allowing for the use of all meiotic and recombination events to increase resolution. Phenotyping, genotyping, population structure analysis, kinship analysis, and marker-trait association analysis are the fundamental phases of GWAS. With the rapid development of sequencing technologies and computational methods, GWAS is becoming a potent tool for identifying the natural variations that underlie complex characteristics in crops. The use of high-throughput sequencing technologies along with genotyping approaches like genotyping-by-sequencing (GBS) and restriction site associated DNA (RAD) sequencing may be highly useful in fast-forward mapping approach like GWAS. Breeders may use GWAS to quickly unravel the genomes through QTL and association mapping by taking advantage of natural variances. The drawbacks of conventional linkage mapping can be successfully overcome with the use of high-resolution mapping and the inclusion of multiple alleles in GWAS.
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Affiliation(s)
- M N Ashwath
- Department of Forest Biology and Tree Improvement, Kerala Agricultural University, Thrissur, Kerala, 680 656, India
| | - Shivaji Ajinath Lavale
- Centre for Plant Biotechnology and Molecular Biology, Kerala Agricultural University, Thrissur, Kerala, 680 656, India
| | - A V Santhoshkumar
- Department of Forest Biology and Tree Improvement, Kerala Agricultural University, Thrissur, Kerala, 680 656, India
| | - Sourav Ranjan Mohapatra
- Department of Forest Biology and Tree Improvement, Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, 751 003, India.
| | - Ankita Bhardwaj
- Department of Silviculture and Agroforestry, Kerala Agricultural University, Thrissur, Kerala, 680 656, India
| | - Umakanta Dash
- Department of Silviculture and Agroforestry, Kerala Agricultural University, Thrissur, Kerala, 680 656, India
| | - K Shiran
- Department of Forest Biology and Tree Improvement, Kerala Agricultural University, Thrissur, Kerala, 680 656, India
| | - Kajal Samantara
- Institute of Technology, University of Tartu, 50411, Tartu, Estonia
| | - Shabir Hussain Wani
- Mountain Research Center for Field crops, Sher-e-Kashmir University of Agricultural Sciences and Technology Srinagar, Khudwani, Srinagar, Jammu and Kashmir, India.
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17
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Gracia-Romero A, Vatter T, Kefauver SC, Rezzouk FZ, Segarra J, Nieto-Taladriz MT, Aparicio N, Araus JL. Defining durum wheat ideotypes adapted to Mediterranean environments through remote sensing traits. FRONTIERS IN PLANT SCIENCE 2023; 14:1254301. [PMID: 37731983 PMCID: PMC10508639 DOI: 10.3389/fpls.2023.1254301] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 08/03/2023] [Indexed: 09/22/2023]
Abstract
An acceleration of the genetic advances of durum wheat, as a major crop for the Mediterranean region, is required, but phenotyping still represents a bottleneck for breeding. This study aims to define durum wheat ideotypes under Mediterranean conditions by selecting the most suitable phenotypic remote sensing traits among different ones informing on characteristics related with leaf pigments/photosynthetic status, crop water status, and crop growth/green biomass. A set of 24 post-green revolution durum wheat cultivars were assessed in a wide set of 19 environments, accounted as the specific combinations of a range of latitudes in Spain, under different management conditions (water regimes and planting dates), through 3 consecutive years. Thus, red-green-blue and multispectral derived vegetation indices and canopy temperature were evaluated at anthesis and grain filling. The potential of the assessed remote sensing parameters alone and all combined as grain yield (GY) predictors was evaluated through random forest regression models performed for each environment and phenological stage. Biomass and plot greenness indicators consistently proved to be reliable GY predictors in all of the environments tested for both phenological stages. For the lowest-yielding environment, the contribution of water status measurements was higher during anthesis, whereas, for the highest-yielding environments, better predictions were reported during grain filling. Remote sensing traits measured during the grain filling and informing on pigment content and photosynthetic capacity were highlighted under the environments with warmer conditions, as the late-planting treatments. Overall, canopy greenness indicators were reported as the highest correlated traits for most of the environments and regardless of the phenological moment assessed. The addition of carbon isotope composition of mature kernels was attempted to increase the accuracies, but only a few were slightly benefited, as differences in water status among cultivars were already accounted by the measurement of canopy temperature.
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Affiliation(s)
- Adrian Gracia-Romero
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Thomas Vatter
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Shawn C. Kefauver
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Fatima Zahra Rezzouk
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | - Joel Segarra
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
| | | | - Nieves Aparicio
- Agro-technological Institute of Castilla y León (ITACyL), Valladolid, Spain
| | - José Luis Araus
- Integrative Crop Ecophysiology Group, Plant Physiology Section, Faculty of Biology, University of Barcelona, Barcelona, Spain and AGROTECNIO (Center for Research in Agrotechnology), Lleida, Spain
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18
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Gerullis M, Pieruschka R, Fahrner S, Hartl L, Schurr U, Heckelei T. From genes to policy: mission-oriented governance of plant-breeding research and technologies. FRONTIERS IN PLANT SCIENCE 2023; 14:1235175. [PMID: 37731976 PMCID: PMC10507248 DOI: 10.3389/fpls.2023.1235175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 08/07/2023] [Indexed: 09/22/2023]
Abstract
Mission-oriented governance of research focuses on inspirational, yet attainable goals and targets the sustainable development goals through innovation pathways. We disentangle its implications for plant breeding research and thus impacting the sustainability transformation of agricultural systems, as it requires improved crop varieties and management practices. Speedy success in plant breeding is vital to lower the use of chemical fertilizers and pesticides, increase crop resilience to climate stresses and reduce postharvest losses. A key question is how this success may come about? So far plant breeding research has ignored wider social systems feedbacks, but governance also failed to deliver a set of systemic breeding goals providing directionality and organization to research policy of the same. To address these challenges, we propose a heuristic illustrating the core elements needed for governing plant breeding research: Genetics, Environment, Management and Social system (GxExMxS) are the core elements for defining directions for future breeding. We illustrate this based on historic cases in context of current developments in plant phenotyping technologies and derive implications for governing research infrastructures and breeding programs. As part of mission-oriented governance we deem long-term investments into human resources and experimental set-ups for agricultural systems necessary to ensure a symbiotic relationship for private and public breeding actors and recommend fostering collaboration between social and natural sciences for working towards transdisciplinary collaboration.
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Affiliation(s)
- Maria Gerullis
- Dyson School of Applied Economics and Management, Cornell University, Ithaca, NY, United States
- Wheat and Oat Breeding Research, Institute for Crop Science and Plant Breeding, Bavarian State Research Center for Agriculture, Freising, Germany
| | - Roland Pieruschka
- Plant Sciences, Institute of Bio- and Geosciences 2, Jülich Research Centre, Jülich, Germany
| | - Sven Fahrner
- Plant Sciences, Institute of Bio- and Geosciences 2, Jülich Research Centre, Jülich, Germany
| | - Lorenz Hartl
- Wheat and Oat Breeding Research, Institute for Crop Science and Plant Breeding, Bavarian State Research Center for Agriculture, Freising, Germany
| | - Ulrich Schurr
- Plant Sciences, Institute of Bio- and Geosciences 2, Jülich Research Centre, Jülich, Germany
| | - Thomas Heckelei
- Institute for Food and Resource Economics, University of Bonn, Bonn, Germany
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Poorter H, Hummel GM, Nagel KA, Fiorani F, von Gillhaussen P, Virnich O, Schurr U, Postma JA, van de Zedde R, Wiese-Klinkenberg A. Pitfalls and potential of high-throughput plant phenotyping platforms. FRONTIERS IN PLANT SCIENCE 2023; 14:1233794. [PMID: 37680357 PMCID: PMC10481964 DOI: 10.3389/fpls.2023.1233794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Accepted: 08/01/2023] [Indexed: 09/09/2023]
Abstract
Automated high-throughput plant phenotyping (HTPP) enables non-invasive, fast and standardized evaluations of a large number of plants for size, development, and certain physiological variables. Many research groups recognize the potential of HTPP and have made significant investments in HTPP infrastructure, or are considering doing so. To make optimal use of limited resources, it is important to plan and use these facilities prudently and to interpret the results carefully. Here we present a number of points that users should consider before purchasing, building or utilizing such equipment. They relate to (1) the financial and time investment for acquisition, operation, and maintenance, (2) the constraints associated with such machines in terms of flexibility and growth conditions, (3) the pros and cons of frequent non-destructive measurements, (4) the level of information provided by proxy traits, and (5) the utilization of calibration curves. Using data from an Arabidopsis experiment, we demonstrate how diurnal changes in leaf angle can impact plant size estimates from top-view cameras, causing deviations of more than 20% over the day. Growth analysis data from another rosette species showed that there was a curvilinear relationship between total and projected leaf area. Neglecting this curvilinearity resulted in linear calibration curves that, although having a high r2 (> 0.92), also exhibited large relative errors. Another important consideration we discussed is the frequency at which calibration curves need to be generated and whether different treatments, seasons, or genotypes require distinct calibration curves. In conclusion, HTPP systems have become a valuable addition to the toolbox of plant biologists, provided that these systems are tailored to the research questions of interest, and users are aware of both the possible pitfalls and potential involved.
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Affiliation(s)
- Hendrik Poorter
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
- Department of Natural Sciences, Macquarie University, North Ryde, NSW, Australia
| | | | - Kerstin A. Nagel
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Fabio Fiorani
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | | | - Olivia Virnich
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | - Ulrich Schurr
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
| | | | - Rick van de Zedde
- Plant Sciences Group, Wageningen University & Research, Wageningen, Netherlands
| | - Anika Wiese-Klinkenberg
- Plant Sciences (IBG-2), Forschungszentrum Jülich GmbH, Jülich, Germany
- Bioinformatics (IBG-4), Forschungszentrum Jülich GmbH, Jülich, Germany
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20
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Wong CYS. Plant optics: underlying mechanisms in remotely sensed signals for phenotyping applications. AOB PLANTS 2023; 15:plad039. [PMID: 37560760 PMCID: PMC10407989 DOI: 10.1093/aobpla/plad039] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Accepted: 07/04/2023] [Indexed: 08/11/2023]
Abstract
Optical-based remote sensing offers great potential for phenotyping vegetation traits and functions for a range of applications including vegetation monitoring and assessment. A key strength of optical-based approaches is the underlying mechanistic link to vegetation physiology, biochemistry, and structure that influences a spectral signal. By exploiting spectral variation driven by plant physiological response to environment, remotely sensed products can be used to estimate vegetation traits and functions. However, oftentimes these products are proxies based on covariance, which can lead to misinterpretation and decoupling under certain scenarios. This viewpoint will discuss (i) the optical properties of vegetation, (ii) applications of vegetation indices, solar-induced fluorescence, and machine-learning approaches, and (iii) how covariance can lead to good empirical proximation of plant traits and functions. Understanding and acknowledging the underlying mechanistic basis of plant optics must be considered as remotely sensed data availability and applications continue to grow. Doing so will enable appropriate application and consideration of limitations for the use of optical-based remote sensing for phenotyping applications.
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21
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Montanaro G, Petrozza A, Rustioni L, Cellini F, Nuzzo V. Phenotyping Key Fruit Quality Traits in Olive Using RGB Images and Back Propagation Neural Networks. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0061. [PMID: 37363144 PMCID: PMC10289815 DOI: 10.34133/plantphenomics.0061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/12/2022] [Accepted: 06/06/2023] [Indexed: 06/28/2023]
Abstract
To predict oil and phenol concentrations in olive fruit, the combination of back propagation neural networks (BPNNs) and contact-less plant phenotyping techniques was employed to retrieve RGB image-based digital proxies of oil and phenol concentrations. Fruits of cultivars (×3) differing in ripening time were sampled (~10-day interval, ×2 years), pictured and analyzed for phenol and oil concentrations. Prior to this, fruit samples were pictured and images were segmented to extract the red (R), green (G), and blue (B) mean pixel values that were rearranged in 35 RGB-based colorimetric indexes. Three BPNNs were designed using as input variables (a) the original 35 RGB indexes, (b) the scores of principal components after a principal component analysis (PCA) pre-processing of those indexes, and (c) a reduced number (28) of the RGB indexes achieved after a sparse PCA. The results show that the predictions reached the highest mean R2 values ranging from 0.87 to 0.95 (oil) and from 0.81 to 0.90 (phenols) across the BPNNs. In addition to the R2, other performance metrics were calculated (root mean squared error and mean absolute error) and combined into a general performance indicator (GPI). The resulting rank of the GPI suggests that a BPNN with a specific topology might be designed for cultivars grouped according to their ripening period. The present study documented that an RGB-based image phenotyping can effectively predict key quality traits in olive fruit supporting the developing olive sector within a digital agriculture domain.
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Affiliation(s)
| | - Angelo Petrozza
- ALSIA, Agenzia Lucana Sviluppo Innovazione in Agricoltura, Metapontum Agrobios Research Center, 75010 Metaponto, Italy
| | - Laura Rustioni
- Department of Biological and Environmental Sciences and Technologies, University of Salento, Lecce, Italy
| | - Francesco Cellini
- ALSIA, Agenzia Lucana Sviluppo Innovazione in Agricoltura, Metapontum Agrobios Research Center, 75010 Metaponto, Italy
| | - Vitale Nuzzo
- Università degli Studi della Basilicata, 85100 Potenza, Italy
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22
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Holzinger A, Keiblinger K, Holub P, Zatloukal K, Müller H. AI for life: Trends in artificial intelligence for biotechnology. N Biotechnol 2023; 74:16-24. [PMID: 36754147 DOI: 10.1016/j.nbt.2023.02.001] [Citation(s) in RCA: 46] [Impact Index Per Article: 46.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 02/05/2023] [Accepted: 02/05/2023] [Indexed: 02/08/2023]
Abstract
Due to popular successes (e.g., ChatGPT) Artificial Intelligence (AI) is on everyone's lips today. When advances in biotechnology are combined with advances in AI unprecedented new potential solutions become available. This can help with many global problems and contribute to important Sustainability Development Goals. Current examples include Food Security, Health and Well-being, Clean Water, Clean Energy, Responsible Consumption and Production, Climate Action, Life below Water, or protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss. AI is ubiquitous in the life sciences today. Topics include a wide range from machine learning and Big Data analytics, knowledge discovery and data mining, biomedical ontologies, knowledge-based reasoning, natural language processing, decision support and reasoning under uncertainty, temporal and spatial representation and inference, and methodological aspects of explainable AI (XAI) with applications of biotechnology. In this pre-Editorial paper, we provide an overview of open research issues and challenges for each of the topics addressed in this special issue. Potential authors can directly use this as a guideline for developing their paper.
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Affiliation(s)
- Andreas Holzinger
- University of Natural Resources and Life Sciences Vienna, Austria; Medical University Graz, Austria; Alberta Machine Intelligence Institute Edmonton, Canada.
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23
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Zheng K, Lin H, Hong X, Che H, Ma X, Wei X, Mei L. Development of a multispectral fluorescence LiDAR for point cloud segmentation of plants. OPTICS EXPRESS 2023; 31:18613-18629. [PMID: 37381570 DOI: 10.1364/oe.490004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/20/2023] [Accepted: 05/08/2023] [Indexed: 06/30/2023]
Abstract
The accelerating development of high-throughput plant phenotyping demands a LiDAR system to achieve spectral point cloud, which will significantly improve the accuracy and efficiency of segmentation based on its intrinsic fusion of spectral and spatial data. Meanwhile, a relatively longer detection range is required for platforms e.g., unmanned aerial vehicles (UAV) and poles. Towards the aims above, what we believe to be, a novel multispectral fluorescence LiDAR, featuring compact volume, light weight, and low cost, has been proposed and designed. A 405 nm laser diode was employed to excite the fluorescence of plants, and the point cloud attached with both the elastic and inelastic signal intensities that was obtained through the R-, G-, B-channels of a color image sensor. A new position retrieval method has been developed to evaluate far field echo signals, from which the spectral point cloud can be obtained. Experiments were designed to validate the spectral/spatial accuracy and the segmentation performance. It has been found out that the values obtained through the R-, G-, B-channels are consistent with the emission spectrum measured by a spectrometer, achieving a maximum R2 of 0.97. The theoretical spatial resolution can reach up to 47 mm and 0.7 mm in the x- and y-direction at a distance of around 30 m, respectively. The values of recall, precision, and F score for the segmentation of the fluorescence point cloud were all beyond 0.97. Besides, a field test has been carried out on plants at a distance of about 26 m, which further demonstrated that the multispectral fluorescence data can significantly facilitate the segmentation process in a complex scene. These promising results prove that the proposed multispectral fluorescence LiDAR has great potential in applications of digital forestry inventory and intelligent agriculture.
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Serouart M, Lopez-Lozano R, Daubige G, Baumont M, Escale B, De Solan B, Baret F. Analyzing Changes in Maize Leaves Orientation due to GxExM Using an Automatic Method from RGB Images. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0046. [PMID: 37228515 PMCID: PMC10204743 DOI: 10.34133/plantphenomics.0046] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 04/08/2023] [Indexed: 05/27/2023]
Abstract
The sowing pattern has an important impact on light interception efficiency in maize by determining the spatial distribution of leaves within the canopy. Leaves orientation is an important architectural trait determining maize canopies light interception. Previous studies have indicated how maize genotypes may adapt leaves orientation to avoid mutual shading with neighboring plants as a plastic response to intraspecific competition. The goal of the present study is 2-fold: firstly, to propose and validate an automatic algorithm (Automatic Leaf Azimuth Estimation from Midrib detection [ALAEM]) based on leaves midrib detection in vertical red green blue (RGB) images to describe leaves orientation at the canopy level; and secondly, to describe genotypic and environmental differences in leaves orientation in a panel of 5 maize hybrids sowing at 2 densities (6 and 12 plants.m-2) and 2 row spacing (0.4 and 0.8 m) over 2 different sites in southern France. The ALAEM algorithm was validated against in situ annotations of leaves orientation, showing a satisfactory agreement (root mean square [RMSE] error = 0.1, R2 = 0.35) in the proportion of leaves oriented perpendicular to rows direction across sowing patterns, genotypes, and sites. The results from ALAEM permitted to identify significant differences in leaves orientation associated to leaves intraspecific competition. In both experiments, a progressive increase in the proportion of leaves oriented perpendicular to the row is observed when the rectangularity of the sowing pattern increases from 1 (6 plants.m-2, 0.4 m row spacing) towards 8 (12 plants.m-2, 0.8 m row spacing). Significant differences among the 5 cultivars were found, with 2 hybrids exhibiting, systematically, a more plastic behavior with a significantly higher proportion of leaves oriented perpendicularly to avoid overlapping with neighbor plants at high rectangularity. Differences in leaves orientation were also found between experiments in a squared sowing pattern (6 plants.m-2, 0.4 m row spacing), indicating a possible contribution of illumination conditions inducing a preferential orientation toward east-west direction when intraspecific competition is low.
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Affiliation(s)
- Mario Serouart
- Arvalis, Institut du végétal, 228, route de l’aérodrome - CS 40509, 84914 Avignon Cedex 9, France
- INRAE, Avignon Université, UMR EMMAH, UMT CAPTE, 228, route de l’aérodrome - CS 40509, 84914 Avignon Cedex 9, France
| | - Raul Lopez-Lozano
- INRAE, Avignon Université, UMR EMMAH, UMT CAPTE, 228, route de l’aérodrome - CS 40509, 84914 Avignon Cedex 9, France
| | - Gaëtan Daubige
- Arvalis, Institut du végétal, 228, route de l’aérodrome - CS 40509, 84914 Avignon Cedex 9, France
| | - Maëva Baumont
- Arvalis, Ecophysiology, 21 Chemin de Pau, 64121 Montardon, France
| | - Brigitte Escale
- Arvalis, Ecophysiology, 21 Chemin de Pau, 64121 Montardon, France
| | - Benoit De Solan
- Arvalis, Institut du végétal, 228, route de l’aérodrome - CS 40509, 84914 Avignon Cedex 9, France
| | - Frédéric Baret
- INRAE, Avignon Université, UMR EMMAH, UMT CAPTE, 228, route de l’aérodrome - CS 40509, 84914 Avignon Cedex 9, France
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25
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Zieschank V, Junker RR. Digital whole-community phenotyping: tracking morphological and physiological responses of plant communities to environmental changes in the field. FRONTIERS IN PLANT SCIENCE 2023; 14:1141554. [PMID: 37229120 PMCID: PMC10203609 DOI: 10.3389/fpls.2023.1141554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 04/13/2023] [Indexed: 05/27/2023]
Abstract
Plant traits are informative for ecosystem functions and processes and help to derive general rules and predictions about responses to environmental gradients, global change and perturbations. Ecological field studies often use 'low-throughput' methods to assess plant phenotypes and integrate species-specific traits to community-wide indices. In contrast, agricultural greenhouse or lab-based studies often employ 'high-throughput phenotyping' to assess plant individuals tracking their growth or fertilizer and water demand. In ecological field studies, remote sensing makes use of freely movable devices like satellites or unmanned aerial vehicles (UAVs) which provide large-scale spatial and temporal data. Adopting such methods for community ecology on a smaller scale may provide novel insights on the phenotypic properties of plant communities and fill the gap between traditional field measurements and airborne remote sensing. However, the trade-off between spatial resolution, temporal resolution and scope of the respective study requires highly specific setups so that the measurements fit the scientific question. We introduce small-scale, high-resolution digital automated phenotyping as a novel source of quantitative trait data in ecological field studies that provides complementary multi-faceted data of plant communities. We customized an automated plant phenotyping system for its mobile application in the field for 'digital whole-community phenotyping' (DWCP), capturing the 3-dimensional structure and multispectral information of plant communities. We demonstrated the potential of DWCP by recording plant community responses to experimental land-use treatments over two years. DWCP captured changes in morphological and physiological community properties in response to mowing and fertilizer treatments and thus reliably informed about changes in land-use. In contrast, manually measured community-weighted mean traits and species composition remained largely unaffected and were not informative about these treatments. DWCP proved to be an efficient method for characterizing plant communities, complements other methods in trait-based ecology, provides indicators of ecosystem states, and may help to forecast tipping points in plant communities often associated with irreversible changes in ecosystems.
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Affiliation(s)
- Vincent Zieschank
- Evolutionary Ecology of Plants, Department of Biology, Philipps-University Marburg, Marburg, Germany
| | - Robert R. Junker
- Evolutionary Ecology of Plants, Department of Biology, Philipps-University Marburg, Marburg, Germany
- Department of Environment and Biodiversity, University of Salzburg, Salzburg, Austria
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26
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Driever SM, Mossink L, Ocaña DN, Kaiser E. A simple system for phenotyping of plant transpiration and stomatal conductance response to drought. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2023; 329:111626. [PMID: 36738936 DOI: 10.1016/j.plantsci.2023.111626] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2022] [Revised: 01/25/2023] [Accepted: 01/31/2023] [Indexed: 06/18/2023]
Abstract
Plant breeding for increased crop water use efficiency or drought stress resistance requires methods to quickly assess the transpiration rate (E) and stomatal conductance (gs) of a large number of individual plants. Several methods to measure E and gs exist, each of which has its own advantages and shortcomings. To add to this toolbox, we developed a method that uses whole-plant thermal imaging in a controlled environment, where aerial humidity is changed rapidly to induce changes in E that are reflected in changes in leaf temperature. This approach is based on a simplified energy balance equation, without the need for a reference material or complicated calculations. To test this concept, we built a double-sided, perforated, open-top plexiglass chamber that was supplied with air at a high flow rate (35 L min-1) and whose relative humidity (RH) could be switched rapidly. Measurements included air and leaf temperature as well as RH. Using several well-watered and drought stressed genotypes of Arabidopsis thaliana that were exposed to multiple cycles in RH (30-50 % and back), we showed that leaf temperature as measured in our system correlated well with E and gs measured in a commercial gas exchange system. Our results demonstrate that, at least within a given species, the differences in leaf temperature under several RH can be used as a proxy for E and gs. Given that this method is fairly quick, noninvasive and remote, we envision that it could be upscaled for work within rapid plant phenotyping systems.
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Affiliation(s)
- Steven M Driever
- Centre for Crop Systems Analysis, Wageningen University and Research, Wageningen, the Netherlands
| | - Leon Mossink
- Centre for Crop Systems Analysis, Wageningen University and Research, Wageningen, the Netherlands
| | - Diego Nuñez Ocaña
- Horticulture and Product Physiology, Wageningen University and Research, Wageningen, the Netherlands
| | - Elias Kaiser
- Horticulture and Product Physiology, Wageningen University and Research, Wageningen, the Netherlands.
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27
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Wong CYS, Jones T, McHugh DP, Gilbert ME, Gepts P, Palkovic A, Buckley TN, Magney TS. TSWIFT: Tower Spectrometer on Wheels for Investigating Frequent Timeseries for high-throughput phenotyping of vegetation physiology. PLANT METHODS 2023; 19:29. [PMID: 36978119 PMCID: PMC10044391 DOI: 10.1186/s13007-023-01001-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Remote sensing instruments enable high-throughput phenotyping of plant traits and stress resilience across scale. Spatial (handheld devices, towers, drones, airborne, and satellites) and temporal (continuous or intermittent) tradeoffs can enable or constrain plant science applications. Here, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF). RESULTS We demonstrate potential applications for monitoring short-term (diurnal) and long-term (seasonal) variation of vegetation for high-throughput phenotyping applications. We deployed TSWIFT in a field experiment of 300 common bean genotypes in two treatments: control (irrigated) and drought (terminal drought). We evaluated the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and SIF, as well as the coefficient of variation (CV) across the visible-near infrared spectral range (400 to 900 nm). NDVI tracked structural variation early in the growing season, following initial plant growth and development. PRI and SIF were more dynamic, exhibiting variation diurnally and seasonally, enabling quantification of genotypic variation in physiological response to drought conditions. Beyond vegetation indices, CV of hyperspectral reflectance showed the most variability across genotypes, treatment, and time in the visible and red-edge spectral regions. CONCLUSIONS TSWIFT enables continuous and automated monitoring of hyperspectral reflectance for assessing variation in plant structure and function at high spatial and temporal resolutions for high-throughput phenotyping. Mobile, tower-based systems like this can provide short- and long-term datasets to assess genotypic and/or management responses to the environment, and ultimately enable the spectral prediction of resource-use efficiency, stress resilience, productivity and yield.
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Affiliation(s)
| | - Taylor Jones
- Department of Earth & Environment, Boston University, Boston, MA 02215 USA
| | - Devin P. McHugh
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Matthew E. Gilbert
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Paul Gepts
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Antonia Palkovic
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Thomas N. Buckley
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Troy S. Magney
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
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28
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Hacisalihoglu G, Armstrong P. Crop Seed Phenomics: Focus on Non-Destructive Functional Trait Phenotyping Methods and Applications. PLANTS (BASEL, SWITZERLAND) 2023; 12:1177. [PMID: 36904037 PMCID: PMC10005477 DOI: 10.3390/plants12051177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Revised: 02/27/2023] [Accepted: 03/02/2023] [Indexed: 06/18/2023]
Abstract
Seeds play a critical role in ensuring food security for the earth's 8 billion people. There is great biodiversity in plant seed content traits worldwide. Consequently, the development of robust, rapid, and high-throughput methods is required for seed quality evaluation and acceleration of crop improvement. There has been considerable progress in the past 20 years in various non-destructive methods to uncover and understand plant seed phenomics. This review highlights recent advances in non-destructive seed phenomics techniques, including Fourier Transform near infrared (FT-NIR), Dispersive-Diode Array (DA-NIR), Single-Kernel (SKNIR), Micro-Electromechanical Systems (MEMS-NIR) spectroscopy, Hyperspectral Imaging (HSI), and Micro-Computed Tomography Imaging (micro-CT). The potential applications of NIR spectroscopy are expected to continue to rise as more seed researchers, breeders, and growers successfully adopt it as a powerful non-destructive method for seed quality phenomics. It will also discuss the advantages and limitations that need to be solved for each technique and how each method could help breeders and industry with trait identification, measurement, classification, and screening or sorting of seed nutritive traits. Finally, this review will focus on the future outlook for promoting and accelerating crop improvement and sustainability.
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Affiliation(s)
- Gokhan Hacisalihoglu
- Biological Sciences Department, Florida A&M University, Tallahassee, FL 32307, USA
| | - Paul Armstrong
- USDA-ARS Center for Grain and Animal Health Research, Manhattan, KS 66502, USA
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29
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Harfouche AL, Nakhle F, Harfouche AH, Sardella OG, Dart E, Jacobson D. A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey. TRENDS IN PLANT SCIENCE 2023; 28:154-184. [PMID: 36167648 DOI: 10.1016/j.tplants.2022.08.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 08/22/2022] [Accepted: 08/25/2022] [Indexed: 06/16/2023]
Abstract
Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large datasets. We provide a perspective and primer on AI applications to phenome research. We propose a novel human-centric explainable AI (X-AI) system architecture consisting of data architecture, technology infrastructure, and AI architecture design. We clarify the difference between post hoc models and 'interpretable by design' models. We include guidance for effectively using an interpretable by design model in phenomic analysis. We also provide directions to sources of tools and resources for making data analytics increasingly accessible. This primer is accompanied by an interactive online tutorial.
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Affiliation(s)
- Antoine L Harfouche
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy.
| | - Farid Nakhle
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy
| | - Antoine H Harfouche
- Unité de Formation et de Recherche en Sciences Économiques, Gestion, Mathématiques, et Informatique, Université Paris Nanterre, 92001 Nanterre, France
| | - Orlando G Sardella
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy
| | - Eli Dart
- Energy Sciences Network (ESnet), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Daniel Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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30
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Cai S, Gou W, Wen W, Lu X, Fan J, Guo X. Design and Development of a Low-Cost UGV 3D Phenotyping Platform with Integrated LiDAR and Electric Slide Rail. PLANTS (BASEL, SWITZERLAND) 2023; 12:plants12030483. [PMID: 36771568 PMCID: PMC9919849 DOI: 10.3390/plants12030483] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Revised: 01/12/2023] [Accepted: 01/18/2023] [Indexed: 05/27/2023]
Abstract
Unmanned ground vehicles (UGV) have attracted much attention in crop phenotype monitoring due to their lightweight and flexibility. This paper describes a new UGV equipped with an electric slide rail and point cloud high-throughput acquisition and phenotype extraction system. The designed UGV is equipped with an autopilot system, a small electric slide rail, and Light Detection and Ranging (LiDAR) to achieve high-throughput, high-precision automatic crop point cloud acquisition and map building. The phenotype analysis system realized single plant segmentation and pipeline extraction of plant height and maximum crown width of the crop point cloud using the Random sampling consistency (RANSAC), Euclidean clustering, and k-means clustering algorithm. This phenotyping system was used to collect point cloud data and extract plant height and maximum crown width for 54 greenhouse-potted lettuce plants. The results showed that the correlation coefficient (R2) between the collected data and manual measurements were 0.97996 and 0.90975, respectively, while the root mean square error (RMSE) was 1.51 cm and 4.99 cm, respectively. At less than a tenth of the cost of the PlantEye F500, UGV achieves phenotypic data acquisition with less error and detects morphological trait differences between lettuce types. Thus, it could be suitable for actual 3D phenotypic measurements of greenhouse crops.
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Affiliation(s)
- Shuangze Cai
- School of Agricultural Equipment Engineering, Jiangsu University, Zhenjiang 212013, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Wenbo Gou
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Weiliang Wen
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
| | - Xianju Lu
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Jiangchuan Fan
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing PAIDE Science and Technology Development Co., Ltd., Beijing 100097, China
| | - Xinyu Guo
- School of Agricultural Equipment Engineering, Jiangsu University, Zhenjiang 212013, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
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Govindasamy P, Muthusamy SK, Bagavathiannan M, Mowrer J, Jagannadham PTK, Maity A, Halli HM, G. K. S, Vadivel R, T. K. D, Raj R, Pooniya V, Babu S, Rathore SS, L. M, Tiwari G. Nitrogen use efficiency-a key to enhance crop productivity under a changing climate. FRONTIERS IN PLANT SCIENCE 2023; 14:1121073. [PMID: 37143873 PMCID: PMC10151540 DOI: 10.3389/fpls.2023.1121073] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 03/20/2023] [Indexed: 05/06/2023]
Abstract
Nitrogen (N) is an essential element required for the growth and development of all plants. On a global scale, N is agriculture's most widely used fertilizer nutrient. Studies have shown that crops use only 50% of the applied N effectively, while the rest is lost through various pathways to the surrounding environment. Furthermore, lost N negatively impacts the farmer's return on investment and pollutes the water, soil, and air. Therefore, enhancing nitrogen use efficiency (NUE) is critical in crop improvement programs and agronomic management systems. The major processes responsible for low N use are the volatilization, surface runoff, leaching, and denitrification of N. Improving NUE through agronomic management practices and high-throughput technologies would reduce the need for intensive N application and minimize the negative impact of N on the environment. The harmonization of agronomic, genetic, and biotechnological tools will improve the efficiency of N assimilation in crops and align agricultural systems with global needs to protect environmental functions and resources. Therefore, this review summarizes the literature on nitrogen loss, factors affecting NUE, and agronomic and genetic approaches for improving NUE in various crops and proposes a pathway to bring together agronomic and environmental needs.
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Affiliation(s)
- Prabhu Govindasamy
- Division of Agronomy, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
- *Correspondence: Muthukumar Bagavathiannan, ; Prabhu Govindasamy,
| | - Senthilkumar K. Muthusamy
- Division of Crop Improvement, Indian Council of Agricultural Research (ICAR)-Central Tuber Crops Research Institute, Thiruvananthapuram, India
| | - Muthukumar Bagavathiannan
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
- *Correspondence: Muthukumar Bagavathiannan, ; Prabhu Govindasamy,
| | - Jake Mowrer
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX, United States
| | | | - Aniruddha Maity
- Crop, Soil and Environmental Sciences, Auburn University, Auburn, AL, United States
| | - Hanamant M. Halli
- School of Soil Stress Management, Indian Council of Agricultural Research (ICAR)-National Institute of Abiotic Stress Management, Pune, India
| | - Sujayananad G. K.
- Crop Protection, Indian Council of Agricultural Research (ICAR)-Indian Institute of Pulse Research, Kanpur, India
| | - Rajagopal Vadivel
- School of Soil Stress Management, Indian Council of Agricultural Research (ICAR)-National Institute of Abiotic Stress Management, Pune, India
| | - Das T. K.
- Division of Agronomy, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Rishi Raj
- Division of Agronomy, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Vijay Pooniya
- Division of Agronomy, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Subhash Babu
- Division of Agronomy, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Sanjay Singh Rathore
- Division of Agronomy, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Muralikrishnan L.
- Division of Agricultural Extension, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Gopal Tiwari
- Division of Agronomy, Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
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Wu J, Wen S, Lan Y, Yin X, Zhang J, Ge Y. Estimation of cotton canopy parameters based on unmanned aerial vehicle (UAV) oblique photography. PLANT METHODS 2022; 18:129. [PMID: 36482426 PMCID: PMC9733379 DOI: 10.1186/s13007-022-00966-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 11/27/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND The technology of cotton defoliation is essential for mechanical cotton harvesting. Agricultural unmanned aerial vehicle (UAV) spraying has the advantages of low cost, high efficiency and no mechanical damage to cotton and has been favored and widely used by cotton planters in China. However, there are also some problems of low cotton defoliation rates and high impurity rates caused by unclear spraying amounts of cotton defoliants. The chemical rate recommendation and application should be based upon crop canopy volume rather than on land area. Plant height and leaf area index (LAI) is directly connected to plant canopy structure. Accurate dynamic monitoring of plant height and LAI provides important information for evaluating cotton growth and production. The traditional method to obtain plant height and LAI was s a time-consuming and labor-intensive task. It is very difficult and unrealistic to use the traditional measurement method to make the temporal and spatial variation map of plant height and LAI of large cotton fields. With the application of UAV in agriculture, remote sensing by UAV is currently regarded as an effective technology for monitoring and estimating plant height and LAI. RESULTS In this paper, we used UAV RGB photos to build dense point clouds to estimate cotton plant height and LAI following cotton defoliant spraying. The results indicate that the proposed method was able to dynamically monitor the changes in the LAI of cotton at different times. At 3 days after defoliant spraying, the correlation between the plant height estimated based on the constructed dense point cloud and the measured plant height was strong, with [Formula: see text] and RMSE values of 0.962 and 0.913, respectively. At 10 days after defoliant spraying, the correlation became weaker over time, with [Formula: see text] and RMSE values of 0.018 and 0.027, respectively. Comparing the actual manually measured LAI with the estimated LAI based on the dense point cloud, the [Formula: see text] and RMSE were 0.872 and 0.814 and 0.132 and 0.173 at 3 and 10 days after defoliant spraying, respectively. CONCLUSIONS Dense point cloud construction based on UAV remote sensing is a potential alternative to plant height and LAI estimation. The accuracy of LAI estimation can be improved by considering both plant height and planting density.
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Affiliation(s)
- Jinyong Wu
- Engineering College, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agriculture Aviation Pesticides Praying Technology, South China Agricultural University, Guangzhou, China
| | - Sheng Wen
- Engineering College, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agriculture Aviation Pesticides Praying Technology, South China Agricultural University, Guangzhou, China
| | - Yubin Lan
- National Center for International Collaboration Research on Precision Agriculture Aviation Pesticides Praying Technology, South China Agricultural University, Guangzhou, China
- College of Electronic Engineering, South China Agricultural University, Guangzhou, China
| | - Xuanchun Yin
- Engineering College, South China Agricultural University, Guangzhou, China
- National Center for International Collaboration Research on Precision Agriculture Aviation Pesticides Praying Technology, South China Agricultural University, Guangzhou, China
| | - Jiantao Zhang
- National Center for International Collaboration Research on Precision Agriculture Aviation Pesticides Praying Technology, South China Agricultural University, Guangzhou, China
- College of Mathematics and Informatics, South China Agricultural University, Guangzhou, China
| | - Yufeng Ge
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, USA
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Yu L, Liu L, Yang W, Wu D, Wang J, He Q, Chen Z, Liu Q. A non-destructive coconut fruit and seed traits extraction method based on Micro-CT and deeplabV3+ model. FRONTIERS IN PLANT SCIENCE 2022; 13:1069849. [PMID: 36561444 PMCID: PMC9763456 DOI: 10.3389/fpls.2022.1069849] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/14/2022] [Accepted: 11/21/2022] [Indexed: 06/17/2023]
Abstract
With the completion of the coconut gene map and the gradual improvement of related molecular biology tools, molecular marker-assisted breeding of coconut has become the next focus of coconut breeding, and accurate coconut phenotypic traits measurement will provide technical support for screening and identifying the correspondence between genotype and phenotype. A Micro-CT system was developed to measure coconut fruits and seeds automatically and nondestructively to acquire the 3D model and phenotyping traits. A deeplabv3+ model with an Xception backbone was used to segment the sectional image of coconut fruits and seeds automatically. Compared with the structural-light system measurement, the mean absolute percentage error of the fruit volume and surface area measurements by the Micro-CT system was 1.87% and 2.24%, respectively, and the squares of the correlation coefficients were 0.977 and 0.964, respectively. In addition, compared with the manual measurements, the mean absolute percentage error of the automatic copra weight and total biomass measurements was 8.85% and 25.19%, respectively, and the adjusted squares of the correlation coefficients were 0.922 and 0.721, respectively. The Micro-CT system can nondestructively obtain up to 21 agronomic traits and 57 digital traits precisely.
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Affiliation(s)
- Lejun Yu
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - Lingbo Liu
- Wuhan National Laboratory for Optoelectronics, Britton Chance Center for Biomedical Photonics, Key Laboratory of Ministry of Education for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Huazhong Agricultural University, Wuhan, China
| | - Dan Wu
- Wuhan National Laboratory for Optoelectronics, Britton Chance Center for Biomedical Photonics, Key Laboratory of Ministry of Education for Biomedical Photonics, Department of Biomedical Engineering, Huazhong University of Science and Technology, Wuhan, China
| | - Jinhu Wang
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - Qiang He
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - ZhouShuai Chen
- School of Biomedical Engineering, Hainan University, Haikou, China
| | - Qian Liu
- School of Biomedical Engineering, Hainan University, Haikou, China
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Tao H, Xu S, Tian Y, Li Z, Ge Y, Zhang J, Wang Y, Zhou G, Deng X, Zhang Z, Ding Y, Jiang D, Guo Q, Jin S. Proximal and remote sensing in plant phenomics: 20 years of progress, challenges, and perspectives. PLANT COMMUNICATIONS 2022; 3:100344. [PMID: 35655429 PMCID: PMC9700174 DOI: 10.1016/j.xplc.2022.100344] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/19/2022] [Revised: 05/08/2022] [Accepted: 05/27/2022] [Indexed: 06/01/2023]
Abstract
Plant phenomics (PP) has been recognized as a bottleneck in studying the interactions of genomics and environment on plants, limiting the progress of smart breeding and precise cultivation. High-throughput plant phenotyping is challenging owing to the spatio-temporal dynamics of traits. Proximal and remote sensing (PRS) techniques are increasingly used for plant phenotyping because of their advantages in multi-dimensional data acquisition and analysis. Substantial progress of PRS applications in PP has been observed over the last two decades and is analyzed here from an interdisciplinary perspective based on 2972 publications. This progress covers most aspects of PRS application in PP, including patterns of global spatial distribution and temporal dynamics, specific PRS technologies, phenotypic research fields, working environments, species, and traits. Subsequently, we demonstrate how to link PRS to multi-omics studies, including how to achieve multi-dimensional PRS data acquisition and processing, how to systematically integrate all kinds of phenotypic information and derive phenotypic knowledge with biological significance, and how to link PP to multi-omics association analysis. Finally, we identify three future perspectives for PRS-based PP: (1) strengthening the spatial and temporal consistency of PRS data, (2) exploring novel phenotypic traits, and (3) facilitating multi-omics communication.
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Affiliation(s)
- Haiyu Tao
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Shan Xu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Yongchao Tian
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Zhaofeng Li
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yan Ge
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Jiaoping Zhang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, National Center for Soybean Improvement, Key Laboratory for Biology and Genetic Improvement of Soybean (General, Ministry of Agriculture), Nanjing Agricultural University, Nanjing 210095, China
| | - Yu Wang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China
| | - Guodong Zhou
- Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Xiong Deng
- Key Laboratory of Plant Molecular Physiology, Institute of Botany, Chinese Academy of Sciences, Beijing 100093, China
| | - Ze Zhang
- The Key Laboratory of Oasis Eco-agriculture, Xinjiang Production and Construction Corps, Agriculture College, Shihezi University, Shihezi 832003, China
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Dong Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China
| | - Qinghua Guo
- Institute of Ecology, College of Urban and Environmental Science, Peking University, Beijing 100871, China
| | - Shichao Jin
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, National Engineering and Technology Center for Information Agriculture, Collaborative Innovation Centre for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Address: No. 1 Weigang, Xuanwu District, Nanjing 210095, China; Hainan Yazhou Bay Seed Laboratory, Sanya 572025, China; Sanya Research Institute of Nanjing Agriculture University, Sanya 572024, China; Jiangsu Provincial Key Laboratory of Geographic Information Science and Technology, International Institute for Earth System Sciences, Nanjing University, Nanjing, Jiangsu 210023, China.
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Vishal MK, Saluja R, Aggrawal D, Banerjee B, Raju D, Kumar S, Chinnusamy V, Sahoo RN, Adinarayana J. Leaf Count Aided Novel Framework for Rice ( Oryza sativa L.) Genotypes Discrimination in Phenomics: Leveraging Computer Vision and Deep Learning Applications. PLANTS (BASEL, SWITZERLAND) 2022; 11:2663. [PMID: 36235529 PMCID: PMC9614605 DOI: 10.3390/plants11192663] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/30/2022] [Revised: 08/02/2022] [Accepted: 08/26/2022] [Indexed: 06/16/2023]
Abstract
Drought is a detrimental factor to gaining higher yields in rice (Oryza sativa L.), especially amid the rising occurrence of drought across the globe. To combat this situation, it is essential to develop novel drought-resilient varieties. Therefore, screening of drought-adaptive genotypes is required with high precision and high throughput. In contemporary emerging science, high throughput plant phenotyping (HTPP) is a crucial technology that attempts to break the bottleneck of traditional phenotyping. In traditional phenotyping, screening significant genotypes is a tedious task and prone to human error while measuring various plant traits. In contrast, owing to the potential advantage of HTPP over traditional phenotyping, image-based traits, also known as i-traits, were used in our study to discriminate 110 genotypes grown for genome-wide association study experiments under controlled (well-watered), and drought-stress (limited water) conditions, under a phenomics experiment in a controlled environment with RGB images. Our proposed framework non-destructively estimated drought-adaptive plant traits from the images, such as the number of leaves, convex hull, plant-aspect ratio (plant spread), and similarly associated geometrical and morphological traits for analyzing and discriminating genotypes. The results showed that a single trait, the number of leaves, can also be used for discriminating genotypes. This critical drought-adaptive trait was associated with plant size, architecture, and biomass. In this work, the number of leaves and other characteristics were estimated non-destructively from top view images of the rice plant for each genotype. The estimation of the number of leaves for each rice plant was conducted with the deep learning model, YOLO (You Only Look Once). The leaves were counted by detecting corresponding visible leaf tips in the rice plant. The detection accuracy was 86-92% for dense to moderate spread large plants, and 98% for sparse spread small plants. With this framework, the susceptible genotypes (MTU1010, PUSA-1121 and similar genotypes) and drought-resistant genotypes (Heera, Anjali, Dular and similar genotypes) were grouped in the core set with a respective group of drought-susceptible and drought-tolerant genotypes based on the number of leaves, and the leaves' emergence during the peak drought-stress period. Moreover, it was found that the number of leaves was significantly associated with other pertinent morphological, physiological and geometrical traits. Other geometrical traits were measured from the RGB images with the help of computer vision.
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Affiliation(s)
| | - Rohit Saluja
- CSE, Indian Institute of Technology Bombay, Mumbai 400076, India
- Indian Institute of Information Technology, Hyderabad 500032, India
| | | | - Biplab Banerjee
- CSRE, Indian Institute of Technology Bombay, Mumbai 400076, India
| | - Dhandapani Raju
- Indian Council of Agricultural Research—Indian Agricultural Research Institute, Pusa, New Delhi 110012, India
| | - Sudhir Kumar
- Indian Council of Agricultural Research—Indian Agricultural Research Institute, Pusa, New Delhi 110012, India
| | - Viswanathan Chinnusamy
- Indian Council of Agricultural Research—Indian Agricultural Research Institute, Pusa, New Delhi 110012, India
| | - Rabi Narayan Sahoo
- Indian Council of Agricultural Research—Indian Agricultural Research Institute, Pusa, New Delhi 110012, India
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Correia PMP, Cairo Westergaard J, Bernardes da Silva A, Roitsch T, Carmo-Silva E, Marques da Silva J. High-throughput phenotyping of physiological traits for wheat resilience to high temperature and drought stress. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5235-5251. [PMID: 35446418 PMCID: PMC9440435 DOI: 10.1093/jxb/erac160] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 04/20/2022] [Indexed: 05/30/2023]
Abstract
Interannual and local fluctuations in wheat crop yield are mostly explained by abiotic constraints. Heatwaves and drought, which are among the top stressors, commonly co-occur, and their frequency is increasing with global climate change. High-throughput methods were optimized to phenotype wheat plants under controlled water deficit and high temperature, with the aim to identify phenotypic traits conferring adaptative stress responses. Wheat plants of 10 genotypes were grown in a fully automated plant facility under 25/18 °C day/night for 30 d, and then the temperature was increased for 7 d (38/31 °C day/night) while maintaining half of the plants well irrigated and half at 30% field capacity. Thermal and multispectral images and pot weights were registered twice daily. At the end of the experiment, key metabolites and enzyme activities from carbohydrate and antioxidant metabolism were quantified. Regression machine learning models were successfully established to predict plant biomass using image-extracted parameters. Evapotranspiration traits expressed significant genotype-environment interactions (G×E) when acclimatization to stress was continuously monitored. Consequently, transpiration efficiency was essential to maintain the balance between water-saving strategies and biomass production in wheat under water deficit and high temperature. Stress tolerance included changes in carbohydrate metabolism, particularly in the sucrolytic and glycolytic pathways, and in antioxidant metabolism. The observed genetic differences in sensitivity to high temperature and water deficit can be exploited in breeding programmes to improve wheat resilience to climate change.
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Affiliation(s)
| | - Jesper Cairo Westergaard
- Department of Plant and Environmental Sciences, Section of Crop Science, Copenhagen University, Højbakkegård Allé 13, 2630 Tåstrup, Denmark
| | - Anabela Bernardes da Silva
- BioISI – Biosystems & Integrative Sciences Institute, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
| | - Thomas Roitsch
- Department of Plant and Environmental Sciences, Section of Crop Science, Copenhagen University, Højbakkegård Allé 13, 2630 Tåstrup, Denmark
- Department of Adaptive Biotechnologies, Global Change Research Institute, CAS, 603 00 Brno, Czech Republic
| | | | - Jorge Marques da Silva
- BioISI – Biosystems & Integrative Sciences Institute, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
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Li X, Xu X, Chen M, Xu M, Wang W, Liu C, Yu L, Liu W, Yang W. The field phenotyping platform's next darling: Dicotyledons. FRONTIERS IN PLANT SCIENCE 2022; 13:935748. [PMID: 36092402 PMCID: PMC9449727 DOI: 10.3389/fpls.2022.935748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
The genetic information and functional properties of plants have been further identified with the completion of the whole-genome sequencing of numerous crop species and the rapid development of high-throughput phenotyping technologies, laying a suitable foundation for advanced precision agriculture and enhanced genetic gains. Collecting phenotypic data from dicotyledonous crops in the field has been identified as a key factor in the collection of large-scale phenotypic data of crops. On the one hand, dicotyledonous plants account for 4/5 of all angiosperm species and play a critical role in agriculture. However, their morphology is complex, and an abundance of dicot phenotypic information is available, which is critical for the analysis of high-throughput phenotypic data in the field. As a result, the focus of this paper is on the major advancements in ground-based, air-based, and space-based field phenotyping platforms over the last few decades and the research progress in the high-throughput phenotyping of dicotyledonous field crop plants in terms of morphological indicators, physiological and biochemical indicators, biotic/abiotic stress indicators, and yield indicators. Finally, the future development of dicots in the field is explored from the perspectives of identifying new unified phenotypic criteria, developing a high-performance infrastructure platform, creating a phenotypic big data knowledge map, and merging the data with those of multiomic techniques.
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Affiliation(s)
- Xiuni Li
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Xiangyao Xu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Menggen Chen
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Mei Xu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Wenyan Wang
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Chunyan Liu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Liang Yu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Weiguo Liu
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
| | - Wenyu Yang
- College of Agronomy, Sichuan Agricultural University, Chengdu, China
- Sichuan Engineering Research Center for Crop Strip Intercropping System, Chengdu, China
- Key Laboratory of Crop Ecophysiology and Farming System in Southwest, Ministry of Agriculture, Chengdu, China
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Nanosensor Applications in Plant Science. BIOSENSORS 2022; 12:bios12090675. [PMID: 36140060 PMCID: PMC9496508 DOI: 10.3390/bios12090675] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/12/2022] [Accepted: 08/18/2022] [Indexed: 12/28/2022]
Abstract
Plant science is a major research topic addressing some of the most important global challenges we face today, including energy and food security. Plant science has a role in the production of staple foods and materials, as well as roles in genetics research, environmental management, and the synthesis of high-value compounds such as pharmaceuticals or raw materials for energy production. Nanosensors—selective transducers with a characteristic dimension that is nanometre in scale—have emerged as important tools for monitoring biological processes such as plant signalling pathways and metabolism in ways that are non-destructive, minimally invasive, and capable of real-time analysis. A variety of nanosensors have been used to study different biological processes; for example, optical nanosensors based on Förster resonance energy transfer (FRET) have been used to study protein interactions, cell contents, and biophysical parameters, and electrochemical nanosensors have been used to detect redox reactions in plants. Nanosensor applications in plants include nutrient determination, disease assessment, and the detection of proteins, hormones, and other biological substances. The combination of nanosensor technology and plant sciences has the potential to be a powerful alliance and could support the successful delivery of the 2030 Sustainable Development Goals. However, a lack of knowledge regarding the health effects of nanomaterials and the high costs of some of the raw materials required has lessened their commercial impact.
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Tayade R, Yoon J, Lay L, Khan AL, Yoon Y, Kim Y. Utilization of Spectral Indices for High-Throughput Phenotyping. PLANTS (BASEL, SWITZERLAND) 2022; 11:1712. [PMID: 35807664 PMCID: PMC9268975 DOI: 10.3390/plants11131712] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2022] [Revised: 06/20/2022] [Accepted: 06/24/2022] [Indexed: 06/15/2023]
Abstract
The conventional plant breeding evaluation of large sets of plant phenotypes with precision and speed is very challenging. Thus, consistent, automated, multifaceted, and high-throughput phenotyping (HTP) technologies are becoming increasingly significant as tools to aid conventional breeding programs to develop genetically improved crops. With rapid technological advancement, various vegetation indices (VIs) have been developed. These VI-based imaging approaches, linked with artificial intelligence and a variety of remote sensing applications, provide high-throughput evaluations, particularly in the field of precision agriculture. VIs can be used to analyze and predict different quantitative and qualitative aspects of vegetation. Here, we provide an overview of the various VIs used in agricultural research, focusing on those that are often employed for crop or vegetation evaluation, because that has a linear relationship to crop output, which is frequently utilized in crop chlorophyll, health, moisture, and production predictions. In addition, the following aspects are here described: the importance of VIs in crop research and precision agriculture, their utilization in HTP, recent photogrammetry technology, mapping, and geographic information system software integrated with unmanned aerial vehicles and its key features. Finally, we discuss the challenges and future perspectives of HTP technologies and propose approaches for the development of new tools to assess plants' agronomic traits and data-driven HTP resolutions for precision breeding.
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Affiliation(s)
- Rupesh Tayade
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Korea; (R.T.); (L.L.)
| | - Jungbeom Yoon
- Horticultural and Herbal Crop Environment Division, National Institute of Horticultural and Herbal Science, Rural Development Administration, Wanju 55365, Korea;
| | - Liny Lay
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Korea; (R.T.); (L.L.)
| | - Abdul Latif Khan
- Department of Engineering Technology, University of Houston, Texas, TX 77204, USA;
| | - Youngnam Yoon
- Crop Production Technology Research Division, National Institute of Crop Science, Rural Development Administration, Miryang 50424, Korea
| | - Yoonha Kim
- Department of Applied Biosciences, Kyungpook National University, Daegu 41566, Korea; (R.T.); (L.L.)
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Li W, Liu J, Zhang H, Liu Z, Wang Y, Xing L, He Q, Du H. Plant pan-genomics: recent advances, new challenges, and roads ahead. J Genet Genomics 2022; 49:833-846. [PMID: 35750315 DOI: 10.1016/j.jgg.2022.06.004] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 06/09/2022] [Accepted: 06/10/2022] [Indexed: 10/18/2022]
Abstract
Pan-genomics can encompass most of the genetic diversity of a species or population and has proved to be a powerful tool for studying genomic evolution and the origin and domestication of species, and for providing information for plant improvement. Plant genomics has greatly progressed because of improvements in sequencing technologies and the rapid reduction of sequencing costs. Nevertheless, pan-genomics still presents many challenges, including computationally intensive assembly methods, high costs with large numbers of samples, ineffective integration of big data, and difficulty in applying it to downstream multi-omics analysis and breeding research. In this review, we summarize the definition and recent achievements of plant pan-genomics, computational technologies used for pan-genome construction, and the applications of pan-genomes in plant genomics and molecular breeding. We also discuss challenges and perspectives for future pan-genomics studies and provide a detailed pipeline for sample selection, genome assembly and annotation, structural variation identification, and construction and application of graph-based pan-genomes. The aim is to provide important guidance for plant pan-genome research and a better understanding of the genetic basis of genome evolution, crop domestication, and phenotypic diversity for future studies.
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Affiliation(s)
- Wei Li
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China
| | - Jianan Liu
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China
| | - Hongyu Zhang
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China
| | - Ze Liu
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China
| | - Yu Wang
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China
| | - Longsheng Xing
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China
| | - Qiang He
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China
| | - Huilong Du
- School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding, Hebei 071000, China.
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Deb M, Garai A, Das A, Dhal KG. LS-Net: a convolutional neural network for leaf segmentation of rosette plants. Neural Comput Appl 2022. [DOI: 10.1007/s00521-022-07479-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Ren A, Jiang D, Kang M, Wu J, Xiao F, Hou P, Fu X. Evaluation of an intelligent artificial climate chamber for high-throughput crop phenotyping in wheat. PLANT METHODS 2022; 18:77. [PMID: 35672714 PMCID: PMC9170875 DOI: 10.1186/s13007-022-00916-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Accepted: 05/28/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND The superposition of COVID-19 and climate change has brought great challenges to global food security. As a major economic crop in the world, studying its phenotype to cultivate high-quality wheat varieties is an important way to increase grain yield. However, most of the existing phenotyping platforms have the disadvantages of high construction and maintenance costs, immobile and limited in use by climatic factors, while the traditional climate chambers lack phenotypic data acquisition, which makes crop phenotyping research and development difficult. Crop breeding progress is slow. At present, there is an urgent need to develop a low-cost, easy-to-promote, climate- and site-independent facility that combines the functions of crop cultivation and phenotype acquisition. We propose a movable cabin-type intelligent artificial climate chamber, and build an environmental control system, a crop phenotype monitoring system, and a crop phenotype acquisition system. RESULT We selected two wheat varieties with different early vigor to carry out the cultivation experiments and phenotype acquisition of wheat under different nitrogen fertilizer application rates in an intelligent artificial climate chamber. With the help of the crop phenotype acquisition system, images of wheat at the trefoil stage, pre-tillering stage, late tillering stage and jointing stage were collected, and then the phenotypic information including wheat leaf area, plant height, and canopy temperature were extracted by the crop type acquisition system. We compared systematic and manual measurements of crop phenotypes for wheat phenotypes. The results of the analysis showed that the systematic measurements of leaf area, plant height and canopy temperature of wheat in four growth periods were highly correlated with the artificial measurements. The correlation coefficient (r) is positive, and the determination coefficient (R2) is greater than 0.7156. The root mean square error (RSME) is less than 2.42. Among them, the crop phenotype-based collection system has the smallest measurement error for the phenotypic characteristics of wheat trefoil stage. The canopy temperature RSME is only 0.261. The systematic measurement values of wheat phenotypic characteristics were significantly positively correlated with the artificial measurement values, the fitting degree was good, and the errors were all within the acceptable range. The experiment showed that the phenotypic data obtained with the intelligent artificial climate chamber has high accuracy. We verified the feasibility of wheat cultivation and phenotype acquisition based on intelligent artificial climate chamber. CONCLUSION It is feasible to study wheat cultivation and canopy phenotype with the help of intelligent artificial climate chamber. Based on a variety of environmental monitoring sensors and environmental regulation equipment, the growth environment factors of crops can be adjusted. Based on high-precision mechanical transmission and multi-dimensional imaging sensors, crop images can be collected to extract crop phenotype information. Its use is not limited by environmental and climatic factors. Therefore, the intelligent artificial climate chamber is expected to be a powerful tool for breeders to develop excellent germplasm varieties.
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Affiliation(s)
- Anhua Ren
- College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China
| | - Dong Jiang
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, 210095, China
| | - Min Kang
- College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China
- Jiangsu Key Laboratory of Intelligence Agricultural Equipment, Nanjing, 210031, China
| | - Jie Wu
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, 210095, China
| | - Fangcheng Xiao
- Nanjing Huitong Crop Phenotypic Research Institute Co., Ltd, Nanjing, 211225, China
| | - Pei Hou
- College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China
| | - Xiuqing Fu
- College of Engineering, Nanjing Agricultural University, Nanjing, 210031, China.
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, 210095, China.
- Jiangsu Key Laboratory of Intelligence Agricultural Equipment, Nanjing, 210031, China.
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Fu P, Montes CM, Siebers MH, Gomez-Casanovas N, McGrath JM, Ainsworth EA, Bernacchi CJ. Advances in field-based high-throughput photosynthetic phenotyping. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:3157-3172. [PMID: 35218184 PMCID: PMC9126737 DOI: 10.1093/jxb/erac077] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/23/2022] [Indexed: 05/22/2023]
Abstract
Gas exchange techniques revolutionized plant research and advanced understanding, including associated fluxes and efficiencies, of photosynthesis, photorespiration, and respiration of plants from cellular to ecosystem scales. These techniques remain the gold standard for inferring photosynthetic rates and underlying physiology/biochemistry, although their utility for high-throughput phenotyping (HTP) of photosynthesis is limited both by the number of gas exchange systems available and the number of personnel available to operate the equipment. Remote sensing techniques have long been used to assess ecosystem productivity at coarse spatial and temporal resolutions, and advances in sensor technology coupled with advanced statistical techniques are expanding remote sensing tools to finer spatial scales and increasing the number and complexity of phenotypes that can be extracted. In this review, we outline the photosynthetic phenotypes of interest to the plant science community and describe the advances in high-throughput techniques to characterize photosynthesis at spatial scales useful to infer treatment or genotypic variation in field-based experiments or breeding trials. We will accomplish this objective by presenting six lessons learned thus far through the development and application of proximal/remote sensing-based measurements and the accompanying statistical analyses. We will conclude by outlining what we perceive as the current limitations, bottlenecks, and opportunities facing HTP of photosynthesis.
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Affiliation(s)
- Peng Fu
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Christopher M Montes
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
| | - Matthew H Siebers
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
| | - Nuria Gomez-Casanovas
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Justin M McGrath
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
| | - Elizabeth A Ainsworth
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
| | - Carl J Bernacchi
- Carl R. Woese Institute for Genomic Biology, University of Illinois at Urbana-Champaign, Champaign, IL, USA
- Department of Plant Biology, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Department of Crop Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- United States Department of Agriculture, Global Change and Photosynthesis Research Unit, Agricultural Research Service, Urbana, IL, USA
- Institute for Sustainability, Energy & Environment, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Center for Advanced Bioenergy and Bioproducts Innovation, University of Illinois at Urbana-Champaign, Urbana, IL, USA
- Correspondence:
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Liang F, Zhan W, Hu G, Liu H, Xing Y, Li Z, Han Z. Five plants per RIL for phenotyping traits of high or moderate heritability ensure the power of QTL mapping in a rice MAGIC population. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2022; 42:28. [PMID: 37309531 PMCID: PMC10248629 DOI: 10.1007/s11032-022-01299-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/20/2022] [Indexed: 06/14/2023]
Abstract
Currently, the power of QTL mapping is mainly dependent on the quality of phenotypic data in a given population, regardless of the statistical method, as the quality of genotypic data is easily guaranteed in the laboratory. Increasing the sample size per line used for phenotyping is a good way to improve the quality of phenotypic data. However, accommodating a large-scale mapping population takes a large area of rice field, which frequently results in high costs and extra environmental noises. To acquire a reasonable small sample size without a penalty in mapping power, we conducted three experiments with a 4-way MAGIC population and measured phenotypes of 5, 10, and 20 plants per RIL. Three traits including heading date, plant height, and tillers per plant were focused. With SNP- and bin-based QTL mapping, 3 major and 3 minor QTLs for heading date with high heritability and 2 major QTLs for plant height with moderate heritability were commonly detected across the three experiments, but no QTL for tillers per plant with low heritability were commonly identified. In addition, bin-based QTL mapping was more powerful than SNP-based mapping and able to rank the genetic effects of parental alleles. Thus, 5 plants per RIL for phenotyping ensure the power of QTL mapping for traits of high or moderate heritability, and bin-based QTL mapping is recommended for multiparent populations.
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Affiliation(s)
- Famao Liang
- College of Agriculture, Yangtze University, Jingzhou, 434000 China
| | - Wei Zhan
- Hubei Provincial Key Laboratory for Protection and Application of Special Plant Germplasm in Wuling Area of China, South-Central University for Nationalities, Wuhan, 430074 China
| | - Gang Hu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430070 China
| | - Hua Liu
- College of Agriculture, Yangtze University, Jingzhou, 434000 China
| | - Yongzhong Xing
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430070 China
| | - Zhixin Li
- College of Agriculture, Yangtze University, Jingzhou, 434000 China
| | - Zhongmin Han
- Key Laboratory of Soybean Molecular Design Breeding, Northeast Institute of Geography and Agroecology, The Innovative Academy of Seed Design, Chinese Academy of Sciences, 150081 Harbin, China
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agriculture University, Wuhan, 430070 China
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Cao P, Zhao Y, Wu F, Xin D, Liu C, Wu X, Lv J, Chen Q, Qi Z. Multi-Omics Techniques for Soybean Molecular Breeding. Int J Mol Sci 2022; 23:4994. [PMID: 35563386 PMCID: PMC9099442 DOI: 10.3390/ijms23094994] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Revised: 04/22/2022] [Accepted: 04/28/2022] [Indexed: 02/04/2023] Open
Abstract
Soybean is a major crop that provides essential protein and oil for food and feed. Since its origin in China over 5000 years ago, soybean has spread throughout the world, becoming the second most important vegetable oil crop and the primary source of plant protein for global consumption. From early domestication and artificial selection through hybridization and ultimately molecular breeding, the history of soybean breeding parallels major advances in plant science throughout the centuries. Now, rapid progress in plant omics is ushering in a new era of precision design breeding, exemplified by the engineering of elite soybean varieties with specific oil compositions to meet various end-use targets. The assembly of soybean reference genomes, made possible by the development of genome sequencing technology and bioinformatics over the past 20 years, was a great step forward in soybean research. It facilitated advances in soybean transcriptomics, proteomics, metabolomics, and phenomics, all of which paved the way for an integrated approach to molecular breeding in soybean. In this review, we summarize the latest progress in omics research, highlight novel findings made possible by omics techniques, note current drawbacks and areas for further research, and suggest that an efficient multi-omics approach may accelerate soybean breeding in the future. This review will be of interest not only to soybean breeders but also to researchers interested in the use of cutting-edge omics technologies for crop research and improvement.
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Affiliation(s)
- Pan Cao
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
| | - Ying Zhao
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
| | - Fengjiao Wu
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
| | - Dawei Xin
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
| | - Chunyan Liu
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
| | - Xiaoxia Wu
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
| | - Jian Lv
- Department of Innovation, Syngenta Biotechnology China, Beijing 102206, China
| | - Qingshan Chen
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
| | - Zhaoming Qi
- College of Agriculture, Northeast Agricultural University, Harbin 150030, China; (P.C.); (Y.Z.); (F.W.); (D.X.); (C.L.)
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Paes de Melo B, Carpinetti PDA, Fraga OT, Rodrigues-Silva PL, Fioresi VS, de Camargos LF, Ferreira MFDS. Abiotic Stresses in Plants and Their Markers: A Practice View of Plant Stress Responses and Programmed Cell Death Mechanisms. PLANTS (BASEL, SWITZERLAND) 2022; 11:1100. [PMID: 35567101 PMCID: PMC9103730 DOI: 10.3390/plants11091100] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/06/2022] [Accepted: 04/07/2022] [Indexed: 05/12/2023]
Abstract
Understanding how plants cope with stress and the intricate mechanisms thereby used to adapt and survive environmental imbalances comprise one of the most powerful tools for modern agriculture. Interdisciplinary studies suggest that knowledge in how plants perceive, transduce and respond to abiotic stresses are a meaningful way to design engineered crops since the manipulation of basic characteristics leads to physiological remodeling for plant adaption to different environments. Herein, we discussed the main pathways involved in stress-sensing, signal transduction and plant adaption, highlighting biochemical, physiological and genetic events involved in abiotic stress responses. Finally, we have proposed a list of practice markers for studying plant responses to multiple stresses, highlighting how plant molecular biology, phenotyping and genetic engineering interconnect for creating superior crops.
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Affiliation(s)
- Bruno Paes de Melo
- Trait Development Department, LongPing HighTech, Cravinhos 14140-000, SP, Brazil
| | - Paola de Avelar Carpinetti
- Genetics and Breeding Program, Universidade Federal do Espírito Santo, Alegre 29500-000, ES, Brazil; (P.d.A.C.); (V.S.F.); (M.F.d.S.F.)
| | - Otto Teixeira Fraga
- Applied Biochemistry Program, Universidade Federal de Viçosa, Viçosa 36570-000, MG, Brazil;
| | | | - Vinícius Sartori Fioresi
- Genetics and Breeding Program, Universidade Federal do Espírito Santo, Alegre 29500-000, ES, Brazil; (P.d.A.C.); (V.S.F.); (M.F.d.S.F.)
| | | | - Marcia Flores da Silva Ferreira
- Genetics and Breeding Program, Universidade Federal do Espírito Santo, Alegre 29500-000, ES, Brazil; (P.d.A.C.); (V.S.F.); (M.F.d.S.F.)
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Kudenov MW, Altaqui A, Williams C. Practical spectral photography II: snapshot spectral imaging using linear retarders and microgrid polarization cameras. OPTICS EXPRESS 2022; 30:12337-12352. [PMID: 35472871 DOI: 10.1364/oe.453538] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/11/2022] [Accepted: 03/20/2022] [Indexed: 06/14/2023]
Abstract
Despite recent advances, customized multispectral cameras can be challenging or costly to deploy in some use cases. Complexities span electronic synchronization, multi-camera calibration, parallax and spatial co-registration, and data acquisition from multiple cameras, all of which can hamper their ease of use. This paper discusses a generalized procedure for multispectral sensing using a pixelated polarization camera and anisotropic polymer film retarders to create multivariate optical filters. We then describe the calibration procedure, which leverages neural networks to convert measured data into calibrated spectra (intensity versus wavelength). Experimental results are presented for a multivariate and channeled optical filter. Finally, imaging results taken using a red, green, and blue microgrid polarization camera and the channeled optical filter are presented. Imaging experiments indicated that the calculated spectra's root mean square error is highest in the region where the camera's red, green, and blue filter responses overlap. The average error of the spectral reflectance, measured of our spectralon tiles, was 6.5% for wavelengths spanning 425-675 nm. This technique demonstrates that 12 spectral channels can be obtained with a relatively simple and robust optical setup, and at minimal cost beyond the purchase of the camera.
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Noshita K, Murata H, Kirie S. Model-based plant phenomics on morphological traits using morphometric descriptors. BREEDING SCIENCE 2022; 72:19-30. [PMID: 36045892 PMCID: PMC8987841 DOI: 10.1270/jsbbs.21078] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Accepted: 12/20/2021] [Indexed: 06/15/2023]
Abstract
The morphological traits of plants contribute to many important functional features such as radiation interception, lodging tolerance, gas exchange efficiency, spatial competition between individuals and/or species, and disease resistance. Although the importance of plant phenotyping techniques is increasing with advances in molecular breeding strategies, there are barriers to its advancement, including the gap between measured data and phenotypic values, low quantitativity, and low throughput caused by the lack of models for representing morphological traits. In this review, we introduce morphological descriptors that can be used for phenotyping plant morphological traits. Geometric morphometric approaches pave the way to a general-purpose method applicable to single units. Hierarchical structures composed of an indefinite number of multiple elements, which is often observed in plants, can be quantified in terms of their multi-scale topological characteristics using topological data analysis. Theoretical morphological models capture specific anatomical structures, if recognized. These morphological descriptors provide us with the advantages of model-based plant phenotyping, including robust quantification of limited datasets. Moreover, we discuss the future possibilities that a system of model-based measurement and model refinement would solve the lack of morphological models and the difficulties in scaling out the phenotyping processes.
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Affiliation(s)
- Koji Noshita
- Department of Biology, Kyushu University, Fukuoka, Fukuoka 819-0395, Japan
- Plant Frontier Research Center, Kyushu University, Fukuoka, Fukuoka 819-0395, Japan
| | - Hidekazu Murata
- Department of Biology, Kyushu University, Fukuoka, Fukuoka 819-0395, Japan
| | - Shiryu Kirie
- metaPhorest (Bioaesthetics Platform), Department of Electrical Engineering and Bioscience, Waseda University, TWIns, Tokyo 162-8480, Japan
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Ostmeyer TJ, Bahuguna RN, Kirkham MB, Bean S, Jagadish SVK. Enhancing Sorghum Yield Through Efficient Use of Nitrogen - Challenges and Opportunities. FRONTIERS IN PLANT SCIENCE 2022; 13:845443. [PMID: 35295626 PMCID: PMC8919068 DOI: 10.3389/fpls.2022.845443] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2021] [Accepted: 02/08/2022] [Indexed: 06/14/2023]
Abstract
Sorghum is an important crop, which is widely used as food, forage, fodder and biofuel. Despite its natural adaption to resource-poor and stressful environments, increasing yield potential of sorghum under more favorable conditions holds promise. Nitrogen is the most important nutrient for crops, having a dynamic impact on all growth, yield, and grain-quality-determining processes. Thus, increasing nitrogen use efficiency (NUE) in sorghum would provide opportunities to achieve higher yield and better-quality grain. NUE is a complex trait, which is regulated by several genes. Hence, exploring genetic diversity for NUE can help to develop molecular markers associated with NUE, which can be utilized to develop high NUE sorghum genotypes with greater yield potential. Research on improving NUE in sorghum suggests that, under water-deficit conditions, traits such as stay-green and altered canopy architecture, and under favorable conditions, traits such as an optimized stay-green and senescence ratio and efficient N translocation to grain, are potential breeding targets to develop high NUE sorghum genotypes. Hence, under a wide range of environments, sorghum breeding programs will need to reconsider strategies and develop breeding programs based on environment-specific trait(s) for better adaptation and improvement in productivity and grain quality. Unprecedented progress in sensor-based technology and artificial intelligence in high-throughput phenotyping has provided new horizons to explore complex traits in situ, such as NUE. A better understanding of the genetics and molecular pathways involving NUE, accompanied by targeted high-throughput sensor-based indices, is critical for identifying lines or developing management practices to enhance NUE in sorghum.
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Affiliation(s)
- Troy J. Ostmeyer
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Rajeev Nayan Bahuguna
- Center for Advanced Studies on Climate Change, Dr. Rajendra Prasad Central Agricultural University, Samastipur, India
| | - M. B. Kirkham
- Department of Agronomy, Kansas State University, Manhattan, KS, United States
| | - Scott Bean
- Grain Quality and Structure Research Unit, CGAHR, USDA-ARS, Manhattan, KS, United States
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Phenotypic Traits Extraction and Genetic Characteristics Assessment of Eucalyptus Trials Based on UAV-Borne LiDAR and RGB Images. REMOTE SENSING 2022. [DOI: 10.3390/rs14030765] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
Phenotype describes the physical, physiological and biochemical characteristics of organisms that are determined or influenced by genes and environment. Accurate extraction of phenotypic data is a prerequisite for comprehensive forest phenotyping in order to improve the growth and development of forest plantations. Combined with the assessments of genetic characteristics, forest phenotyping will help to accelerate the breeding process, improve stress resistance and enhance the quality of the planted forest. In this study, we disposed our study in Eucalyptus trials within the Gaofeng forest farm (a typical Eucalyptus plantation site in southern China) for a high-throughput phenotypic traits extraction and genetic characteristics analysis based on high-density point clouds (acquired by a UAV-borne LiDAR sensor) and high-resolution RGB images (acquired by a UAV-borne camera), aiming at developing a high-resolution and high-throughput UAV-based phenotyping approach for tree breeding. First, we compared the effect of CHM-based Marker-Controlled Watershed Segmentation (MWS) and Point Cloud-based Cluster Segmentation (PCS) for extracting individual trees; Then, the phenotypic traits (i.e., tree height, diameter at breast height, crown width), the structural metrics (n = 19) and spectral indices (n = 9) of individual trees were extracted and assessed; Finally, a genetic characteristics analysis was carried out based on the above results, and we compared the differences between high-throughput phenotyping by UAV-based data and on manual measurements. Results showed that: in the relatively low stem density site of the trial (760 n/ha), the overall accuracy of MWS and PCS was similar, while in the higher stem density sites (982 n/ha, 1239 n/ha), the overall accuracy of MWS (F(2) = 0.93, F(3) = 0.86) was higher than PCS (F(2) = 0.84, F(3) = 0.74); With the increase of stem density, the difference between the overall accuracy of MWS and PCS gradually expanded. Both UAV–LiDAR extracted phenotypic traits and manual measurements were significantly different across the Eucalyptus clones (P < 0.05), as were most of the structural metrics (47/57) and spectral indices (26/27), revealing the genetic divergence between the clones. The rank of clones demonstrated that the pure clones (of E. urophylla), the hybrid clones (of E. urophylla as the female parent) and the hybrid clones (of E. wetarensis and E. grandis) have a higher fineness of growth. This study proved that UAV-based fine-resolution remote sensing could be an efficient, accurate and precise technology in phenotyping (used in genetic analysis) for tree breeding.
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