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Yan W, Sharif R, Sohail H, Zhu Y, Chen X, Xu X. Surviving a Double-Edged Sword: Response of Horticultural Crops to Multiple Abiotic Stressors. Int J Mol Sci 2024; 25:5199. [PMID: 38791235 PMCID: PMC11121501 DOI: 10.3390/ijms25105199] [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: 03/31/2024] [Revised: 05/04/2024] [Accepted: 05/08/2024] [Indexed: 05/26/2024] Open
Abstract
Climate change-induced weather events, such as extreme temperatures, prolonged drought spells, or flooding, pose an enormous risk to crop productivity. Studies on the implications of multiple stresses may vary from those on a single stress. Usually, these stresses coincide, amplifying the extent of collateral damage and contributing to significant financial losses. The breadth of investigations focusing on the response of horticultural crops to a single abiotic stress is immense. However, the tolerance mechanisms of horticultural crops to multiple abiotic stresses remain poorly understood. In this review, we described the most prevalent types of abiotic stresses that occur simultaneously and discussed them in in-depth detail regarding the physiological and molecular responses of horticultural crops. In particular, we discussed the transcriptional, posttranscriptional, and metabolic responses of horticultural crops to multiple abiotic stresses. Strategies to breed multi-stress-resilient lines have been presented. Our manuscript presents an interesting amount of proposed knowledge that could be valuable in generating resilient genotypes for multiple stressors.
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Affiliation(s)
- Wenjing Yan
- School of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China; (W.Y.); (R.S.); (H.S.); (Y.Z.); (X.C.)
| | - Rahat Sharif
- School of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China; (W.Y.); (R.S.); (H.S.); (Y.Z.); (X.C.)
| | - Hamza Sohail
- School of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China; (W.Y.); (R.S.); (H.S.); (Y.Z.); (X.C.)
| | - Yu Zhu
- School of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China; (W.Y.); (R.S.); (H.S.); (Y.Z.); (X.C.)
| | - Xuehao Chen
- School of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China; (W.Y.); (R.S.); (H.S.); (Y.Z.); (X.C.)
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, The Ministry of Education of China, Yangzhou University, Yangzhou 225009, China
| | - Xuewen Xu
- School of Horticulture and Landscape Architecture, Yangzhou University, Yangzhou 225009, China; (W.Y.); (R.S.); (H.S.); (Y.Z.); (X.C.)
- Joint International Research Laboratory of Agriculture and Agri-Product Safety, The Ministry of Education of China, Yangzhou University, Yangzhou 225009, China
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2
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Kopeć P. Climate Change-The Rise of Climate-Resilient Crops. PLANTS (BASEL, SWITZERLAND) 2024; 13:490. [PMID: 38498432 PMCID: PMC10891513 DOI: 10.3390/plants13040490] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/28/2023] [Revised: 01/31/2024] [Accepted: 02/06/2024] [Indexed: 03/20/2024]
Abstract
Climate change disrupts food production in many regions of the world. The accompanying extreme weather events, such as droughts, floods, heat waves, and cold snaps, pose threats to crops. The concentration of carbon dioxide also increases in the atmosphere. The United Nations is implementing the climate-smart agriculture initiative to ensure food security. An element of this project involves the breeding of climate-resilient crops or plant cultivars with enhanced resistance to unfavorable environmental conditions. Modern agriculture, which is currently homogeneous, needs to diversify the species and cultivars of cultivated plants. Plant breeding programs should extensively incorporate new molecular technologies, supported by the development of field phenotyping techniques. Breeders should closely cooperate with scientists from various fields of science.
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Affiliation(s)
- Przemysław Kopeć
- The Franciszek Górski Institute of Plant Physiology, Polish Academy of Sciences, Niezapominajek 21, 30-239 Kraków, Poland
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3
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Kenzhebayeva S, Mazkirat S, Shoinbekova S, Atabayeva S, Abekova A, Omirbekova N, Doktyrbay G, Asrandina S, Zharassova D, Amirova A, Serfling A. Phenotyping and Exploitation of Kompetitive Allele-Specific PCR Assays for Genes Underpinning Leaf Rust Resistance in New Spring Wheat Mutant Lines. Curr Issues Mol Biol 2024; 46:689-709. [PMID: 38248347 PMCID: PMC10814123 DOI: 10.3390/cimb46010045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
Leaf rust (Puccinia triticina Eriks) is a wheat disease causing substantial yield losses in wheat production globally. The identification of genetic resources with permanently effective resistance genes and the generation of mutant lines showing increased levels of resistance allow the efficient incorporation of these target genes into germplasm pools by marker-assisted breeding. In this study, new mutant (M3 generation) lines generated from the rust-resistant variety Kazakhstanskaya-19 were developed using gamma-induced mutagenesis through 300-, 350-, and 400-Gy doses. In field trials after leaf rust inoculation, 75 mutant lines showed adult plant resistance. These lines were evaluated for resistance at the seedling stage via microscopy in greenhouse experiments. Most of these lines (89.33%) were characterized as resistant at both developmental stages. Hyperspectral imaging analysis indicated that infected leaves of wheat genotypes showed increased relative reflectance in visible and near-infrared light compared to the non-infected genotypes, with peak means at 462 and 644 nm, and 1936 and 2392 nm, respectively. Five spectral indexes, including red edge normalized difference vegetation index (RNDVI), structure-insensitive pigment index (SIPI), ratio vegetation index (RVSI), water index (WI), and normalized difference water index (NDWI), demonstrated significant potential for determining disease severity at the seedling stage. The most significant differences in reflectance between susceptible and resistant mutant lines appeared at 694.57 and 987.51 nm. The mutant lines developed were also used for the development and validation of KASP markers for leaf rust resistance genes Lr1, Lr2a, Lr3, Lr9, Lr10, and Lr17. The mutant lines had high frequencies of "a" resistance alleles (0.88) in all six Lr genes, which were significantly associated with seedling resistance and suggest the potential of favorable haplotype introgression through functional markers. Nine mutant lines characterized by the presence of "b" alleles in Lr9 and Lr10-except for one line with allele "a" in Lr9 and three mutant lines with allele "a" in Lr10-showed the progressive development of fungal haustorial mother cells 72 h after inoculation. One line from 300-Gy-dosed mutant germplasm with "b" alleles in Lr1, Lr2a, Lr10, and Lr17 and "a" alleles in Lr3 and Lr9 was characterized as resistant based on the low number of haustorial mother cells, suggesting the contribution of the "a" alleles of Lr3 and Lr9.
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Affiliation(s)
- Saule Kenzhebayeva
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (S.S.); (S.A.); (N.O.); (G.D.); (S.A.); (A.A.)
| | - Shynarbek Mazkirat
- Kazakh Research Institute of Agriculture and Plant Growing, Almaty Region, Almalybak 040909, Kazakhstan; (S.M.); (A.A.)
| | - Sabina Shoinbekova
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (S.S.); (S.A.); (N.O.); (G.D.); (S.A.); (A.A.)
| | - Saule Atabayeva
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (S.S.); (S.A.); (N.O.); (G.D.); (S.A.); (A.A.)
| | - Alfia Abekova
- Kazakh Research Institute of Agriculture and Plant Growing, Almaty Region, Almalybak 040909, Kazakhstan; (S.M.); (A.A.)
| | - Nargul Omirbekova
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (S.S.); (S.A.); (N.O.); (G.D.); (S.A.); (A.A.)
| | - Gulina Doktyrbay
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (S.S.); (S.A.); (N.O.); (G.D.); (S.A.); (A.A.)
| | - Saltant Asrandina
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (S.S.); (S.A.); (N.O.); (G.D.); (S.A.); (A.A.)
| | - Dinara Zharassova
- Mangyshlak Experimental Botanical Garden, Ministry of Science and Higher Education of the Republic of Kazakhstan, Aktau R00A3E0, Kazakhstan;
| | - Aigul Amirova
- Faculty of Biology and Biotechnology, Al-Farabi Kazakh National University, Almaty 050040, Kazakhstan; (S.S.); (S.A.); (N.O.); (G.D.); (S.A.); (A.A.)
| | - Albrecht Serfling
- Institute for Resistance Research and Stress Tolerance, Julius Kuehn-Institute (JKI) Federal Research Centre for Cultivated Plants, 06484 Quedlinburg, Germany;
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Ding G, Shen L, Dai J, Jackson R, Liu S, Ali M, Sun L, Wen M, Xiao J, Deakin G, Jiang D, Wang XE, Zhou J. The Dissection of Nitrogen Response Traits Using Drone Phenotyping and Dynamic Phenotypic Analysis to Explore N Responsiveness and Associated Genetic Loci in Wheat. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0128. [PMID: 38148766 PMCID: PMC10750832 DOI: 10.34133/plantphenomics.0128] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/03/2023] [Accepted: 11/23/2023] [Indexed: 12/28/2023]
Abstract
Inefficient nitrogen (N) utilization in agricultural production has led to many negative impacts such as excessive use of N fertilizers, redundant plant growth, greenhouse gases, long-lasting toxicity in ecosystem, and even effect on human health, indicating the importance to optimize N applications in cropping systems. Here, we present a multiseasonal study that focused on measuring phenotypic changes in wheat plants when they were responding to different N treatments under field conditions. Powered by drone-based aerial phenotyping and the AirMeasurer platform, we first quantified 6 N response-related traits as targets using plot-based morphological, spectral, and textural signals collected from 54 winter wheat varieties. Then, we developed dynamic phenotypic analysis using curve fitting to establish profile curves of the traits during the season, which enabled us to compute static phenotypes at key growth stages and dynamic phenotypes (i.e., phenotypic changes) during N response. After that, we combine 12 yield production and N-utilization indices manually measured to produce N efficiency comprehensive scores (NECS), based on which we classified the varieties into 4 N responsiveness (i.e., N-dependent yield increase) groups. The NECS ranking facilitated us to establish a tailored machine learning model for N responsiveness-related varietal classification just using N-response phenotypes with high accuracies. Finally, we employed the Wheat55K SNP Array to map single-nucleotide polymorphisms using N response-related static and dynamic phenotypes, helping us explore genetic components underlying N responsiveness in wheat. In summary, we believe that our work demonstrates valuable advances in N response-related plant research, which could have major implications for improving N sustainability in wheat breeding and production.
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Affiliation(s)
- Guohui Ding
- College of Agriculture, Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies,
Nanjing Agricultural University, Nanjing 210095, China
| | - Liyan Shen
- College of Agriculture, Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies,
Nanjing Agricultural University, Nanjing 210095, China
| | - Jie Dai
- College of Agriculture, Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies,
Nanjing Agricultural University, Nanjing 210095, China
| | - Robert Jackson
- Cambridge Crop Research,
National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK
| | - Shuchen Liu
- College of Agriculture, Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies,
Nanjing Agricultural University, Nanjing 210095, China
| | - Mujahid Ali
- College of Agriculture, Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies,
Nanjing Agricultural University, Nanjing 210095, China
| | - Li Sun
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cytogenetics Institute,
Nanjing Agricultural University/JCIC-MCP, Nanjing, Jiangsu 210095, China
| | - Mingxing Wen
- Zhenjiang Institute of Agricultural Science, Jurong, Jiangsu 212400, China
| | - Jin Xiao
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cytogenetics Institute,
Nanjing Agricultural University/JCIC-MCP, Nanjing, Jiangsu 210095, China
| | - Greg Deakin
- Cambridge Crop Research,
National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK
| | - Dong Jiang
- Regional Technique Innovation Center for Wheat Production, Key Laboratory of Crop Physiology and Ecology in Southern China, Ministry of Agriculture,
Nanjing Agricultural University, Nanjing, Jiangsu 210095, China
| | - Xiu-e Wang
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, Cytogenetics Institute,
Nanjing Agricultural University/JCIC-MCP, Nanjing, Jiangsu 210095, China
| | - Ji Zhou
- College of Agriculture, Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies,
Nanjing Agricultural University, Nanjing 210095, China
- Cambridge Crop Research,
National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK
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5
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Yamazaki A, Takezawa A, Nagasaka K, Motoki K, Nishimura K, Nakano R, Nakazaki T. A simple method for measuring pollen germination rate using machine learning. PLANT REPRODUCTION 2023; 36:355-364. [PMID: 37278944 DOI: 10.1007/s00497-023-00472-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 05/30/2023] [Indexed: 06/07/2023]
Abstract
The pollen germination rate decreases under various abiotic stresses, such as high-temperature stress, and it is one of the causes of inhibition of plant reproduction. Thus, measuring pollen germination rate is vital for understanding the reproductive ability of plants. However, measuring the pollen germination rate requires much labor when counting pollen. Therefore, we used the Yolov5 machine learning package in order to perform transfer learning and constructed a model that can detect germinated and non-germinated pollen separately. Pollen images of the chili pepper, Capsicum annuum, were used to create this model. Using images with a width of 640 pixels for training constructed a more accurate model than using images with a width of 320 pixels. This model could estimate the pollen germination rate of the F2 population of C. chinense previously studied with high accuracy. In addition, significantly associated gene regions previously detected in genome-wide association studies in this F2 population could again be detected using the pollen germination rate predicted by this model as a trait. Moreover, the model detected rose, tomato, radish, and strawberry pollen grains with similar accuracy to chili pepper. The pollen germination rate could be estimated even for plants other than chili pepper, probably because pollen images were similar among different plant species. We obtained a model that can identify genes related to pollen germination rate through genetic analyses in many plants.
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Affiliation(s)
- Akira Yamazaki
- Faculty of Agriculture, Kindai University, Nara, 631-8505, Japan.
| | - Ao Takezawa
- Graduate School of Agriculture, Kyoto University, Kizugawa, 619-0218, Japan
| | - Kyoka Nagasaka
- Graduate School of Agriculture, Kyoto University, Kizugawa, 619-0218, Japan
| | - Ko Motoki
- Graduate School of Agriculture, Kyoto University, Kizugawa, 619-0218, Japan
| | - Kazusa Nishimura
- Graduate School of Agriculture, Kyoto University, Kizugawa, 619-0218, Japan
| | - Ryohei Nakano
- Graduate School of Agriculture, Kyoto University, Kizugawa, 619-0218, Japan
| | - Tetsuya Nakazaki
- Graduate School of Agriculture, Kyoto University, Kizugawa, 619-0218, Japan
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6
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Anshori MF, Dirpan A, Sitaresmi T, Rossi R, Farid M, Hairmansis A, Sapta Purwoko B, Suwarno WB, Nugraha Y. An overview of image-based phenotyping as an adaptive 4.0 technology for studying plant abiotic stress: A bibliometric and literature review. Heliyon 2023; 9:e21650. [PMID: 38027954 PMCID: PMC10660044 DOI: 10.1016/j.heliyon.2023.e21650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 09/20/2023] [Accepted: 10/25/2023] [Indexed: 12/01/2023] Open
Abstract
Improving the tolerance of crop species to abiotic stresses that limit plant growth and productivity is essential for mitigating the emerging problems of global warming. In this context, imaged data analysis represents an effective method in the 4.0 technology era, where this method has the non-destructive and recursive characterization of plant phenotypic traits as selection criteria. So, the plant breeders are helped in the development of adapted and climate-resilient crop varieties. Although image-based phenotyping has recently resulted in remarkable improvements for identifying the crop status under a range of growing conditions, the topic of its application for assessing the plant behavioral responses to abiotic stressors has not yet been extensively reviewed. For such a purpose, bibliometric analysis is an ideal analytical concept to analyze the evolution and interplay of image-based phenotyping to abiotic stresses by objectively reviewing the literature in light of existing database. Bibliometricy, a bibliometric analysis was applied using a systematic methodology which involved data mining, mining data improvement and analysis, and manuscript construction. The obtained results indicate that there are 554 documents related to image-based phenotyping to abiotic stress until 5 January 2023. All document showed the future development trends of image-based phenotyping will be mainly centered in the United States, European continent and China. The keywords analysis major focus to the application of 4.0 technology and machine learning in plant breeding, especially to create the tolerant variety under abiotic stresses. Drought and saline become an abiotic stress often using image-based phenotyping. Besides that, the rice, wheat and maize as the main commodities in this topic. In conclusion, the present work provides information on resolutive interactions in developing image-based phenotyping to abiotic stress, especially optimizing high-throughput sensors in image-based phenotyping for the future development.
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Affiliation(s)
| | - Andi Dirpan
- Department of Agricultural Technology, Hasanuddin University, Makassar, 90245, Indonesia
- Center of Excellence in Science and Technology on Food Product Diversification, 90245, Makassar, Indonesia
| | - Trias Sitaresmi
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, 16911, Cibinong, Indonesia
| | - Riccardo Rossi
- Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence (UNIFI), Piazzale delle Cascine 18, 50144, Florence, Italy
| | - Muh Farid
- Department of Agronomy, Hasanuddin University, Makassar, 90245, Indonesia
| | - Aris Hairmansis
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, 16911, Cibinong, Indonesia
| | - Bambang Sapta Purwoko
- Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Bogor, 11680, Indonesia
| | - Willy Bayuardi Suwarno
- Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Bogor, 11680, Indonesia
| | - Yudhistira Nugraha
- Research Center for Food Crops, Research Organization for Agriculture and Food, National Research and Innovation Agency, 16911, Cibinong, Indonesia
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7
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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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8
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Huang L, Zhang Y, Guo J, Peng Q, Zhou Z, Duan X, Tanveer M, Guo Y. High-throughput root phenotyping of crop cultivars tolerant to low N in waterlogged soils. FRONTIERS IN PLANT SCIENCE 2023; 14:1271539. [PMID: 37780519 PMCID: PMC10533935 DOI: 10.3389/fpls.2023.1271539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2023] [Accepted: 08/29/2023] [Indexed: 10/03/2023]
Affiliation(s)
- Liping Huang
- International Research Center for Environmental Membrane Biology, College of Food Science and Engineering, Foshan University, Foshan, China
- Foshan ZhiBao Ecological Technology Co. Ltd., Foshan, China
| | - Yujing Zhang
- International Research Center for Environmental Membrane Biology, College of Food Science and Engineering, Foshan University, Foshan, China
- Foshan ZhiBao Ecological Technology Co. Ltd., Foshan, China
| | - Jieru Guo
- International Research Center for Environmental Membrane Biology, College of Food Science and Engineering, Foshan University, Foshan, China
- Foshan ZhiBao Ecological Technology Co. Ltd., Foshan, China
| | - Qianlan Peng
- International Research Center for Environmental Membrane Biology, College of Food Science and Engineering, Foshan University, Foshan, China
| | - Zhaoyang Zhou
- International Research Center for Environmental Membrane Biology, College of Food Science and Engineering, Foshan University, Foshan, China
| | - Xiaosong Duan
- International Research Center for Environmental Membrane Biology, College of Food Science and Engineering, Foshan University, Foshan, China
| | - Mohsin Tanveer
- Tasmanian Institute of Agriculture, University of Tasmania, Hobart, TAS, Australia
| | - Yongjun Guo
- International Research Center for Environmental Membrane Biology, College of Food Science and Engineering, Foshan University, Foshan, China
- Foshan ZhiBao Ecological Technology Co. Ltd., Foshan, China
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9
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McCoy JCS, Spicer JI, Ibbini Z, Tills O. Phenomics as an approach to Comparative Developmental Physiology. Front Physiol 2023; 14:1229500. [PMID: 37645563 PMCID: PMC10461620 DOI: 10.3389/fphys.2023.1229500] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 07/24/2023] [Indexed: 08/31/2023] Open
Abstract
The dynamic nature of developing organisms and how they function presents both opportunity and challenge to researchers, with significant advances in understanding possible by adopting innovative approaches to their empirical study. The information content of the phenotype during organismal development is arguably greater than at any other life stage, incorporating change at a broad range of temporal, spatial and functional scales and is of broad relevance to a plethora of research questions. Yet, effectively measuring organismal development, and the ontogeny of physiological regulations and functions, and their responses to the environment, remains a significant challenge. "Phenomics", a global approach to the acquisition of phenotypic data at the scale of the whole organism, is uniquely suited as an approach. In this perspective, we explore the synergies between phenomics and Comparative Developmental Physiology (CDP), a discipline of increasing relevance to understanding sensitivity to drivers of global change. We then identify how organismal development itself provides an excellent model for pushing the boundaries of phenomics, given its inherent complexity, comparably smaller size, relative to adult stages, and the applicability of embryonic development to a broad suite of research questions using a diversity of species. Collection, analysis and interpretation of whole organismal phenotypic data are the largest obstacle to capitalising on phenomics for advancing our understanding of biological systems. We suggest that phenomics within the context of developing organismal form and function could provide an effective scaffold for addressing grand challenges in CDP and phenomics.
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Affiliation(s)
| | | | | | - Oliver Tills
- School of Biological and Marine Sciences, University of Plymouth, Plymouth, United Kingdom
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10
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Luo T, Liu X, Lakshmanan P. A Combined Genomics and Phenomics Approach is Needed to Boost Breeding in Sugarcane. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0074. [PMID: 37456081 PMCID: PMC10348406 DOI: 10.34133/plantphenomics.0074] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/08/2023] [Accepted: 06/29/2023] [Indexed: 07/18/2023]
Affiliation(s)
- Ting Luo
- Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Guangxi Key Laboratory of Sugarcane Genetic Improvement, Guangxi Academy of Agricultural Sciences, Nanning 530007, China
| | - Xiaoyan Liu
- Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Guangxi Key Laboratory of Sugarcane Genetic Improvement, Guangxi Academy of Agricultural Sciences, Nanning 530007, China
| | - Prakash Lakshmanan
- Key Laboratory of Sugarcane Biotechnology and Genetic Improvement (Guangxi), Ministry of Agriculture and Rural Affairs, Sugarcane Research Institute, Guangxi Academy of Agricultural Sciences, Guangxi Key Laboratory of Sugarcane Genetic Improvement, Guangxi Academy of Agricultural Sciences, Nanning 530007, China
- Interdisciplinary Research Center for Agriculture Green Development in Yangtze River Basin, College of Resources and Environment, Southwest University, Chongqing 400716, China
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, St Lucia 4067, QLD, Australia
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11
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Penne R, De Boi I, Vanlanduit S. On the Angular Control of Rotating Lasers by Means of Line Calculus on Hyperboloids. SENSORS (BASEL, SWITZERLAND) 2023; 23:6126. [PMID: 37447975 DOI: 10.3390/s23136126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/15/2023] [Revised: 06/26/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023]
Abstract
We propose a new paradigm for modelling and calibrating laser scanners with rotation symmetry, as is the case for lidars or for galvanometric laser systems with one or two rotating mirrors. Instead of bothering about the intrinsic parameters of a physical model, we use the geometric properties of the device to model it as a specific configuration of lines, which can be recovered by a line-data-driven procedure. Compared to universal data-driven methods that train general line models, our algebraic-geometric approach only requires a few measurements. We elaborate the case of a galvanometric laser scanner with two mirrors, that we model as a grid of hyperboloids represented by a grid of 3×3 lines. This provides a new type of look-up table, containing not more than nine elements, lines rather than points, where we replace the approximating interpolation with exact affine combinations of lines. The proposed method is validated in a realistic virtual setting. As a collateral contribution, we present a robust algorithm for fitting ruled surfaces of revolution on noisy line measurements.
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Affiliation(s)
- Rudi Penne
- InViLab, Faculty of Applied Engineering, University of Antwerp, 2000 Antwerp, Belgium
| | - Ivan De Boi
- InViLab, Faculty of Applied Engineering, University of Antwerp, 2000 Antwerp, Belgium
| | - Steve Vanlanduit
- InViLab, Faculty of Applied Engineering, University of Antwerp, 2000 Antwerp, Belgium
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12
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Bacha SAS, Iqbal B. Advancing agro-ecological sustainability through emerging genetic approaches in crop improvement for plants. Funct Integr Genomics 2023; 23:145. [PMID: 37133756 DOI: 10.1007/s10142-023-01074-4] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Revised: 04/24/2023] [Accepted: 04/26/2023] [Indexed: 05/04/2023]
Affiliation(s)
- Syed Asim Shah Bacha
- Institute of Pomology, Chinese Academy of Agricultural Sciences/Laboratory of Quality & Safety Risk Assessment for Fruit, Ministry of Agriculture and Rural Affairs, Xingcheng, 125100, China
| | - Babar Iqbal
- School of Environment and Safety Engineering, Jiangsu University, Zhenjiang, China.
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13
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Pineda M, Barón M. Assessment of Black Rot in Oilseed Rape Grown under Climate Change Conditions Using Biochemical Methods and Computer Vision. PLANTS (BASEL, SWITZERLAND) 2023; 12:1322. [PMID: 36987010 PMCID: PMC10058869 DOI: 10.3390/plants12061322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/09/2023] [Revised: 03/06/2023] [Accepted: 03/10/2023] [Indexed: 06/19/2023]
Abstract
Global warming is a challenge for plants and pathogens, involving profound changes in the physiology of both contenders to adapt to the new environmental conditions and to succeed in their interaction. Studies have been conducted on the behavior of oilseed rape plants and two races (1 and 4) of the bacterium Xanthomonas campestris pv. campestris (Xcc) and their interaction to anticipate our response in the possible future climate. Symptoms caused by both races of Xcc were very similar to each other under any climatic condition assayed, although the bacterial count from infected leaves differed for each race. Climate change caused an earlier onset of Xcc symptoms by at least 3 days, linked to oxidative stress and a change in pigment composition. Xcc infection aggravated the leaf senescence already induced by climate change. To identify Xcc-infected plants early under any climatic condition, four classifying algorithms were trained with parameters obtained from the images of green fluorescence, two vegetation indices and thermography recorded on Xcc-symptomless leaves. Classification accuracies were above 0.85 out of 1.0 in all cases, with k-nearest neighbor analysis and support vector machines performing best under the tested climatic conditions.
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14
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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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15
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Cooper M, Messina CD. Breeding crops for drought-affected environments and improved climate resilience. THE PLANT CELL 2023; 35:162-186. [PMID: 36370076 PMCID: PMC9806606 DOI: 10.1093/plcell/koac321] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Accepted: 11/01/2022] [Indexed: 05/12/2023]
Abstract
Breeding climate-resilient crops with improved levels of abiotic and biotic stress resistance as a response to climate change presents both opportunities and challenges. Applying the framework of the "breeder's equation," which is used to predict the response to selection for a breeding program cycle, we review methodologies and strategies that have been used to successfully breed crops with improved levels of drought resistance, where the target population of environments (TPEs) is a spatially and temporally heterogeneous mixture of drought-affected and favorable (water-sufficient) environments. Long-term improvement of temperate maize for the US corn belt is used as a case study and compared with progress for other crops and geographies. Integration of trait information across scales, from genomes to ecosystems, is needed to accurately predict yield outcomes for genotypes within the current and future TPEs. This will require transdisciplinary teams to explore, identify, and exploit novel opportunities to accelerate breeding program outcomes; both improved germplasm resources and improved products (cultivars, hybrids, clones, and populations) that outperform and replace the products in use by farmers, in combination with modified agronomic management strategies suited to their local environments.
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Affiliation(s)
| | - Carlos D Messina
- Horticultural Sciences Department, University of Florida, Gainesville, Florida 32611, USA
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16
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Daviet B, Fernandez R, Cabrera-Bosquet L, Pradal C, Fournier C. PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time. PLANT METHODS 2022; 18:130. [PMID: 36482291 PMCID: PMC9730636 DOI: 10.1186/s13007-022-00961-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 11/22/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND High-throughput phenotyping platforms allow the study of the form and function of a large number of genotypes subjected to different growing conditions (GxE). A number of image acquisition and processing pipelines have been developed to automate this process, for micro-plots in the field and for individual plants in controlled conditions. Capturing shoot development requires extracting from images both the evolution of the 3D plant architecture as a whole, and a temporal tracking of the growth of its organs. RESULTS We propose PhenoTrack3D, a new pipeline to extract a 3D + t reconstruction of maize. It allows the study of plant architecture and individual organ development over time during the entire growth cycle. The method tracks the development of each organ from a time-series of plants whose organs have already been segmented in 3D using existing methods, such as Phenomenal [Artzet et al. in BioRxiv 1:805739, 2019] which was chosen in this study. First, a novel stem detection method based on deep-learning is used to locate precisely the point of separation between ligulated and growing leaves. Second, a new and original multiple sequence alignment algorithm has been developed to perform the temporal tracking of ligulated leaves, which have a consistent geometry over time and an unambiguous topological position. Finally, growing leaves are back-tracked with a distance-based approach. This pipeline is validated on a challenging dataset of 60 maize hybrids imaged daily from emergence to maturity in the PhenoArch platform (ca. 250,000 images). Stem tip was precisely detected over time (RMSE < 2.1 cm). 97.7% and 85.3% of ligulated and growing leaves respectively were assigned to the correct rank after tracking, on 30 plants × 43 dates. The pipeline allowed to extract various development and architecture traits at organ level, with good correlation to manual observations overall, on random subsets of 10-355 plants. CONCLUSIONS We developed a novel phenotyping method based on sequence alignment and deep-learning. It allows to characterise the development of maize architecture at organ level, automatically and at a high-throughput. It has been validated on hundreds of plants during the entire development cycle, showing its applicability on GxE analyses of large maize datasets.
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Affiliation(s)
- Benoit Daviet
- LEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
| | - Romain Fernandez
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France
- CIRAD, INRAE, UMR AGAP Institut, Univ Montpellier, Institut Agro, 34398, Montpellier, France
| | | | - Christophe Pradal
- CIRAD, UMR AGAP Institut, 34398, Montpellier, France.
- CIRAD, INRAE, UMR AGAP Institut, Univ Montpellier, Institut Agro, 34398, Montpellier, France.
- Inria & LIRMM, CNRS, Univ Montpellier, Montpellier, France.
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17
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Yang W, Egea G, Ghamkhar K. Editorial: Convolutional neural networks and deep learning for crop improvement and production. FRONTIERS IN PLANT SCIENCE 2022; 13:1079148. [PMID: 36466228 PMCID: PMC9716429 DOI: 10.3389/fpls.2022.1079148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Accepted: 11/04/2022] [Indexed: 06/17/2023]
Affiliation(s)
- Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, National Center of Plant Gene Research, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Gregorio Egea
- Area of Agroforestry Engineering, School of Agricultural Engineering, University of Seville, Seville, Spain
| | - Kioumars Ghamkhar
- Margot Forde Germplasm Centre, Grasslands Research Centre, AgResearch, Palmerston North, New Zealand
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18
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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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19
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Sun G, Lu H, Zhao Y, Zhou J, Jackson R, Wang Y, Xu L, Wang A, Colmer J, Ober E, Zhao Q, Han B, Zhou J. AirMeasurer: open-source software to quantify static and dynamic traits derived from multiseason aerial phenotyping to empower genetic mapping studies in rice. THE NEW PHYTOLOGIST 2022; 236:1584-1604. [PMID: 35901246 PMCID: PMC9796158 DOI: 10.1111/nph.18314] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/17/2022] [Accepted: 05/31/2022] [Indexed: 05/11/2023]
Abstract
Low-altitude aerial imaging, an approach that can collect large-scale plant imagery, has grown in popularity recently. Amongst many phenotyping approaches, unmanned aerial vehicles (UAVs) possess unique advantages as a consequence of their mobility, flexibility and affordability. Nevertheless, how to extract biologically relevant information effectively has remained challenging. Here, we present AirMeasurer, an open-source and expandable platform that combines automated image analysis, machine learning and original algorithms to perform trait analysis using 2D/3D aerial imagery acquired by low-cost UAVs in rice (Oryza sativa) trials. We applied the platform to study hundreds of rice landraces and recombinant inbred lines at two sites, from 2019 to 2021. A range of static and dynamic traits were quantified, including crop height, canopy coverage, vegetative indices and their growth rates. After verifying the reliability of AirMeasurer-derived traits, we identified genetic variants associated with selected growth-related traits using genome-wide association study and quantitative trait loci mapping. We found that the AirMeasurer-derived traits had led to reliable loci, some matched with published work, and others helped us to explore new candidate genes. Hence, we believe that our work demonstrates valuable advances in aerial phenotyping and automated 2D/3D trait analysis, providing high-quality phenotypic information to empower genetic mapping for crop improvement.
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Affiliation(s)
- Gang Sun
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
| | - Hengyun Lu
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Yan Zhao
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Jie Zhou
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
| | - Robert Jackson
- Cambridge Crop ResearchNational Institute of Agricultural Botany (NIAB)CambridgeCB3 0LEUK
| | - Yongchun Wang
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Ling‐xiang Xu
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
| | - Ahong Wang
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Joshua Colmer
- Earlham InstituteNorwich Research ParkNorwichNR4 7UHUK
| | - Eric Ober
- Cambridge Crop ResearchNational Institute of Agricultural Botany (NIAB)CambridgeCB3 0LEUK
| | - Qiang Zhao
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Bin Han
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant SciencesChinese Academy of SciencesShanghai200233China
| | - Ji Zhou
- State Key Laboratory of Crop Genetics & Germplasm Enhancement, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co‐sponsored by Province and MinistryNanjing Agricultural UniversityNanjing210095China
- Cambridge Crop ResearchNational Institute of Agricultural Botany (NIAB)CambridgeCB3 0LEUK
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20
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Weksler S, Rozenstein O, Ben Dor E. Continuous seasonal monitoring of nitrogen and water content in lettuce using a dual phenomics system. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5294-5305. [PMID: 34958347 DOI: 10.1093/jxb/erab561] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 12/23/2021] [Indexed: 06/14/2023]
Abstract
The collection and analysis of large amounts of information on a plant-by-plant basis contributes to the development of precision fertigation and may be achieved by combining remote-sensing technology with high-throughput phenotyping methods. Here, lettuce plants (Lactuca sativa) were grown under optimal and suboptimal nitrogen and irrigation treatments from seedlings to harvest. A Plantarray system was used to calculate and log weights, daily transpiration, and momentary transpiration rates throughout the experiment. From 15 d after planting until experiment termination, the entire array of plants was imaged hourly (from 09.00 h to 14.00 h) using a hyperspectral moving camera. Three vegetation indices were calculated from the plants' reflectance signal: red-edge chlorophyll index (RECI), photochemical reflectance index (PRI), and water index (WI), and combined treatments, physiological measurements, and vegetation indices were compared. RECI values differed significantly between nitrogen treatments from the first day of imaging, and WI values distinguished well-irrigated from drought-treated groups before detecting significant differences in daily transpiration rate. The PRI, calculated hourly during the drought-treatment phase, changed with the momentary transpiration rate. Thus, hyperspectral imaging might be used in growing facilities to detect nitrogen or water shortages in plants before their physiological response affects yields.
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Affiliation(s)
- Shahar Weksler
- Porter School of Environment and Earth Sciences, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
- Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization-Volcani Institute, HaMaccabim Road 68, P.O.B 15159, Rishon LeZion 7528809, Israel
| | - Offer Rozenstein
- Institute of Soil, Water and Environmental Sciences, Agricultural Research Organization-Volcani Institute, HaMaccabim Road 68, P.O.B 15159, Rishon LeZion 7528809, Israel
| | - Eyal Ben Dor
- Porter School of Environment and Earth Sciences, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 6997801, Israel
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21
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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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22
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Langan P, Bernád V, Walsh J, Henchy J, Khodaeiaminjan M, Mangina E, Negrão S. Phenotyping for waterlogging tolerance in crops: current trends and future prospects. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5149-5169. [PMID: 35642593 PMCID: PMC9440438 DOI: 10.1093/jxb/erac243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 05/30/2022] [Indexed: 06/15/2023]
Abstract
Yield losses to waterlogging are expected to become an increasingly costly and frequent issue in some regions of the world. Despite the extensive work that has been carried out examining the molecular and physiological responses to waterlogging, phenotyping for waterlogging tolerance has proven difficult. This difficulty is largely due to the high variability of waterlogging conditions such as duration, temperature, soil type, and growth stage of the crop. In this review, we highlight use of phenotyping to assess and improve waterlogging tolerance in temperate crop species. We start by outlining the experimental methods that have been utilized to impose waterlogging stress, ranging from highly controlled conditions of hydroponic systems to large-scale screenings in the field. We also describe the phenotyping traits used to assess tolerance ranging from survival rates and visual scoring to precise photosynthetic measurements. Finally, we present an overview of the challenges faced in attempting to improve waterlogging tolerance, the trade-offs associated with phenotyping in controlled conditions, limitations of classic phenotyping methods, and future trends using plant-imaging methods. If effectively utilized to increase crop resilience to changing climates, crop phenotyping has a major role to play in global food security.
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Affiliation(s)
- Patrick Langan
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Villő Bernád
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | - Jason Walsh
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
- School of Computer Science and UCD Energy Institute, University College Dublin, Dublin, Ireland
| | - Joey Henchy
- School of Biology and Environmental Science, University College Dublin, Dublin, Ireland
| | | | - Eleni Mangina
- School of Computer Science and UCD Energy Institute, University College Dublin, Dublin, Ireland
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Roitsch T, Himanen K, Chawade A, Jaakola L, Nehe A, Alexandersson E. Functional phenomics for improved climate resilience in Nordic agriculture. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5111-5127. [PMID: 35727101 PMCID: PMC9440434 DOI: 10.1093/jxb/erac246] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Accepted: 06/06/2022] [Indexed: 05/26/2023]
Abstract
The five Nordic countries span the most northern region for field cultivation in the world. This presents challenges per se, with short growing seasons, long days, and a need for frost tolerance. Climate change has additionally increased risks for micro-droughts and water logging, as well as pathogens and pests expanding northwards. Thus, Nordic agriculture demands crops that are adapted to the specific Nordic growth conditions and future climate scenarios. A focus on crop varieties and traits important to Nordic agriculture, including the unique resource of nutritious wild crops, can meet these needs. In fact, with a future longer growing season due to climate change, the region could contribute proportionally more to global agricultural production. This also applies to other northern regions, including the Arctic. To address current growth conditions, mitigate impacts of climate change, and meet market demands, the adaptive capacity of crops that both perform well in northern latitudes and are more climate resilient has to be increased, and better crop management systems need to be built. This requires functional phenomics approaches that integrate versatile high-throughput phenotyping, physiology, and bioinformatics. This review stresses key target traits, the opportunities of latitudinal studies, and infrastructure needs for phenotyping to support Nordic agriculture.
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Affiliation(s)
- Thomas Roitsch
- Department of Plant and Environmental Sciences, University of Copenhagen, Denmark
- Department of Adaptive Biotechnologies, Global Change Research Institute, Czech Academy of Sciences, Brno, Czechia
| | - Kristiina Himanen
- National Plant Phenotyping Infrastructure, HiLIFE, University of Helsinki, Finland
- Organismal and Evolutionary Biology Research Program, Viikki Plant Science Centre, Faculty of Biological and Environmental Sciences, University of Helsinki, Finland
| | - Aakash Chawade
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Lomma, Sweden
| | - Laura Jaakola
- Climate laboratory Holt, Department of Arctic and Marine Biology, UiT the Arctic University of Norway, Tromsø, Norway
- NIBIO, Norwegian Institute of Bioeconomy Research, Ås, Norway
| | - Ajit Nehe
- Department of Plant Breeding, Swedish University of Agricultural Sciences, Lomma, Sweden
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24
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Nguyen PT, Shi F, Wang J, Badenhorst PE, Spangenberg GC, Smith KF, Daetwyler HD. Within and combined season prediction models for perennial ryegrass biomass yield using ground- and air-based sensor data. FRONTIERS IN PLANT SCIENCE 2022; 13:950720. [PMID: 36003811 PMCID: PMC9393552 DOI: 10.3389/fpls.2022.950720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/06/2022] [Indexed: 06/15/2023]
Abstract
Across-season biomass assessment is crucial in the cultivar selection process to accurately evaluate the yield performance of lines under different growing conditions. However, it has been difficult to have an accurate, reliable, and repeated fresh biomass (FM) estimation of large populations of plants in the field without destructive harvesting, which incurs significant labor and operation costs. Sensor-based phenotyping platforms have advanced in the data collection of structural and vegetative information of plants, but the developed prediction models are still limited by low correlations at different growth stages and seasons. In this study, our objective was to develop and validate the global prediction models for across-season harvested fresh biomass (FM) yield based on the ground- and air-based sensor data including ground-based LiDAR, ground-based ultrasonic, and air-based multispectral camera to extract LiDAR plant volume (LV), LiDAR point density (LV_Den), height, and Normalized Difference Vegetative Index (NDVI). The study was conducted in a row-plot field trial with 480 rows (3 rows in a plot per cultivar) throughout the whole 2020 growing season up to the reproductive stage. We evaluated the performance of each plant parameter, their relationship, and the best subset prediction models using statistical stepwise selection at the row and plot levels through the seasonal and combined seasonal datasets. The best performing model: F M ~ L V ∗ L V _ D e n ∗ N D V I had a determination of coefficient R 2 of at least 0.9 in vegetative stages and 0.8 in the reproductive stage. Similar results can be achieved in a simpler model with just two LiDAR variables- F M ~ L V ∗ L V _ D e n . In addition, LV and LV_Den showed a robust correlation with FM on their own over seasons and growth stages, while NDVI only performed well in some seasons. The simpler model based on only LiDAR data can be widely applied over season without the need of additional sensor data and may thus make the in-field across-season biomass assessment more feasible and practical for fast and cost-effective development of higher biomass yield cultivars.
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Affiliation(s)
- Phat T. Nguyen
- School of Applied System Biology, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Fan Shi
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Junping Wang
- Agriculture Victoria, Hamilton Centre, Hamilton, VIC, Australia
| | | | - German C. Spangenberg
- School of Applied System Biology, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
| | - Kevin F. Smith
- Agriculture Victoria, Hamilton Centre, Hamilton, VIC, Australia
- Faculty of Veterinary and Agricultural Sciences, School of Agriculture and Food, The University of Melbourne, Melbourne, VIC, Australia
| | - Hans D. Daetwyler
- School of Applied System Biology, La Trobe University, Bundoora, VIC, Australia
- Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC, Australia
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25
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Yu S, Fan J, Lu X, Wen W, Shao S, Guo X, Zhao C. Hyperspectral Technique Combined With Deep Learning Algorithm for Prediction of Phenotyping Traits in Lettuce. FRONTIERS IN PLANT SCIENCE 2022; 13:927832. [PMID: 35845657 PMCID: PMC9279906 DOI: 10.3389/fpls.2022.927832] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2022] [Accepted: 06/13/2022] [Indexed: 06/15/2023]
Abstract
The currently available methods for evaluating most biochemical traits of plant phenotyping are destructive and have extremely low throughput. However, hyperspectral techniques can non-destructively obtain the spectral reflectance characteristics of plants, which can provide abundant biophysical and biochemical information. Therefore, plant spectra combined with machine learning algorithms can be used to predict plant phenotyping traits. However, the raw spectral reflectance characteristics contain noise and redundant information, thus can easily affect the robustness of the models developed via multivariate analysis methods. In this study, two end-to-end deep learning models were developed based on 2D convolutional neural networks (2DCNN) and fully connected neural networks (FCNN; Deep2D and DeepFC, respectively) to rapidly and non-destructively predict the phenotyping traits of lettuces from spectral reflectance. Three linear and two nonlinear multivariate analysis methods were used to develop models to weigh the performance of the deep learning models. The models based on multivariate analysis methods require a series of manual feature extractions, such as pretreatment and wavelength selection, while the proposed models can automatically extract the features in relation to phenotyping traits. A visible near-infrared hyperspectral camera was used to image lettuce plants growing in the field, and the spectra extracted from the images were used to train the network. The proposed models achieved good performance with a determination coefficient of prediction ( R p 2 ) of 0.9030 and 0.8490 using Deep2D for soluble solids content and DeepFC for pH, respectively. The performance of the deep learning models was compared with five multivariate analysis method. The quantitative analysis showed that the deep learning models had higher R p 2 than all the multivariate analysis methods, indicating better performance. Also, wavelength selection and different pretreatment methods had different effects on different multivariate analysis methods, and the selection of appropriate multivariate analysis methods and pretreatment methods increased more time and computational cost. Unlike multivariate analysis methods, the proposed deep learning models did not require any pretreatment or dimensionality reduction and thus are more suitable for application in high-throughput plant phenotyping platforms. These results indicate that the deep learning models can better predict phenotyping traits of plants using spectral reflectance.
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Affiliation(s)
- Shuan Yu
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Jiangchuan Fan
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xianju Lu
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Weiliang Wen
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Song Shao
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Xinyu Guo
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
| | - Chunjiang Zhao
- National Engineering Research Center for Agro-Ecological Big Data Analysis and Application, Anhui University, Hefei, China
- Beijing Key Laboratory of Digital Plant, China National Engineering Research Center for Information Technology in Agriculture, Beijing, China
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26
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Zhang Q, Zhang Q, Jensen J. Association Studies and Genomic Prediction for Genetic Improvements in Agriculture. FRONTIERS IN PLANT SCIENCE 2022; 13:904230. [PMID: 35720549 PMCID: PMC9201771 DOI: 10.3389/fpls.2022.904230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 05/16/2022] [Indexed: 06/15/2023]
Abstract
To feed the fast growing global population with sufficient food using limited global resources, it is urgent to develop and utilize cutting-edge technologies and improve efficiency of agricultural production. In this review, we specifically introduce the concepts, theories, methods, applications and future implications of association studies and predicting unknown genetic value or future phenotypic events using genomics in the area of breeding in agriculture. Genome wide association studies can identify the quantitative genetic loci associated with phenotypes of importance in agriculture, while genomic prediction utilizes individual genetic value to rank selection candidates to improve the next generation of plants or animals. These technologies and methods have improved the efficiency of genetic improvement programs for agricultural production via elite animal breeds and plant varieties. With the development of new data acquisition technologies, there will be more and more data collected from high-through-put technologies to assist agricultural breeding. It will be crucial to extract useful information among these large amounts of data and to face this challenge, more efficient algorithms need to be developed and utilized for analyzing these data. Such development will require knowledge from multiple disciplines of research.
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Affiliation(s)
- Qianqian Zhang
- Institute of Biotechnology, Beijing Academy of Agricultural and Forestry Sciences, Beijing, China
| | - Qin Zhang
- College of Animal Science and Technology, Shandong Agricultural University, Taian, China
- College of Animal Science and Technology, China Agricultural University, BeijingChina
| | - Just Jensen
- Centre for Quantitative Genetics and Genomics, Aarhus University, Aarhus, Denmark
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27
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Gill T, Gill SK, Saini DK, Chopra Y, de Koff JP, Sandhu KS. A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping. PHENOMICS (CHAM, SWITZERLAND) 2022; 2:156-183. [PMID: 36939773 PMCID: PMC9590503 DOI: 10.1007/s43657-022-00048-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/29/2022] [Accepted: 02/11/2022] [Indexed: 02/04/2023]
Abstract
During the last decade, there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant. High throughput phenotyping (HTP) involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing. Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain. More specifically, several root phenotyping tools are discussed to study the plant's hidden half and an area long neglected. However, the use of these HTP technologies produces big data sets that impede the inference from those datasets. Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions. These are interdisciplinary approaches for data analysis using probability, statistics, classification, regression, decision theory, data visualization, and neural networks to relate information extracted with the phenotypes obtained. These techniques use feature extraction, identification, classification, and prediction criteria to identify pertinent data for use in plant breeding and pathology activities. This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms. We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls. Overall, this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions. Finally, we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.
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Affiliation(s)
- Taqdeer Gill
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Simranveer K. Gill
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Dinesh K. Saini
- grid.412577.20000 0001 2176 2352Department of Plant Breeding and Genetics, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Yuvraj Chopra
- grid.412577.20000 0001 2176 2352College of Agriculture, Punjab Agricultural University, Ludhiana, Punjab 141004 India
| | - Jason P. de Koff
- grid.280741.80000 0001 2284 9820Department of Agricultural and Environmental Sciences, Tennessee State University, Nashville, TN 37209 USA
| | - Karansher S. Sandhu
- grid.30064.310000 0001 2157 6568Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99163 USA
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28
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Hall RD, D'Auria JC, Silva Ferreira AC, Gibon Y, Kruszka D, Mishra P, van de Zedde R. High-throughput plant phenotyping: a role for metabolomics? TRENDS IN PLANT SCIENCE 2022; 27:549-563. [PMID: 35248492 DOI: 10.1016/j.tplants.2022.02.001] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/06/2021] [Revised: 01/18/2022] [Accepted: 02/02/2022] [Indexed: 05/17/2023]
Abstract
High-throughput (HTP) plant phenotyping approaches are developing rapidly and are already helping to bridge the genotype-phenotype gap. However, technologies should be developed beyond current physico-spectral evaluations to extend our analytical capacities to the subcellular level. Metabolites define and determine many key physiological and agronomic features in plants and an ability to integrate a metabolomics approach within current HTP phenotyping platforms has huge potential for added value. While key challenges remain on several fronts, novel technological innovations are upcoming yet under-exploited in a phenotyping context. In this review, we present an overview of the state of the art and how current limitations might be overcome to enable full integration of metabolomics approaches into a generic phenotyping pipeline in the near future.
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Affiliation(s)
- Robert D Hall
- BU Bioscience, Wageningen University & Research, 6700 AA, Wageningen, The Netherlands; Laboratory of Plant Physiology, Wageningen University, 6700 AA, Wageningen, The Netherlands; Netherlands Metabolomics Centre, Einsteinweg 55, Leiden, The Netherlands.
| | - John C D'Auria
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK Gatersleben), Gatersleben, Corrensstraße 3, 06466 Seeland, Germany
| | - Antonio C Silva Ferreira
- Universidade Católica Portuguesa, CBQF-Centro de Biotecnologia e Química Fina-Laboratório Associado, Escola Superior de Biotecnologia, Rua Arquiteto Lobão Vital, Apartado 2511, 4202-401 Porto, Portugal; Faculty of AgriSciences, University of Stellenbosch, Matieland 7602, South Africa; Cork Supply Portugal, S.A., Rua Nova do Fial, 4535, Portugal
| | - Yves Gibon
- UMR 1332 Biologie du Fruit et Pathologie, INRAE, Univ. Bordeaux, INRAE Nouvelle Aquitaine - Bordeaux, Avenue Edouard Bourlaux, Villenave d'Ornon, France; Bordeaux Metabolome, MetaboHUB, INRAE, Univ. Bordeaux, Avenue Edouard Bourlaux, Villenave d'Ornon, France PMB-Metabolome, INRAE, Centre INRAE de Nouvelle, Aquitaine-Bordeaux, Villenave d'Ornon, France
| | - Dariusz Kruszka
- Institute of Plant Genetics, Polish Academy of Sciences, 60-479 Poznan, Poland
| | - Puneet Mishra
- Food and Biobased Research, Wageningen University & Research, 6708 WE, Wageningen, The Netherlands
| | - Rick van de Zedde
- Plant Sciences Group, Wageningen University & Research, 6700 AA, Wageningen, The Netherlands
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A Review of Integrative Omic Approaches for Understanding Rice Salt Response Mechanisms. PLANTS 2022; 11:plants11111430. [PMID: 35684203 PMCID: PMC9182744 DOI: 10.3390/plants11111430] [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: 04/04/2022] [Revised: 05/20/2022] [Accepted: 05/24/2022] [Indexed: 01/04/2023]
Abstract
Soil salinity is one of the most serious environmental challenges, posing a growing threat to agriculture across the world. Soil salinity has a significant impact on rice growth, development, and production. Hence, improving rice varieties’ resistance to salt stress is a viable solution for meeting global food demand. Adaptation to salt stress is a multifaceted process that involves interacting physiological traits, biochemical or metabolic pathways, and molecular mechanisms. The integration of multi-omics approaches contributes to a better understanding of molecular mechanisms as well as the improvement of salt-resistant and tolerant rice varieties. Firstly, we present a thorough review of current knowledge about salt stress effects on rice and mechanisms behind rice salt tolerance and salt stress signalling. This review focuses on the use of multi-omics approaches to improve next-generation rice breeding for salinity resistance and tolerance, including genomics, transcriptomics, proteomics, metabolomics and phenomics. Integrating multi-omics data effectively is critical to gaining a more comprehensive and in-depth understanding of the molecular pathways, enzyme activity and interacting networks of genes controlling salinity tolerance in rice. The key data mining strategies within the artificial intelligence to analyse big and complex data sets that will allow more accurate prediction of outcomes and modernise traditional breeding programmes and also expedite precision rice breeding such as genetic engineering and genome editing.
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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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Zhang H, Ge Y, Xie X, Atefi A, Wijewardane NK, Thapa S. High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion. PLANT METHODS 2022; 18:60. [PMID: 35505350 PMCID: PMC9063379 DOI: 10.1186/s13007-022-00892-0] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 04/15/2022] [Indexed: 05/25/2023]
Abstract
BACKGROUND Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. RESULTS The models with a single color feature from RGB images predicted chlorophyll content with R2 ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R2 ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R2 of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R2 of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. CONCLUSION All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum.
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Affiliation(s)
- Huichun Zhang
- College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing, 210037, China.
- Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, Nanjing Forestry University, Nanjing, 210037, China.
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, 68583, USA.
| | - Yufeng Ge
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, 68583, USA
| | - Xinyan Xie
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, 68583, USA
| | - Abbas Atefi
- Department of Biological Systems Engineering, University of Nebraska-Lincoln, Lincoln, NE, 68583, USA
| | - Nuwan K Wijewardane
- Department of Agricultural and Biological Engineering, Mississippi State University, Starkville, MS, USA
| | - Suresh Thapa
- Department of Computer Science and Engineering, University of Nebraska-Lincoln, Lincoln, NE, 68583, USA
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32
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Assessment of kernel presence in winter wheat ears at spikelet scale using near-infrared hyperspectral imaging. J Cereal Sci 2022. [DOI: 10.1016/j.jcs.2022.103497] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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33
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Roles of Selective Agriculture Practices in Sustainable Agricultural Performance: A Systematic Review. SUSTAINABILITY 2022. [DOI: 10.3390/su14063185] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Feeding the growing global population while improving the Earth’s economic, environmental, and social values is a challenge recognised in both the United Nations Sustainable Development Goals and the United Nations Framework Convention on Climate Change. Sustaining global agricultural performance requires regular revision of current farming models, attitudes, and practices. In systematically reviewing the international literature through the lens of the sustainability framework, this paper specifically identifies precision conservation agriculture (PCA), digital agriculture (DA), and resilient agriculture (RA) practices as being of value in meeting future challenges. Each of these adaptations carries significantly positive relationships with sustaining agricultural performance, as well as positively mediating and/or moderating each other. While it is clear from the literature that adopting PCA, DA, and RA would substantially improve the sustainability of agricultural performance, the uptake of these adaptations generally lags. More in-depth social science research is required to understand the value propositions that would encourage uptake of these adaptations and the barriers that prevent them. Recommendations are made to explore the specific knowledge gap that needs to be understood to motivate agriculture practitioners to adopt these changes in practice.
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Ninomiya S. High-throughput field crop phenotyping: current status and challenges. BREEDING SCIENCE 2022; 72:3-18. [PMID: 36045897 PMCID: PMC8987842 DOI: 10.1270/jsbbs.21069] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 12/16/2021] [Indexed: 05/03/2023]
Abstract
In contrast to the rapid advances made in plant genotyping, plant phenotyping is considered a bottleneck in plant science. This has promoted high-throughput plant phenotyping (HTP) studies, resulting in an exponential increase in phenotyping-related publications. The development of HTP was originally intended for use as indoor HTP technologies for model plant species under controlled environments. However, this subsequently shifted to HTP for use in crops in fields. Although HTP in fields is much more difficult to conduct due to unstable environmental conditions compared to HTP in controlled environments, recent advances in HTP technology have allowed these difficulties to be overcome, allowing for rapid, efficient, non-destructive, non-invasive, quantitative, repeatable, and objective phenotyping. Recent HTP developments have been accelerated by the advances in data analysis, sensors, and robot technologies, including machine learning, image analysis, three dimensional (3D) reconstruction, image sensors, laser sensors, environmental sensors, and drones, along with high-speed computational resources. This article provides an overview of recent HTP technologies, focusing mainly on canopy-based phenotypes of major crops, such as canopy height, canopy coverage, canopy biomass, and canopy stressed appearance, in addition to crop organ detection and counting in the fields. Current topics in field HTP are also presented, followed by a discussion on the low rates of adoption of HTP in practical breeding programs.
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Affiliation(s)
- Seishi Ninomiya
- Graduate School of Agriculture and Life Sciences, The University of Tokyo, Nishitokyo, Tokyo 188-0002, Japan
- Plant Phenomics Research Center, Nanjing Agricultural University, Nanjing, China
- Corresponding author (e-mail: )
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35
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Sun D, Robbins K, Morales N, Shu Q, Cen H. Advances in optical phenotyping of cereal crops. TRENDS IN PLANT SCIENCE 2022; 27:191-208. [PMID: 34417079 DOI: 10.1016/j.tplants.2021.07.015] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/12/2021] [Revised: 07/22/2021] [Accepted: 07/24/2021] [Indexed: 06/13/2023]
Abstract
Optical sensors and sensing-based phenotyping techniques have become mainstream approaches in high-throughput phenotyping for improving trait selection and genetic gains in crops. We review recent progress and contemporary applications of optical sensing-based phenotyping (OSP) techniques in cereal crops and highlight optical sensing principles for spectral response and sensor specifications. Further, we group phenotypic traits determined by OSP into four categories - morphological, biochemical, physiological, and performance traits - and illustrate appropriate sensors for each extraction. In addition to the current status, we discuss the challenges of OSP and provide possible solutions. We propose that optical sensing-based traits need to be explored further, and that standardization of the language of phenotyping and worldwide collaboration between phenotyping researchers and other fields need to be established.
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Affiliation(s)
- Dawei Sun
- College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China
| | - Kelly Robbins
- Section of Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Nicolas Morales
- Section of Plant Breeding and Genetics, School of Integrative Plant Science, Cornell University, Ithaca, NY 14853, USA
| | - Qingyao Shu
- Zhejiang Provincial Key Laboratory of Crop Genetic Resources, Institute of Crop Science, Zhejiang University, Hangzhou, PR China; State Key Laboratory of Rice Biology, Zhejiang University, Hangzhou 310058, PR China
| | - Haiyan Cen
- College of Biosystems Engineering and Food Science, and State Key Laboratory of Modern Optical Instrumentation, Zhejiang University, Hangzhou 310058, PR China; Key Laboratory of Spectroscopy Sensing, Ministry of Agriculture and Rural Affairs, Hangzhou 310058, PR China.
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Stephan T, Burgess SM, Cheng H, Danko CG, Gill CA, Jarvis ED, Koepfli KP, Koltes JE, Lyons E, Ronald P, Ryder OA, Schriml LM, Soltis P, VandeWoude S, Zhou H, Ostrander EA, Karlsson EK. Darwinian genomics and diversity in the tree of life. Proc Natl Acad Sci U S A 2022; 119:e2115644119. [PMID: 35042807 PMCID: PMC8795533 DOI: 10.1073/pnas.2115644119] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022] Open
Abstract
Genomics encompasses the entire tree of life, both extinct and extant, and the evolutionary processes that shape this diversity. To date, genomic research has focused on humans, a small number of agricultural species, and established laboratory models. Fewer than 18,000 of ∼2,000,000 eukaryotic species (<1%) have a representative genome sequence in GenBank, and only a fraction of these have ancillary information on genome structure, genetic variation, gene expression, epigenetic modifications, and population diversity. This imbalance reflects a perception that human studies are paramount in disease research. Yet understanding how genomes work, and how genetic variation shapes phenotypes, requires a broad view that embraces the vast diversity of life. We have the technology to collect massive and exquisitely detailed datasets about the world, but expertise is siloed into distinct fields. A new approach, integrating comparative genomics with cell and evolutionary biology, ecology, archaeology, anthropology, and conservation biology, is essential for understanding and protecting ourselves and our world. Here, we describe potential for scientific discovery when comparative genomics works in close collaboration with a broad range of fields as well as the technical, scientific, and social constraints that must be addressed.
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Affiliation(s)
- Taylorlyn Stephan
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20817
| | - Shawn M Burgess
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20817
| | - Hans Cheng
- Avian Disease and Oncology Laboratory, Agricultural Research Service, US Department of Agriculture, East Lansing, MI 48823
| | - Charles G Danko
- Department of Biomedical Sciences, Baker Institute for Animal Health, Cornell University, Ithaca, NY 14850
| | - Clare A Gill
- Department of Animal Science, Texas A&M University, College Station, TX 77843
| | - Erich D Jarvis
- Laboratory of Neurogenetics of Language, The Rockefeller University, New York, NY 10065
- HHMI, Chevy Chase, MD 20815
| | - Klaus-Peter Koepfli
- Smithsonian-Mason School of Conservation, George Mason University, Front Royal, VA 22630
- Smithsonian Conservation Biology Institute, National Zoological Park, Washington, DC 20008
| | - James E Koltes
- Department of Animal Science, Iowa State University, Ames, IA 50011
| | - Eric Lyons
- School of Plant Sciences, BIO5 Institute, University of Arizona, Tucson, AZ 85721
| | - Pamela Ronald
- Department of Plant Pathology, University of California, Davis, CA 95616
- The Genome Center, University of California, Davis, CA 95616
- The Innovative Genomics Institute, University of California, Berkeley, CA 94720
- Grass Genetics, Joint Bioenergy Institute, Emeryville, CA 94608
| | - Oliver A Ryder
- San Diego Zoo Wildlife Alliance, Escondido, CA 92027
- Department of Evolution, Behavior, and Ecology, University of California San Diego, La Jolla, CA 92093
| | - Lynn M Schriml
- Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201
| | - Pamela Soltis
- Florida Museum of Natural History, University of Florida, Gainesville, FL 32611
| | - Sue VandeWoude
- Department of Micro-, Immuno-, and Pathology, Colorado State University, Fort Collins, CO 80532
| | - Huaijun Zhou
- Department of Animal Science, University of California, Davis, CA 95616
| | - Elaine A Ostrander
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20817
| | - Elinor K Karlsson
- Bioinformatics and Integrative Biology, University of Massachusetts Medical School, Worcester, MA 01655;
- Program in Molecular Medicine, University of Massachusetts Medical School, Worcester, MA 01655
- Broad Institute of MIT and Harvard, Cambridge, MA 02142
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Common Latent Space Exploration for Calibration Transfer across Hyperspectral Imaging-Based Phenotyping Systems. REMOTE SENSING 2022. [DOI: 10.3390/rs14020319] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/04/2023]
Abstract
Hyperspectral imaging has increasingly been used in high-throughput plant phenotyping systems. Rapid advancement in the field of phenotyping has resulted in a wide array of hyperspectral imaging systems. However, sharing the plant feature prediction models between different phenotyping facilities becomes challenging due to the differences in imaging environments and imaging sensors. Calibration transfer between imaging facilities is crucially important to cope with such changes. Spectral space adjustment methods including direct standardization (DS), its variants (PDS, DPDS) and spectral scale transformation (SST) require the standard samples to be imaged in different facilities. However, in real-world scenarios, imaging the standard samples is practically unattractive. Therefore, in this study, we presented three methods (TCA, c-PCA, and di-PLSR) to transfer the calibration models without requiring the standard samples. In order to compare the performance of proposed approaches, maize plants were imaged in two greenhouse-based HTPP systems using two pushbroom-style hyperspectral cameras covering the visible near-infrared range. We tested the proposed methods to transfer nitrogen content (N) and relative water content (RWC) calibration models. The results showed that prediction R2 increased by up to 14.50% and 42.20%, while the reduction in RMSEv was up to 74.49% and 76.72% for RWC and N, respectively. The di-PLSR achieved the best results for almost all the datasets included in this study, with TCA being second. The performance of c-PCA was not at par with the di-PLSR and TCA. Our results showed that the di-PLSR helped to recover the performance of RWC, and N models plummeted due to the differences originating from new imaging systems (sensor type, spectrograph, lens system, spatial resolution, spectral resolution, field of view, bit-depth, frame rate, and exposure time) or lighting conditions. The proposed approaches can alleviate the requirement of developing a new calibration model for a new phenotyping facility or to resort to the spectral space adjustment using the standard samples.
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Mishra P. Chemometric approaches for calibrating high-throughput spectral imaging setups to support digital plant phenotyping by calibrating and transferring spectral models from a point spectrometer. Anal Chim Acta 2021; 1187:339154. [PMID: 34753582 DOI: 10.1016/j.aca.2021.339154] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2021] [Revised: 09/27/2021] [Accepted: 10/05/2021] [Indexed: 01/13/2023]
Abstract
Visible and near-infrared (Vis-NIR) spectral imaging is appearing as a potential tool to support high-throughput digital agricultural plant phenotyping. One of the uses of spectral imaging is to predict non-destructively the chemical constituents in the plants such as nitrogen content which can be related to the functional status of plants. However, before using high-throughput spectral imaging, it requires extensive calibration, just as needed for any other spectral sensor. Calibrating the high-throughput spectral imaging setup can be a challenging task as the resources needed to run experiments in high-throughput setups are far more than performing measurements with point spectrometers. Hence, to supply a resource-efficient approach to calibrate spectral cameras integrated with high-throughput plant phenotyping setups, this study proposes the use of chemometric calibration transfer (CT) and model update. The main idea was to use a point spectrometer to develop the primary model and transfer it to the spectral cameras integrated into the high-throughput setups. The potential of the approach was showed using a real Vis-NIR dataset related to nitrogen prediction in wheat plants measured with point spectrometer, tabletop spectral cameras and spectral cameras integrated with a high-throughput plant phenotyping setup. For CT and model update, direct standardization and parameter-free calibration enhancement approaches were explored. A key aim of this study was to only use and compare techniques that does not require any further optimization as they can be easily implemented by the plant biologist in future applications. The proposed approach based on the transfer of point spectroscopy models to spectral cameras in a high-throughput setup can allow spectral calibrations to be sharable and widely applicable, thus helping the global digital plant phenotyping community.
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Affiliation(s)
- Puneet Mishra
- Wageningen University & Research, Food and Biobased Research, Bornse Weilanden 9, P.O. Box 17, 6700AA, Wageningen, the Netherlands.
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39
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Carvalho LC, Gonçalves EF, Marques da Silva J, Costa JM. Potential Phenotyping Methodologies to Assess Inter- and Intravarietal Variability and to Select Grapevine Genotypes Tolerant to Abiotic Stress. FRONTIERS IN PLANT SCIENCE 2021; 12:718202. [PMID: 34764964 PMCID: PMC8575754 DOI: 10.3389/fpls.2021.718202] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/31/2021] [Accepted: 09/28/2021] [Indexed: 06/12/2023]
Abstract
Plant phenotyping is an emerging science that combines multiple methodologies and protocols to measure plant traits (e.g., growth, morphology, architecture, function, and composition) at multiple scales of organization. Manual phenotyping remains as a major bottleneck to the advance of plant and crop breeding. Such constraint fostered the development of high throughput plant phenotyping (HTPP), which is largely based on imaging approaches and automatized data retrieval and processing. Field phenotyping still poses major challenges and the progress of HTPP for field conditions can be relevant to support selection and breeding of grapevine. The aim of this review is to discuss potential and current methods to improve field phenotyping of grapevine to support characterization of inter- and intravarietal diversity. Vitis vinifera has a large genetic diversity that needs characterization, and the availability of methods to support selection of plant material (polyclonal or clonal) able to withstand abiotic stress is paramount. Besides being time consuming, complex and expensive, field experiments are also affected by heterogeneous and uncontrolled climate and soil conditions, mostly due to the large areas of the trials and to the high number of traits to be observed in a number of individuals ranging from hundreds to thousands. Therefore, adequate field experimental design and data gathering methodologies are crucial to obtain reliable data. Some of the major challenges posed to grapevine selection programs for tolerance to water and heat stress are described herein. Useful traits for selection and related field phenotyping methodologies are described and their adequacy for large scale screening is discussed.
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Affiliation(s)
- Luísa C. Carvalho
- LEAF – Linking Landscape, Environment, Agriculture and Food – Research Center, Associated Laboratory TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, Portugal
| | - Elsa F. Gonçalves
- LEAF – Linking Landscape, Environment, Agriculture and Food – Research Center, Associated Laboratory TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, Portugal
| | - Jorge Marques da Silva
- BioISI – Biosystems and Integrative Sciences Institute, Faculty of Sciences, Universidade de Lisboa, Lisboa, Portugal
| | - J. Miguel Costa
- LEAF – Linking Landscape, Environment, Agriculture and Food – Research Center, Associated Laboratory TERRA, Instituto Superior de Agronomia, Universidade de Lisboa, Lisboa, Portugal
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40
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Incorporating Multi-Scale, Spectrally Detected Nitrogen Concentrations into Assessing Nitrogen Use Efficiency for Winter Wheat Breeding Populations. REMOTE SENSING 2021. [DOI: 10.3390/rs13193991] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Annually, over 100 million tons of nitrogen fertilizer are applied in wheat fields to ensure maximum productivity. This amount is often more than needed for optimal yield and can potentially have negative economic and environmental consequences. Monitoring crop nitrogen levels can inform managers of input requirements and potentially avoid excessive fertilization. Standard methods assessing plant nitrogen content, however, are time-consuming, destructive, and expensive. Therefore, the development of approaches estimating leaf nitrogen content in vivo and in situ could benefit fertilization management programs as well as breeding programs for nitrogen use efficiency (NUE). This study examined the ability of hyperspectral data to estimate leaf nitrogen concentrations and nitrogen uptake efficiency (NUpE) at the leaf and canopy levels in multiple winter wheat lines across two seasons. We collected spectral profiles of wheat foliage and canopies using full-range (350–2500 nm) spectroradiometers in combination with leaf tissue collection for standard analytical determination of nitrogen. We then applied partial least-squares regression, using spectral and reference nitrogen measurements, to build predictive models of leaf and canopy nitrogen concentrations. External validation of data from a multi-year model demonstrated effective nitrogen estimation at leaf and canopy level (R2 = 0.72, 0.67; root-mean-square error (RMSE) = 0.42, 0.46; normalized RMSE = 12, 13; bias = −0.06, 0.04, respectively). While NUpE was not directly well predicted using spectral data, NUpE values calculated from predicted leaf and canopy nitrogen levels were well correlated with NUpE determined using traditional methods, suggesting the potential of the approach in possibly replacing standard determination of plant nitrogen in assessing NUE. The results of our research reinforce the ability of hyperspectral data for the retrieval of nitrogen status and expand the utility of hyperspectral data in winter wheat lines to the application of nitrogen management practices and breeding programs.
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Zhu Y, Sun G, Ding G, Zhou J, Wen M, Jin S, Zhao Q, Colmer J, Ding Y, Ober ES, Zhou J. Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat. PLANT PHYSIOLOGY 2021; 187:716-738. [PMID: 34608970 PMCID: PMC8491082 DOI: 10.1093/plphys/kiab324] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2021] [Accepted: 06/22/2021] [Indexed: 05/12/2023]
Abstract
Plant phenomics bridges the gap between traits of agricultural importance and genomic information. Limitations of current field-based phenotyping solutions include mobility, affordability, throughput, accuracy, scalability, and the ability to analyze big data collected. Here, we present a large-scale phenotyping solution that combines a commercial backpack Light Detection and Ranging (LiDAR) device and our analytic software, CropQuant-3D, which have been applied jointly to phenotype wheat (Triticum aestivum) and associated 3D trait analysis. The use of LiDAR can acquire millions of 3D points to represent spatial features of crops, and CropQuant-3D can extract meaningful traits from large, complex point clouds. In a case study examining the response of wheat varieties to three different levels of nitrogen fertilization in field experiments, the combined solution differentiated significant genotype and treatment effects on crop growth and structural variation in the canopy, with strong correlations with manual measurements. Hence, we demonstrate that this system could consistently perform 3D trait analysis at a larger scale and more quickly than heretofore possible and addresses challenges in mobility, throughput, and scalability. To ensure our work could reach non-expert users, we developed an open-source graphical user interface for CropQuant-3D. We, therefore, believe that the combined system is easy-to-use and could be used as a reliable research tool in multi-location phenotyping for both crop research and breeding. Furthermore, together with the fast maturity of LiDAR technologies, the system has the potential for further development in accuracy and affordability, contributing to the resolution of the phenotyping bottleneck and exploiting available genomic resources more effectively.
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Affiliation(s)
- Yulei Zhu
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Gang Sun
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Guohui Ding
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Jie Zhou
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Mingxing Wen
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
- Zhenjiang Institute of Agricultural Science in Hill Area of Jiangsu Province, Jurong 212400, China
| | - Shichao Jin
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Qiang Zhao
- National Center for Gene Research, CAS Center for Excellence in Molecular Plant Sciences, Chinese Academy of Sciences, Shanghai 200233, China
| | - Joshua Colmer
- Earlham Institute, Norwich Research Park, Norwich NR4 7UH, UK
| | - Yanfeng Ding
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
| | - Eric S. Ober
- Cambridge Crop Research, National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK
| | - Ji Zhou
- State Key Laboratory of Crop Genetics and Germplasm Enhancement, College of Engineering, College of Agriculture, Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production Co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, China
- Cambridge Crop Research, National Institute of Agricultural Botany (NIAB), Cambridge CB3 0LE, UK
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Nakhle F, Harfouche AL. Ready, Steady, Go AI: A practical tutorial on fundamentals of artificial intelligence and its applications in phenomics image analysis. PATTERNS (NEW YORK, N.Y.) 2021; 2:100323. [PMID: 34553170 PMCID: PMC8441561 DOI: 10.1016/j.patter.2021.100323] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
High-throughput image-based technologies are now widely used in the rapidly developing field of digital phenomics and are generating ever-increasing amounts and diversity of data. Artificial intelligence (AI) is becoming a game changer in turning the vast seas of data into valuable predictions and insights. However, this requires specialized programming skills and an in-depth understanding of machine learning, deep learning, and ensemble learning algorithms. Here, we attempt to methodically review the usage of different tools, technologies, and services available to the phenomics data community and show how they can be applied to selected problems in explainable AI-based image analysis. This tutorial provides practical and useful resources for novices and experts to harness the potential of the phenomic data in explainable AI-led breeding programs.
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Affiliation(s)
- Farid Nakhle
- Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via S. Camillo de Lellis, Viterbo 01100, Italy
| | - Antoine L. Harfouche
- Department for Innovation in Biological, Agro-food and Forest systems, University of Tuscia, Via S. Camillo de Lellis, Viterbo 01100, Italy
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43
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Razzaq A, Kaur P, Akhter N, Wani SH, Saleem F. Next-Generation Breeding Strategies for Climate-Ready Crops. FRONTIERS IN PLANT SCIENCE 2021; 12:620420. [PMID: 34367194 PMCID: PMC8336580 DOI: 10.3389/fpls.2021.620420] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Accepted: 06/14/2021] [Indexed: 05/17/2023]
Abstract
Climate change is a threat to global food security due to the reduction of crop productivity around the globe. Food security is a matter of concern for stakeholders and policymakers as the global population is predicted to bypass 10 billion in the coming years. Crop improvement via modern breeding techniques along with efficient agronomic practices innovations in microbiome applications, and exploiting the natural variations in underutilized crops is an excellent way forward to fulfill future food requirements. In this review, we describe the next-generation breeding tools that can be used to increase crop production by developing climate-resilient superior genotypes to cope with the future challenges of global food security. Recent innovations in genomic-assisted breeding (GAB) strategies allow the construction of highly annotated crop pan-genomes to give a snapshot of the full landscape of genetic diversity (GD) and recapture the lost gene repertoire of a species. Pan-genomes provide new platforms to exploit these unique genes or genetic variation for optimizing breeding programs. The advent of next-generation clustered regularly interspaced short palindromic repeat/CRISPR-associated (CRISPR/Cas) systems, such as prime editing, base editing, and de nova domestication, has institutionalized the idea that genome editing is revamped for crop improvement. Also, the availability of versatile Cas orthologs, including Cas9, Cas12, Cas13, and Cas14, improved the editing efficiency. Now, the CRISPR/Cas systems have numerous applications in crop research and successfully edit the major crop to develop resistance against abiotic and biotic stress. By adopting high-throughput phenotyping approaches and big data analytics tools like artificial intelligence (AI) and machine learning (ML), agriculture is heading toward automation or digitalization. The integration of speed breeding with genomic and phenomic tools can allow rapid gene identifications and ultimately accelerate crop improvement programs. In addition, the integration of next-generation multidisciplinary breeding platforms can open exciting avenues to develop climate-ready crops toward global food security.
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Affiliation(s)
- Ali Razzaq
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad, Pakistan
| | - Parwinder Kaur
- UWA School of Agriculture and Environment, The University of Western Australia, Perth, WA, Australia
| | - Naheed Akhter
- College of Allied Health Professional, Faculty of Medical Sciences, Government College University Faisalabad, Faisalabad, Pakistan
| | - Shabir Hussain Wani
- Mountain Research Center for Field Crops, Khudwani, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, India
| | - Fozia Saleem
- Centre of Agricultural Biochemistry and Biotechnology (CABB), University of Agriculture, Faisalabad, Pakistan
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Reynolds MP, Lewis JM, Ammar K, Basnet BR, Crespo-Herrera L, Crossa J, Dhugga KS, Dreisigacker S, Juliana P, Karwat H, Kishii M, Krause MR, Langridge P, Lashkari A, Mondal S, Payne T, Pequeno D, Pinto F, Sansaloni C, Schulthess U, Singh RP, Sonder K, Sukumaran S, Xiong W, Braun HJ. Harnessing translational research in wheat for climate resilience. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:5134-5157. [PMID: 34139769 PMCID: PMC8272565 DOI: 10.1093/jxb/erab256] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 06/14/2021] [Indexed: 05/24/2023]
Abstract
Despite being the world's most widely grown crop, research investments in wheat (Triticum aestivum and Triticum durum) fall behind those in other staple crops. Current yield gains will not meet 2050 needs, and climate stresses compound this challenge. However, there is good evidence that heat and drought resilience can be boosted through translating promising ideas into novel breeding technologies using powerful new tools in genetics and remote sensing, for example. Such technologies can also be applied to identify climate resilience traits from among the vast and largely untapped reserve of wheat genetic resources in collections worldwide. This review describes multi-pronged research opportunities at the focus of the Heat and Drought Wheat Improvement Consortium (coordinated by CIMMYT), which together create a pipeline to boost heat and drought resilience, specifically: improving crop design targets using big data approaches; developing phenomic tools for field-based screening and research; applying genomic technologies to elucidate the bases of climate resilience traits; and applying these outputs in developing next-generation breeding methods. The global impact of these outputs will be validated through the International Wheat Improvement Network, a global germplasm development and testing system that contributes key productivity traits to approximately half of the global wheat-growing area.
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Affiliation(s)
- Matthew P Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Janet M Lewis
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Karim Ammar
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Bhoja R Basnet
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - José Crossa
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kanwarpal S Dhugga
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Philomin Juliana
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Hannes Karwat
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Masahiro Kishii
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Margaret R Krause
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Peter Langridge
- School of Agriculture, Food and Wine, University of Adelaide, Waite Campus, PMB1, Glen Osmond SA 5064, Australia
- Wheat Initiative, Julius Kühn-Institute, Königin-Luise-Str. 19, 14195 Berlin, Germany
| | - Azam Lashkari
- CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, 450002, PR China
| | - Suchismita Mondal
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Thomas Payne
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Diego Pequeno
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Francisco Pinto
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Carolina Sansaloni
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Urs Schulthess
- CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, 450002, PR China
| | - Ravi P Singh
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | - Kai Sonder
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
| | | | - Wei Xiong
- CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou, 450002, PR China
| | - Hans J Braun
- International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
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Deterministic and stochastic modelling of impacts from genomic selection and phenomics on genetic gain for perennial ryegrass dry matter yield. Sci Rep 2021; 11:13265. [PMID: 34168203 PMCID: PMC8225875 DOI: 10.1038/s41598-021-92537-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2021] [Accepted: 06/11/2021] [Indexed: 12/02/2022] Open
Abstract
Increasing the efficiency of current forage breeding programs through adoption of new technologies, such as genomic selection (GS) and phenomics (Ph), is challenging without proof of concept demonstrating cost effective genetic gain (∆G). This paper uses decision support software DeltaGen (tactical tool) and QU-GENE (strategic tool), to model and assess relative efficiency of five breeding methods. The effect on ∆G and cost ($) of integrating GS and Ph into an among half-sib (HS) family phenotypic selection breeding strategy was investigated. Deterministic and stochastic modelling were conducted using mock data sets of 200 and 1000 perennial ryegrass HS families using year-by-season-by-location dry matter (DM) yield data and in silico generated data, respectively. Results demonstrated short (deterministic)- and long-term (stochastic) impacts of breeding strategy and integration of key technologies, GS and Ph, on ∆G. These technologies offer substantial improvements in the rate of ∆G, and in some cases improved cost-efficiency. Applying 1% within HS family GS, predicted a 6.35 and 8.10% ∆G per cycle for DM yield from the 200 HS and 1000 HS, respectively. The application of GS in both among and within HS selection provided a significant boost to total annual ∆G, even at low GS accuracy rA of 0.12. Despite some reduction in ∆G, using Ph to assess seasonal DM yield clearly demonstrated its impact by reducing cost per percentage ∆G relative to standard DM cuts. Open-source software tools, DeltaGen and QuLinePlus/QU-GENE, offer ways to model the impact of breeding methodology and technology integration under a range of breeding scenarios.
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Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs. INVENTIONS 2021. [DOI: 10.3390/inventions6020042] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.
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Varshney RK, Bohra A, Yu J, Graner A, Zhang Q, Sorrells ME. Designing Future Crops: Genomics-Assisted Breeding Comes of Age. TRENDS IN PLANT SCIENCE 2021; 26:631-649. [PMID: 33893045 DOI: 10.1016/j.tplants.2021.03.010] [Citation(s) in RCA: 150] [Impact Index Per Article: 50.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/11/2020] [Revised: 03/16/2021] [Accepted: 03/17/2021] [Indexed: 05/18/2023]
Abstract
Over the past decade, genomics-assisted breeding (GAB) has been instrumental in harnessing the potential of modern genome resources and characterizing and exploiting allelic variation for germplasm enhancement and cultivar development. Sustaining GAB in the future (GAB 2.0) will rely upon a suite of new approaches that fast-track targeted manipulation of allelic variation for creating novel diversity and facilitate their rapid and efficient incorporation in crop improvement programs. Genomic breeding strategies that optimize crop genomes with accumulation of beneficial alleles and purging of deleterious alleles will be indispensable for designing future crops. In coming decades, GAB 2.0 is expected to play a crucial role in breeding more climate-smart crop cultivars with higher nutritional value in a cost-effective and timely manner.
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Affiliation(s)
- Rajeev K Varshney
- Center of Excellence in Genomics and Systems Biology (CEGSB), International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, India; State Agricultural Biotechnology Centre, Centre for Crop and Food Innovation, Food Futures Institute, Murdoch University, Murdoch, Western Australia, Australia.
| | - Abhishek Bohra
- Crop Improvement Division, ICAR- Indian Institute of Pulses Research (ICAR- IIPR), Kanpur, India
| | - Jianming Yu
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | - Andreas Graner
- Leibniz Institute of Plant Genetics and Crops Plant Research (IPK), Gatersleben, Germany
| | - Qifa Zhang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Mark E Sorrells
- Department of Plant Breeding and Genetics, Cornell University, Ithaca, NY, USA
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48
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Robles-Zazueta CA, Molero G, Pinto F, Foulkes MJ, Reynolds MP, Murchie EH. Field-based remote sensing models predict radiation use efficiency in wheat. JOURNAL OF EXPERIMENTAL BOTANY 2021; 72:3756-3773. [PMID: 33713415 PMCID: PMC8096595 DOI: 10.1093/jxb/erab115] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Accepted: 03/10/2021] [Indexed: 05/08/2023]
Abstract
Wheat yields are stagnating or declining in many regions, requiring efforts to improve the light conversion efficiency, known as radiation use efficiency (RUE). RUE is a key trait in plant physiology because it links light capture and primary metabolism with biomass accumulation and yield, but its measurement is time consuming and this has limited its use in fundamental research and large-scale physiological breeding. In this study, high-throughput plant phenotyping (HTPP) approaches were used among a population of field-grown wheat with variation in RUE and photosynthetic traits to build predictive models of RUE, biomass, and intercepted photosynthetically active radiation (IPAR). Three approaches were used: best combination of sensors; canopy vegetation indices; and partial least squares regression. The use of remote sensing models predicted RUE with up to 70% accuracy compared with ground truth data. Water indices and canopy greenness indices [normalized difference vegetation index (NDVI), enhanced vegetation index (EVI)] are the better option to predict RUE, biomass, and IPAR, and indices related to gas exchange, non-photochemical quenching [photochemical reflectance index (PRI)] and senescence [structural-insensitive pigment index (SIPI)] are better predictors for these traits at the vegetative and grain-filling stages, respectively. These models will be instrumental to explain canopy processes, improve crop growth and yield modelling, and potentially be used to predict RUE in different crops or ecosystems.
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Affiliation(s)
- Carlos A Robles-Zazueta
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD,UK
- International Maize and Wheat Improvement Center (CIMMYT), carretera Mexico-Veracruz km 45, El Batan, Texcoco, Mexico CP
| | - Gemma Molero
- International Maize and Wheat Improvement Center (CIMMYT), carretera Mexico-Veracruz km 45, El Batan, Texcoco, Mexico CP
- KWS Momont Recherche, 7 rue de Martinval, 59246 Mons-en-Pevele,France
| | - Francisco Pinto
- International Maize and Wheat Improvement Center (CIMMYT), carretera Mexico-Veracruz km 45, El Batan, Texcoco, Mexico CP
| | - M John Foulkes
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD,UK
| | - Matthew P Reynolds
- International Maize and Wheat Improvement Center (CIMMYT), carretera Mexico-Veracruz km 45, El Batan, Texcoco, Mexico CP
| | - Erik H Murchie
- Division of Plant and Crop Sciences, School of Biosciences, University of Nottingham, Sutton Bonington Campus, Leicestershire LE12 5RD,UK
- Correspondence:
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Registration and Fusion of Close-Range Multimodal Wheat Images in Field Conditions. REMOTE SENSING 2021. [DOI: 10.3390/rs13071380] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Multimodal images fusion has the potential to enrich the information gathered by multi-sensor plant phenotyping platforms. Fusion of images from multiple sources is, however, hampered by the technical lock of image registration. The aim of this paper is to provide a solution to the registration and fusion of multimodal wheat images in field conditions and at close range. Eight registration methods were tested on nadir wheat images acquired by a pair of red, green and blue (RGB) cameras, a thermal camera and a multispectral camera array. The most accurate method, relying on a local transformation, aligned the images with an average error of 2 mm but was not reliable for thermal images. More generally, the suggested registration method and the preprocesses necessary before fusion (plant mask erosion, pixel intensity averaging) would depend on the application. As a consequence, the main output of this study was to identify four registration-fusion strategies: (i) the REAL-TIME strategy solely based on the cameras’ positions, (ii) the FAST strategy suitable for all types of images tested, (iii) and (iv) the ACCURATE and HIGHLY ACCURATE strategies handling local distortion but unable to deal with images of very different natures. These suggestions are, however, limited to the methods compared in this study. Further research should investigate how recent cutting-edge registration methods would perform on the specific case of wheat canopy.
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50
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de Jesus Colwell F, Souter J, Bryan GJ, Compton LJ, Boonham N, Prashar A. Development and Validation of Methodology for Estimating Potato Canopy Structure for Field Crop Phenotyping and Improved Breeding. FRONTIERS IN PLANT SCIENCE 2021; 12:612843. [PMID: 33643346 PMCID: PMC7902928 DOI: 10.3389/fpls.2021.612843] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 01/19/2021] [Indexed: 05/30/2023]
Abstract
Traditional phenotyping techniques have long been a bottleneck in breeding programs and genotype- phenotype association studies in potato, as these methods are labor-intensive and time consuming. In addition, depending on the trait measured and metric adopted, they suffer from varying degrees of user bias and inaccuracy, and hence these challenges have effectively prevented the execution of large-scale population-based field studies. This is true not only for commercial traits (e.g., yield, tuber size, and shape), but also for traits strongly associated with plant performance (e.g., canopy development, canopy architecture, and growth rates). This study demonstrates how the use of point cloud data obtained from low-cost UAV imaging can be used to create 3D surface models of the plant canopy, from which detailed and accurate data on plant height and its distribution, canopy ground cover and canopy volume can be obtained over the growing season. Comparison of the canopy datasets at different temporal points enabled the identification of distinct patterns of canopy development, including different patterns of growth, plant lodging, maturity and senescence. Three varieties are presented as exemplars. Variety Nadine presented the growth pattern of an early maturing variety, showing rapid initial growth followed by rapid onset of senescence and plant death. Varieties Bonnie and Bounty presented the pattern of intermediate to late maturing varieties, with Bonnie also showing early canopy lodging. The methodological approach used in this study may alleviate one of the current bottlenecks in the study of plant development, paving the way for an expansion in the scale of future genotype-phenotype association studies.
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Affiliation(s)
- Filipe de Jesus Colwell
- School of Natural Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jock Souter
- Survey Solutions Scotland, Edinburgh, United Kingdom
| | | | - Lindsey J. Compton
- School of Biosciences, University of Birmingham, Birmingham, United Kingdom
| | - Neil Boonham
- School of Natural Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Ankush Prashar
- School of Natural Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
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