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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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Lärm L, Bauer FM, Hermes N, van der Kruk J, Vereecken H, Vanderborght J, Nguyen TH, Lopez G, Seidel SJ, Ewert F, Schnepf A, Klotzsche A. Multi-year belowground data of minirhizotron facilities in Selhausen. Sci Data 2023; 10:672. [PMID: 37789016 PMCID: PMC10547842 DOI: 10.1038/s41597-023-02570-9] [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: 04/26/2023] [Accepted: 09/14/2023] [Indexed: 10/05/2023] Open
Abstract
The production of crops secure the human food supply, but climate change is bringing new challenges. Dynamic plant growth and corresponding environmental data are required to uncover phenotypic crop responses to the changing environment. There are many datasets on above-ground organs of crops, but roots and the surrounding soil are rarely the subject of longer term studies. Here, we present what we believe to be the first comprehensive collection of root and soil data, obtained at two minirhizotron facilities located close together that have the same local climate but differ in soil type. Both facilities have 7m-long horizontal tubes at several depths that were used for crosshole ground-penetrating radar and minirhizotron camera systems. Soil sensors provide observations at a high temporal and spatial resolution. The ongoing measurements cover five years of maize and wheat trials, including drought stress treatments and crop mixtures. We make the processed data available for use in investigating the processes within the soil-plant continuum and the root images to develop and compare image analysis methods.
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Affiliation(s)
- Lena Lärm
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany.
| | - Felix Maximilian Bauer
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany.
| | - Normen Hermes
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
| | - Jan van der Kruk
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
| | - Harry Vereecken
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
| | - Jan Vanderborght
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
| | - Thuy Huu Nguyen
- Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, 53115, Germany
| | - Gina Lopez
- Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, 53115, Germany
| | - Sabine Julia Seidel
- Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, 53115, Germany
| | - Frank Ewert
- Crop Science Group, Institute of Crop Science and Resource Conservation (INRES), University of Bonn, Bonn, 53115, Germany
- Leibniz Centre for Agricultural Landscape Research (ZALF), Müncheberg, 15374, Germany
| | - Andrea Schnepf
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany
| | - Anja Klotzsche
- Institute of Bio-and Geosciences, Agrosphere (IBG-3), Forschungszentrum Jülich GmbH, Jülich, 52425, Germany.
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Cembrowska-Lech D, Krzemińska A, Miller T, Nowakowska A, Adamski C, Radaczyńska M, Mikiciuk G, Mikiciuk M. An Integrated Multi-Omics and Artificial Intelligence Framework for Advance Plant Phenotyping in Horticulture. BIOLOGY 2023; 12:1298. [PMID: 37887008 PMCID: PMC10603917 DOI: 10.3390/biology12101298] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 09/27/2023] [Accepted: 09/28/2023] [Indexed: 10/28/2023]
Abstract
This review discusses the transformative potential of integrating multi-omics data and artificial intelligence (AI) in advancing horticultural research, specifically plant phenotyping. The traditional methods of plant phenotyping, while valuable, are limited in their ability to capture the complexity of plant biology. The advent of (meta-)genomics, (meta-)transcriptomics, proteomics, and metabolomics has provided an opportunity for a more comprehensive analysis. AI and machine learning (ML) techniques can effectively handle the complexity and volume of multi-omics data, providing meaningful interpretations and predictions. Reflecting the multidisciplinary nature of this area of research, in this review, readers will find a collection of state-of-the-art solutions that are key to the integration of multi-omics data and AI for phenotyping experiments in horticulture, including experimental design considerations with several technical and non-technical challenges, which are discussed along with potential solutions. The future prospects of this integration include precision horticulture, predictive breeding, improved disease and stress response management, sustainable crop management, and exploration of plant biodiversity. The integration of multi-omics and AI holds immense promise for revolutionizing horticultural research and applications, heralding a new era in plant phenotyping.
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Affiliation(s)
- Danuta Cembrowska-Lech
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
| | - Adrianna Krzemińska
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | - Tymoteusz Miller
- Polish Society of Bioinformatics and Data Science BIODATA, Popiełuszki 4c, 71-214 Szczecin, Poland; (A.K.); (T.M.)
- Institute of Marine and Environmental Sciences, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland
| | - Anna Nowakowska
- Department of Physiology and Biochemistry, Institute of Biology, University of Szczecin, Felczaka 3c, 71-412 Szczecin, Poland;
| | - Cezary Adamski
- Institute of Biology, University of Szczecin, Wąska 13, 71-415 Szczecin, Poland;
| | | | - Grzegorz Mikiciuk
- Department of Horticulture, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
| | - Małgorzata Mikiciuk
- Department of Bioengineering, Faculty of Environmental Management and Agriculture, West Pomeranian University of Technology in Szczecin, Słowackiego 17, 71-434 Szczecin, Poland;
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Zhang P, Huang J, Ma Y, Wang X, Kang M, Song Y. Crop/Plant Modeling Supports Plant Breeding: II. Guidance of Functional Plant Phenotyping for Trait Discovery. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0091. [PMID: 37780969 PMCID: PMC10538623 DOI: 10.34133/plantphenomics.0091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 08/26/2023] [Indexed: 10/03/2023]
Abstract
Observable morphological traits are widely employed in plant phenotyping for breeding use, which are often the external phenotypes driven by a chain of functional actions in plants. Identifying and phenotyping inherently functional traits for crop improvement toward high yields or adaptation to harsh environments remains a major challenge. Prediction of whole-plant performance in functional-structural plant models (FSPMs) is driven by plant growth algorithms based on organ scale wrapped up with micro-environments. In particular, the models are flexible for scaling down or up through specific functions at the organ nexus, allowing the prediction of crop system behaviors from the genome to the field. As such, by virtue of FSPMs, model parameters that determine organogenesis, development, biomass production, allocation, and morphogenesis from a molecular to the whole plant level can be profiled systematically and made readily available for phenotyping. FSPMs can provide rich functional traits representing biological regulatory mechanisms at various scales in a dynamic system, e.g., Rubisco carboxylation rate, mesophyll conductance, specific leaf nitrogen, radiation use efficiency, and source-sink ratio apart from morphological traits. High-throughput phenotyping such traits is also discussed, which provides an unprecedented opportunity to evolve FSPMs. This will accelerate the co-evolution of FSPMs and plant phenomics, and thus improving breeding efficiency. To expand the great promise of FSPMs in crop science, FSPMs still need more effort in multiscale, mechanistic, reproductive organ, and root system modeling. In summary, this study demonstrates that FSPMs are invaluable tools in guiding functional trait phenotyping at various scales and can thus provide abundant functional targets for phenotyping toward crop improvement.
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Affiliation(s)
- Pengpeng Zhang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Jingyao Huang
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
| | - Yuntao Ma
- College of Land Science and Technology, China Agricultural University, Beijing 100094, China
| | - Xiujuan Wang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Mengzhen Kang
- The State Key Laboratory for Management and Control of Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Youhong Song
- School of Agronomy, Anhui Agricultural University, Hefei, Anhui Province 230036, China
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
- Centre for Crop Science, Queensland Alliance for Agriculture and Food Innovation, The University of Queensland, Brisbane, QLD 4350, Australia
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55
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Akiyama R, Goto T, Tameshige T, Sugisaka J, Kuroki K, Sun J, Akita J, Hatakeyama M, Kudoh H, Kenta T, Tonouchi A, Shimahara Y, Sese J, Kutsuna N, Shimizu-Inatsugi R, Shimizu KK. Seasonal pigment fluctuation in diploid and polyploid Arabidopsis revealed by machine learning-based phenotyping method PlantServation. Nat Commun 2023; 14:5792. [PMID: 37737204 PMCID: PMC10517152 DOI: 10.1038/s41467-023-41260-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2022] [Accepted: 08/29/2023] [Indexed: 09/23/2023] Open
Abstract
Long-term field monitoring of leaf pigment content is informative for understanding plant responses to environments distinct from regulated chambers but is impractical by conventional destructive measurements. We developed PlantServation, a method incorporating robust image-acquisition hardware and deep learning-based software that extracts leaf color by detecting plant individuals automatically. As a case study, we applied PlantServation to examine environmental and genotypic effects on the pigment anthocyanin content estimated from leaf color. We processed >4 million images of small individuals of four Arabidopsis species in the field, where the plant shape, color, and background vary over months. Past radiation, coldness, and precipitation significantly affected the anthocyanin content. The synthetic allopolyploid A. kamchatica recapitulated the fluctuations of natural polyploids by integrating diploid responses. The data support a long-standing hypothesis stating that allopolyploids can inherit and combine the traits of progenitors. PlantServation facilitates the study of plant responses to complex environments termed "in natura".
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Affiliation(s)
- Reiko Akiyama
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland
| | - Takao Goto
- Research and Development Division, LPIXEL Inc., Chiyoda-ku, Tokyo, 100-0004, Japan
| | - Toshiaki Tameshige
- Kihara Institute for Biological Research (KIBR), Yokohama City University, 641-12 Maioka, Totsuka-ward, Yokohama, 244-0813, Japan
- Division of Biological Science, Graduate School of Science and Technology, Nara Institute of Science and Technology (NAIST), 8916-5 Takayama-Cho, Ikoma, Nara, 630-0192, Japan
| | - Jiro Sugisaka
- Kihara Institute for Biological Research (KIBR), Yokohama City University, 641-12 Maioka, Totsuka-ward, Yokohama, 244-0813, Japan
- Center for Ecological Research, Kyoto University, Hirano 2-509-3, Otsu, 520-2113, Japan
| | - Ken Kuroki
- Department of Biological Sciences, Graduate School of Science, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Jianqiang Sun
- Research Center for Agricultural Information Technology, National Agriculture and Food Research Organization, 3-1-1 Kannondai, Tsukuba, Ibaraki, 305-8517, Japan
| | - Junichi Akita
- Department of Electric and Computer Engineering, Kanazawa University, Kakuma, Kanazawa, 920-1192, Japan
| | - Masaomi Hatakeyama
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland
- Functional Genomics Center Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland
| | - Hiroshi Kudoh
- Center for Ecological Research, Kyoto University, Hirano 2-509-3, Otsu, 520-2113, Japan
| | - Tanaka Kenta
- Sugadaira Research Station, Mountain Science Center, University of Tsukuba, 1278-294 Sugadaira-kogen, Ueda, 386-2204, Japan
| | - Aya Tonouchi
- Research and Development Division, LPIXEL Inc., Chiyoda-ku, Tokyo, 100-0004, Japan
| | - Yuki Shimahara
- Research and Development Division, LPIXEL Inc., Chiyoda-ku, Tokyo, 100-0004, Japan
| | - Jun Sese
- Artificial Intelligence Research Center, AIST, 2-3-26 Aomi, Koto-ku, Tokyo, 135-0064, Japan
- Humanome Lab, Inc., L-HUB 3F, 1-4, Shumomiyabi-cho, Shinjuku, Tokyo, 162-0822, Japan
- AIST-Tokyo Tech RWBC-OIL, 2-12-1 O-okayama, Meguro-ku, Tokyo, 152-8550, Japan
| | - Natsumaro Kutsuna
- Research and Development Division, LPIXEL Inc., Chiyoda-ku, Tokyo, 100-0004, Japan
| | - Rie Shimizu-Inatsugi
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
| | - Kentaro K Shimizu
- Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, CH-8057, Zurich, Switzerland.
- Kihara Institute for Biological Research (KIBR), Yokohama City University, 641-12 Maioka, Totsuka-ward, Yokohama, 244-0813, Japan.
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Ting TC, Souza ACM, Imel RK, Guadagno CR, Hoagland C, Yang Y, Wang DR. Quantifying physiological trait variation with automated hyperspectral imaging in rice. FRONTIERS IN PLANT SCIENCE 2023; 14:1229161. [PMID: 37799551 PMCID: PMC10548215 DOI: 10.3389/fpls.2023.1229161] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 08/21/2023] [Indexed: 10/07/2023]
Abstract
Advancements in hyperspectral imaging (HSI) together with the establishment of dedicated plant phenotyping facilities worldwide have enabled high-throughput collection of plant spectral images with the aim of inferring target phenotypes. Here, we test the utility of HSI-derived canopy data, which were collected as part of an automated plant phenotyping system, to predict physiological traits in cultivated Asian rice (Oryza sativa). We evaluated 23 genetically diverse rice accessions from two subpopulations under two contrasting nitrogen conditions and measured 14 leaf- and canopy-level parameters to serve as ground-reference observations. HSI-derived data were used to (1) classify treatment groups across multiple vegetative stages using support vector machines (≥ 83% accuracy) and (2) predict leaf-level nitrogen content (N, %, n=88) and carbon to nitrogen ratio (C:N, n=88) with Partial Least Squares Regression (PLSR) following RReliefF wavelength selection (validation: R 2 = 0.797 and RMSEP = 0.264 for N; R 2 = 0.592 and RMSEP = 1.688 for C:N). Results demonstrated that models developed using training data from one rice subpopulation were able to predict N and C:N in the other subpopulation, while models trained on a single treatment group were not able to predict samples from the other treatment. Finally, optimization of PLSR-RReliefF hyperparameters showed that 300-400 wavelengths generally yielded the best model performance with a minimum calibration sample size of 62. Results support the use of canopy-level hyperspectral imaging data to estimate leaf-level N and C:N across diverse rice, and this work highlights the importance of considering calibration set design prior to data collection as well as hyperparameter optimization for model development in future studies.
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Affiliation(s)
- To-Chia Ting
- Agronomy Department, Purdue University, West Lafayette, IN, United States
| | - Augusto C. M. Souza
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, United States
| | - Rachel K. Imel
- Agronomy Department, Purdue University, West Lafayette, IN, United States
| | | | - Chris Hoagland
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, United States
| | - Yang Yang
- Institute for Plant Sciences, Purdue University, West Lafayette, IN, United States
| | - Diane R. Wang
- Agronomy Department, Purdue University, West Lafayette, IN, United States
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Lu Y, Wang J, Fu L, Yu L, Liu Q. High-throughput and separating-free phenotyping method for on-panicle rice grains based on deep learning. FRONTIERS IN PLANT SCIENCE 2023; 14:1219584. [PMID: 37790779 PMCID: PMC10544938 DOI: 10.3389/fpls.2023.1219584] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 08/28/2023] [Indexed: 10/05/2023]
Abstract
Rice is a vital food crop that feeds most of the global population. Cultivating high-yielding and superior-quality rice varieties has always been a critical research direction. Rice grain-related traits can be used as crucial phenotypic evidence to assess yield potential and quality. However, the analysis of rice grain traits is still mainly based on manual counting or various seed evaluation devices, which incur high costs in time and money. This study proposed a high-precision phenotyping method for rice panicles based on visible light scanning imaging and deep learning technology, which can achieve high-throughput extraction of critical traits of rice panicles without separating and threshing rice panicles. The imaging of rice panicles was realized through visible light scanning. The grains were detected and segmented using the Faster R-CNN-based model, and an improved Pix2Pix model cascaded with it was used to compensate for the information loss caused by the natural occlusion between the rice grains. An image processing pipeline was designed to calculate fifteen phenotypic traits of the on-panicle rice grains. Eight varieties of rice were used to verify the reliability of this method. The R2 values between the extraction by the method and manual measurements of the grain number, grain length, grain width, grain length/width ratio and grain perimeter were 0.99, 0.96, 0.83, 0.90 and 0.84, respectively. Their mean absolute percentage error (MAPE) values were 1.65%, 7.15%, 5.76%, 9.13% and 6.51%. The average imaging time of each rice panicle was about 60 seconds, and the total time of data processing and phenotyping traits extraction was less than 10 seconds. By randomly selecting one thousand grains from each of the eight varieties and analyzing traits, it was found that there were certain differences between varieties in the number distribution of thousand-grain length, thousand-grain width, and thousand-grain length/width ratio. The results show that this method is suitable for high-throughput, non-destructive, and high-precision extraction of on-panicle grains traits without separating. Low cost and robust performance make it easy to popularize. The research results will provide new ideas and methods for extracting panicle traits of rice and other crops.
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Affiliation(s)
- Yuwei Lu
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Jinhu Wang
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Ling Fu
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Department of Physics, School of Science, Hainan University, Haikou, China
| | - Lejun Yu
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
| | - Qian Liu
- Key Laboratory of Biomedical Engineering of Hainan Province, School of Biomedical Engineering, Hainan University, Haikou, China
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Heuermann MC, Knoch D, Junker A, Altmann T. Natural plant growth and development achieved in the IPK PhenoSphere by dynamic environment simulation. Nat Commun 2023; 14:5783. [PMID: 37723146 PMCID: PMC10507097 DOI: 10.1038/s41467-023-41332-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2022] [Accepted: 08/31/2023] [Indexed: 09/20/2023] Open
Abstract
In plant science, the suboptimal match of growing conditions hampers the transfer of knowledge from controlled environments in glasshouses or climate chambers to field environments. Here we present the PhenoSphere, a plant cultivation infrastructure designed to simulate field-like environments in a reproducible manner. To benchmark the PhenoSphere, the effects on plant growth of weather conditions of a single maize growing season and of an averaged season over three years are compared to those of a standard glasshouse and of four years of field trials. The single season simulation proves superior to the glasshouse and the averaged season in the PhenoSphere: The simulated weather regime of the single season triggers plant growth and development progression very similar to that observed in the field. Hence, the PhenoSphere enables detailed analyses of performance-related trait expression and causal biological mechanisms in plant populations exposed to weather conditions of current and anticipated future climate scenarios.
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Affiliation(s)
- Marc C Heuermann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, 06466, Seeland OT Gatersleben, Germany.
| | - Dominic Knoch
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, 06466, Seeland OT Gatersleben, Germany
| | - Astrid Junker
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, 06466, Seeland OT Gatersleben, Germany
- Syngenta Seeds GmbH, Zum Knipkenbach 20, 32107, Bad Salzuflen, Germany
| | - Thomas Altmann
- Department of Molecular Genetics, Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Corrensstrasse 3, 06466, Seeland OT Gatersleben, Germany
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Ochar K, Kim SH. Conservation and Global Distribution of Onion ( Allium cepa L.) Germplasm for Agricultural Sustainability. PLANTS (BASEL, SWITZERLAND) 2023; 12:3294. [PMID: 37765458 PMCID: PMC10535454 DOI: 10.3390/plants12183294] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Revised: 09/11/2023] [Accepted: 09/11/2023] [Indexed: 09/29/2023]
Abstract
Onion (Allium cepa L.) is recognized globally as a crucial vegetable crop, prized not only for its culinary applications but also for its numerous health-promoting properties. With climate change relentlessly exerting mounting challenges to agriculture, the preservation and deployment of onion germplasm has become critical to ensuring sustainable agriculture and safeguarding food security. Global onion germplasm collections function as repositories of genetic diversity, holding within them an extensive array of valuable traits or genes. These can be harnessed to develop varieties resilient to climate adversities. Therefore, detailed information concerning onion germplasm collections from various geographical regions can bolster their utility. Furthermore, an amplified understanding of the importance of fostering international and inter-institutional collaborations becomes essential. Sharing and making use of onion genetic resources can provide viable solutions to the looming agricultural challenges of the future. In this review, we have discussed the preservation and worldwide distribution of onion germplasm, along with its implications for agricultural sustainability. We have also underscored the importance of international and interinstitutional collaboration in onion germplasm collecting and conservation for agricultural sustainability.
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Affiliation(s)
- Kingsley Ochar
- Council for Scientific and Industrial Research, Plant Genetic Resources Research Institute, Bunso P.O. Box 7, Ghana;
| | - Seong-Hoon Kim
- National Agrobiodiversity Center, National Institute of Agricultural Sciences, Rural Development Administration, Jeonju 5487, Republic of Korea
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Robson JK, Ferguson JN, McAusland L, Atkinson JA, Tranchant-Dubreuil C, Cubry P, Sabot F, Wells DM, Price AH, Wilson ZA, Murchie EH. Chlorophyll fluorescence-based high-throughput phenotyping facilitates the genetic dissection of photosynthetic heat tolerance in African (Oryza glaberrima) and Asian (Oryza sativa) rice. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:5181-5197. [PMID: 37347829 PMCID: PMC10498015 DOI: 10.1093/jxb/erad239] [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: 12/05/2022] [Accepted: 06/20/2023] [Indexed: 06/24/2023]
Abstract
Rising temperatures and extreme heat events threaten rice production. Half of the global population relies on rice for basic nutrition, and therefore developing heat-tolerant rice is essential. During vegetative development, reduced photosynthetic rates can limit growth and the capacity to store soluble carbohydrates. The photosystem II (PSII) complex is a particularly heat-labile component of photosynthesis. We have developed a high-throughput chlorophyll fluorescence-based screen for photosynthetic heat tolerance capable of screening hundreds of plants daily. Through measuring the response of maximum PSII efficiency to increasing temperature, this platform generates data for modelling the PSII-temperature relationship in large populations in a small amount of time. Coefficients from these models (photosynthetic heat tolerance traits) demonstrated high heritabilities across African (Oryza glaberrima) and Asian (Oryza sativa, Bengal Assam Aus Panel) rice diversity sets, highlighting valuable genetic variation accessible for breeding. Genome-wide association studies were performed across both species for these traits, representing the first documented attempt to characterize the genetic basis of photosynthetic heat tolerance in any species to date. A total of 133 candidate genes were highlighted. These were significantly enriched with genes whose predicted roles suggested influence on PSII activity and the response to stress. We discuss the most promising candidates for improving photosynthetic heat tolerance in rice.
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Affiliation(s)
- Jordan K Robson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
| | - John N Ferguson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
- Department of Plant Sciences, University of Cambridge, Cambridge, UK
- School of Life Sciences, University of Essex, Colchester, UK
| | - Lorna McAusland
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
| | - Jonathan A Atkinson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
| | | | - Phillipe Cubry
- Institut de Recherche pour le Developpement, 911 Av. Agropolis, 34394 Montpellier, France
| | - François Sabot
- Institut de Recherche pour le Developpement, 911 Av. Agropolis, 34394 Montpellier, France
| | - Darren M Wells
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
| | - Adam H Price
- Institut de Recherche pour le Developpement, 911 Av. Agropolis, 34394 Montpellier, France
| | - Zoe A Wilson
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
| | - Erik H Murchie
- School of Biosciences, University of Nottingham, Sutton Bonington Campus, Loughborough, UK
- School of Biological Sciences, University of Aberdeen, Aberdeen, UK
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61
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Zhang Y, Zhang N, Chai X, Sun T. Machine learning for image-based multi-omics analysis of leaf veins. JOURNAL OF EXPERIMENTAL BOTANY 2023; 74:4928-4941. [PMID: 37410807 DOI: 10.1093/jxb/erad251] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2023] [Accepted: 06/29/2023] [Indexed: 07/08/2023]
Abstract
Veins are a critical component of the plant growth and development system, playing an integral role in supporting and protecting leaves, as well as transporting water, nutrients, and photosynthetic products. A comprehensive understanding of the form and function of veins requires a dual approach that combines plant physiology with cutting-edge image recognition technology. The latest advancements in computer vision and machine learning have facilitated the creation of algorithms that can identify vein networks and explore their developmental progression. Here, we review the functional, environmental, and genetic factors associated with vein networks, along with the current status of research on image analysis. In addition, we discuss the methods of venous phenotype extraction and multi-omics association analysis using machine learning technology, which could provide a theoretical basis for improving crop productivity by optimizing the vein network architecture.
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Affiliation(s)
- Yubin Zhang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
| | - Ning Zhang
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
| | - Xiujuan Chai
- Agricultural Information Institute, Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
| | - Tan Sun
- Key Laboratory of Agricultural Big Data, Ministry of Agriculture and Rural Affairs, Beijing, China
- Chinese Academy of Agricultural Sciences, No.12 Zhongguancun South St, Beijing 100081, China
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62
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Ashwath MN, Lavale SA, Santhoshkumar AV, Mohapatra SR, Bhardwaj A, Dash U, Shiran K, Samantara K, Wani SH. Genome-wide association studies: an intuitive solution for SNP identification and gene mapping in trees. Funct Integr Genomics 2023; 23:297. [PMID: 37700096 DOI: 10.1007/s10142-023-01224-8] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Revised: 04/26/2023] [Accepted: 08/31/2023] [Indexed: 09/14/2023]
Abstract
Analysis of natural diversity in wild/cultivated plants can be used to understand the genetic basis for plant breeding programs. Recent advancements in DNA sequencing have expanded the possibilities for genetically altering essential features. There have been several recently disclosed statistical genetic methods for discovering the genes impacting target qualities. One of these useful methods is the genome-wide association study (GWAS), which effectively identifies candidate genes for a variety of plant properties by examining the relationship between a molecular marker (such as SNP) and a target trait. Conventional QTL mapping with highly structured populations has major limitations. The limited number of recombination events results in poor resolution for quantitative traits. Only two alleles at any given locus can be studied simultaneously. Conventional mapping approach fails to work in perennial plants and vegetatively propagated crops. These limitations are sidestepped by association mapping or GWAS. The flexibility of GWAS comes from the fact that the individuals being examined need not be linked to one another, allowing for the use of all meiotic and recombination events to increase resolution. Phenotyping, genotyping, population structure analysis, kinship analysis, and marker-trait association analysis are the fundamental phases of GWAS. With the rapid development of sequencing technologies and computational methods, GWAS is becoming a potent tool for identifying the natural variations that underlie complex characteristics in crops. The use of high-throughput sequencing technologies along with genotyping approaches like genotyping-by-sequencing (GBS) and restriction site associated DNA (RAD) sequencing may be highly useful in fast-forward mapping approach like GWAS. Breeders may use GWAS to quickly unravel the genomes through QTL and association mapping by taking advantage of natural variances. The drawbacks of conventional linkage mapping can be successfully overcome with the use of high-resolution mapping and the inclusion of multiple alleles in GWAS.
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Affiliation(s)
- M N Ashwath
- Department of Forest Biology and Tree Improvement, Kerala Agricultural University, Thrissur, Kerala, 680 656, India
| | - Shivaji Ajinath Lavale
- Centre for Plant Biotechnology and Molecular Biology, Kerala Agricultural University, Thrissur, Kerala, 680 656, India
| | - A V Santhoshkumar
- Department of Forest Biology and Tree Improvement, Kerala Agricultural University, Thrissur, Kerala, 680 656, India
| | - Sourav Ranjan Mohapatra
- Department of Forest Biology and Tree Improvement, Odisha University of Agriculture and Technology, Bhubaneswar, Odisha, 751 003, India.
| | - Ankita Bhardwaj
- Department of Silviculture and Agroforestry, Kerala Agricultural University, Thrissur, Kerala, 680 656, India
| | - Umakanta Dash
- Department of Silviculture and Agroforestry, Kerala Agricultural University, Thrissur, Kerala, 680 656, India
| | - K Shiran
- Department of Forest Biology and Tree Improvement, Kerala Agricultural University, Thrissur, Kerala, 680 656, India
| | - Kajal Samantara
- Institute of Technology, University of Tartu, 50411, Tartu, Estonia
| | - Shabir Hussain Wani
- Mountain Research Center for Field crops, Sher-e-Kashmir University of Agricultural Sciences and Technology Srinagar, Khudwani, Srinagar, Jammu and Kashmir, India.
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63
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Gerullis M, Pieruschka R, Fahrner S, Hartl L, Schurr U, Heckelei T. From genes to policy: mission-oriented governance of plant-breeding research and technologies. FRONTIERS IN PLANT SCIENCE 2023; 14:1235175. [PMID: 37731976 PMCID: PMC10507248 DOI: 10.3389/fpls.2023.1235175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Accepted: 08/07/2023] [Indexed: 09/22/2023]
Abstract
Mission-oriented governance of research focuses on inspirational, yet attainable goals and targets the sustainable development goals through innovation pathways. We disentangle its implications for plant breeding research and thus impacting the sustainability transformation of agricultural systems, as it requires improved crop varieties and management practices. Speedy success in plant breeding is vital to lower the use of chemical fertilizers and pesticides, increase crop resilience to climate stresses and reduce postharvest losses. A key question is how this success may come about? So far plant breeding research has ignored wider social systems feedbacks, but governance also failed to deliver a set of systemic breeding goals providing directionality and organization to research policy of the same. To address these challenges, we propose a heuristic illustrating the core elements needed for governing plant breeding research: Genetics, Environment, Management and Social system (GxExMxS) are the core elements for defining directions for future breeding. We illustrate this based on historic cases in context of current developments in plant phenotyping technologies and derive implications for governing research infrastructures and breeding programs. As part of mission-oriented governance we deem long-term investments into human resources and experimental set-ups for agricultural systems necessary to ensure a symbiotic relationship for private and public breeding actors and recommend fostering collaboration between social and natural sciences for working towards transdisciplinary collaboration.
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Affiliation(s)
- Maria Gerullis
- Dyson School of Applied Economics and Management, Cornell University, Ithaca, NY, United States
- Wheat and Oat Breeding Research, Institute for Crop Science and Plant Breeding, Bavarian State Research Center for Agriculture, Freising, Germany
| | - Roland Pieruschka
- Plant Sciences, Institute of Bio- and Geosciences 2, Jülich Research Centre, Jülich, Germany
| | - Sven Fahrner
- Plant Sciences, Institute of Bio- and Geosciences 2, Jülich Research Centre, Jülich, Germany
| | - Lorenz Hartl
- Wheat and Oat Breeding Research, Institute for Crop Science and Plant Breeding, Bavarian State Research Center for Agriculture, Freising, Germany
| | - Ulrich Schurr
- Plant Sciences, Institute of Bio- and Geosciences 2, Jülich Research Centre, Jülich, Germany
| | - Thomas Heckelei
- Institute for Food and Resource Economics, University of Bonn, Bonn, Germany
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64
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Simpson CJC, Singh P, Sogbohossou DEO, Eric Schranz M, Hibberd JM. A rapid method to quantify vein density in C 4 plants using starch staining. PLANT, CELL & ENVIRONMENT 2023; 46:2928-2938. [PMID: 37350263 PMCID: PMC10947256 DOI: 10.1111/pce.14656] [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: 03/15/2023] [Revised: 06/05/2023] [Accepted: 06/13/2023] [Indexed: 06/24/2023]
Abstract
C4 photosynthesis has evolved multiple times in the angiosperms and typically involves alterations to the biochemistry, cell biology and development of leaves. One common modification found in C4 plants compared with the ancestral C3 state is an increase in vein density such that the leaf contains a larger proportion of bundle sheath cells. Recent findings indicate that there may be significant intraspecific variation in traits such as vein density in C4 plants but to use such natural variation for trait-mapping, rapid phenotyping would be required. Here we report a high-throughput method to quantify vein density that leverages the bundle sheath-specific accumulation of starch found in C4 species. Starch staining allowed high-contrast images to be acquired permitting image analysis with MATLAB- and Python-based programmes. The method works for dicotyledons and monocotolydons. We applied this method to Gynandropsis gynandra where significant variation in vein density was detected between natural accessions, and Zea mays where no variation was apparent in the genotypically diverse lines assessed. We anticipate this approach will be useful to map genes controlling vein density in C4 species demonstrating natural variation for this trait.
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Affiliation(s)
| | - Pallavi Singh
- Department of Plant SciencesUniversity of CambridgeCambridgeUK
| | | | - M. Eric Schranz
- Biosystematics GroupWageningen UniversityWageningenThe Netherlands
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65
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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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66
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Susmitha P, Kumar P, Yadav P, Sahoo S, Kaur G, Pandey MK, Singh V, Tseng TM, Gangurde SS. Genome-wide association study as a powerful tool for dissecting competitive traits in legumes. FRONTIERS IN PLANT SCIENCE 2023; 14:1123631. [PMID: 37645459 PMCID: PMC10461012 DOI: 10.3389/fpls.2023.1123631] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 06/08/2023] [Indexed: 08/31/2023]
Abstract
Legumes are extremely valuable because of their high protein content and several other nutritional components. The major challenge lies in maintaining the quantity and quality of protein and other nutritional compounds in view of climate change conditions. The global need for plant-based proteins has increased the demand for seeds with a high protein content that includes essential amino acids. Genome-wide association studies (GWAS) have evolved as a standard approach in agricultural genetics for examining such intricate characters. Recent development in machine learning methods shows promising applications for dimensionality reduction, which is a major challenge in GWAS. With the advancement in biotechnology, sequencing, and bioinformatics tools, estimation of linkage disequilibrium (LD) based associations between a genome-wide collection of single-nucleotide polymorphisms (SNPs) and desired phenotypic traits has become accessible. The markers from GWAS could be utilized for genomic selection (GS) to predict superior lines by calculating genomic estimated breeding values (GEBVs). For prediction accuracy, an assortment of statistical models could be utilized, such as ridge regression best linear unbiased prediction (rrBLUP), genomic best linear unbiased predictor (gBLUP), Bayesian, and random forest (RF). Both naturally diverse germplasm panels and family-based breeding populations can be used for association mapping based on the nature of the breeding system (inbred or outbred) in the plant species. MAGIC, MCILs, RIAILs, NAM, and ROAM are being used for association mapping in several crops. Several modifications of NAM, such as doubled haploid NAM (DH-NAM), backcross NAM (BC-NAM), and advanced backcross NAM (AB-NAM), have also been used in crops like rice, wheat, maize, barley mustard, etc. for reliable marker-trait associations (MTAs), phenotyping accuracy is equally important as genotyping. Highthroughput genotyping, phenomics, and computational techniques have advanced during the past few years, making it possible to explore such enormous datasets. Each population has unique virtues and flaws at the genomics and phenomics levels, which will be covered in more detail in this review study. The current investigation includes utilizing elite breeding lines as association mapping population, optimizing the choice of GWAS selection, population size, and hurdles in phenotyping, and statistical methods which will analyze competitive traits in legume breeding.
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Affiliation(s)
- Pusarla Susmitha
- Regional Agricultural Research Station, Acharya N.G. Ranga Agricultural University, Andhra Pradesh, India
| | - Pawan Kumar
- Department of Genetics and Plant Breeding, College of Agriculture, Chaudhary Charan Singh (CCS) Haryana Agricultural University, Hisar, India
| | - Pankaj Yadav
- Department of Bioscience and Bioengineering, Indian Institute of Technology, Rajasthan, India
| | - Smrutishree Sahoo
- Department of Genetics and Plant Breeding, School of Agriculture, Gandhi Institute of Engineering and Technology (GIET) University, Odisha, India
| | - Gurleen Kaur
- Horticultural Sciences Department, University of Florida, Gainesville, FL, United States
| | - Manish K. Pandey
- Department of Genomics, Prebreeding and Bioinformatics, International Crops Research Institute for the Semi-Arid Tropics, Hyderabad, India
| | - Varsha Singh
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Te Ming Tseng
- Department of Plant and Soil Sciences, Mississippi State University, Starkville, MS, United States
| | - Sunil S. Gangurde
- Department of Plant Pathology, University of Georgia, Tifton, GA, United States
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67
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Moustaka J, Moustakas M. Early-Stage Detection of Biotic and Abiotic Stress on Plants by Chlorophyll Fluorescence Imaging Analysis. BIOSENSORS 2023; 13:796. [PMID: 37622882 PMCID: PMC10452221 DOI: 10.3390/bios13080796] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/05/2023] [Revised: 07/30/2023] [Accepted: 08/04/2023] [Indexed: 08/26/2023]
Abstract
Most agricultural land, as a result of climate change, experiences severe stress that significantly reduces agricultural yields. Crop sensing by imaging techniques allows early-stage detection of biotic or abiotic stress to avoid damage and significant yield losses. Among the top certified imaging techniques for plant stress detection is chlorophyll a fluorescence imaging, which can evaluate spatiotemporal leaf changes, permitting the pre-symptomatic monitoring of plant physiological status long before any visible symptoms develop, allowing for high-throughput assessment. Here, we review different examples of how chlorophyll a fluorescence imaging analysis can be used to evaluate biotic and abiotic stress. Chlorophyll a is able to detect biotic stress as early as 15 min after Spodoptera exigua feeding, or 30 min after Botrytis cinerea application on tomato plants, or on the onset of water-deficit stress, and thus has potential for early stress detection. Chlorophyll fluorescence (ChlF) analysis is a rapid, non-invasive, easy to perform, low-cost, and highly sensitive method that can estimate photosynthetic performance and detect the influence of diverse stresses on plants. In terms of ChlF parameters, the fraction of open photosystem II (PSII) reaction centers (qp) can be used for early stress detection, since it has been found in many recent studies to be the most accurate and appropriate indicator for ChlF-based screening of the impact of environmental stress on plants.
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Affiliation(s)
| | - Michael Moustakas
- Department of Botany, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece;
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68
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Lauterberg M, Tschiersch H, Papa R, Bitocchi E, Neumann K. Engaging Precision Phenotyping to Scrutinize Vegetative Drought Tolerance and Recovery in Chickpea Plant Genetic Resources. PLANTS (BASEL, SWITZERLAND) 2023; 12:2866. [PMID: 37571019 PMCID: PMC10421427 DOI: 10.3390/plants12152866] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Revised: 07/24/2023] [Accepted: 07/28/2023] [Indexed: 08/13/2023]
Abstract
Precise and high-throughput phenotyping (HTP) of vegetative drought tolerance in chickpea plant genetic resources (PGR) would enable improved screening for genotypes with low relative loss of biomass formation and reliable physiological performance. It could also provide a basis to further decipher the quantitative trait drought tolerance and recovery and gain a better understanding of the underlying mechanisms. In the context of climate change and novel nutritional trends, legumes and chickpea in particular are becoming increasingly important because of their high protein content and adaptation to low-input conditions. The PGR of legumes represent a valuable source of genetic diversity that can be used for breeding. However, the limited use of germplasm is partly due to a lack of available characterization data. The development of HTP systems offers a perspective for the analysis of dynamic plant traits such as abiotic stress tolerance and can support the identification of suitable genetic resources with a potential breeding value. Sixty chickpea accessions were evaluated on an HTP system under contrasting water regimes to precisely evaluate growth, physiological traits, and recovery under optimal conditions in comparison to drought stress at the vegetative stage. In addition to traits such as Estimated Biovolume (EB), Plant Height (PH), and several color-related traits over more than forty days, photosynthesis was examined by chlorophyll fluorescence measurements on relevant days prior to, during, and after drought stress. With high data quality, a wide phenotypic diversity for adaptation, tolerance, and recovery to drought was recorded in the chickpea PGR panel. In addition to a loss of EB between 72% and 82% after 21 days of drought, photosynthetic capacity decreased by 16-28%. Color-related traits can be used as indicators of different drought stress stages, as they show the progression of stress.
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Affiliation(s)
- Madita Lauterberg
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Gatersleben, Germany; (M.L.)
| | - Henning Tschiersch
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Gatersleben, Germany; (M.L.)
| | - Roberto Papa
- Department of Agricultural, Food and Environmental Sciences, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Elena Bitocchi
- Department of Agricultural, Food and Environmental Sciences, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Kerstin Neumann
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), 06466 Gatersleben, Germany; (M.L.)
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69
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Ye Y, Wang P, Zhang M, Abbas M, Zhang J, Liang C, Wang Y, Wei Y, Meng Z, Zhang R. UAV-based time-series phenotyping reveals the genetic basis of plant height in upland cotton. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2023; 115:937-951. [PMID: 37154288 DOI: 10.1111/tpj.16272] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 04/28/2023] [Accepted: 05/02/2023] [Indexed: 05/10/2023]
Abstract
Plant height (PH) is an important agronomic trait affecting crop architecture, biomass, resistance to lodging and mechanical harvesting. Elucidating the genetic governance of plant height is crucial because of the global demand for high crop yields. However, during the rapid growth period of plants the PH changes a lot on a daily basis, which makes it difficult to accurately phenotype the trait by hand on a large scale. In this study, an unmanned aerial vehicle (UAV)-based remote-sensing phenotyping platform was applied to obtain time-series PHs of 320 upland cotton accessions in three different field trials. The results showed that the PHs obtained from UAV images were significantly correlated with ground-based manual measurements, for three trials (R2 = 0.96, 0.95 and 0.96). Two genetic loci on chromosomes A01 and A11 associated with PH were identified by genome-wide association studies (GWAS). GhUBP15 and GhCUL1 were identified to influence PH in further analysis. We obtained a time series of PH values for three field conditions based on remote sensing with UAV. The key genes identified in this study are of great value for the breeding of ideal plant architecture in cotton.
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Affiliation(s)
- Yulu Ye
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Peilin Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Man Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Mubashir Abbas
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Jiaxin Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Chengzhen Liang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yuan Wang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Yunxiao Wei
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Zhigang Meng
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
| | - Rui Zhang
- Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, 100081, China
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70
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Zargar SM, Manzoor M, Bhat B, Wani AB, Sofi PA, Sudan J, Ebinezer LB, Dall'Acqua S, Peron G, Masi A. Metabolic-GWAS provides insights into genetic architecture of seed metabolome in buckwheat. BMC PLANT BIOLOGY 2023; 23:373. [PMID: 37501129 PMCID: PMC10375682 DOI: 10.1186/s12870-023-04381-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Accepted: 07/13/2023] [Indexed: 07/29/2023]
Abstract
BACKGROUND Buckwheat (Fagopyrum spp.), belonging to the Polygonaceae family, is an ancient pseudo-cereal with high nutritional and nutraceutical properties. Buckwheat proteins are gluten-free and show balanced amino acid and micronutrient profiles, with higher content of health-promoting bioactive flavonoids that make it a golden crop of the future. Plant metabolome is increasingly gaining importance as a crucial component to understand the connection between plant physiology and environment and as a potential link between the genome and phenome. However, the genetic architecture governing the metabolome and thus, the phenome is not well understood. Here, we aim to obtain a deeper insight into the genetic architecture of seed metabolome in buckwheat by integrating high throughput metabolomics and genotyping-by-sequencing applying an array of bioinformatics tools for data analysis. RESULTS High throughput metabolomic analysis identified 24 metabolites in seed endosperm of 130 diverse buckwheat genotypes. The genotyping-by-sequencing (GBS) of these genotypes revealed 3,728,028 SNPs. The Genome Association and Prediction Integrated Tool (GAPIT) assisted in the identification of 27 SNPs/QTLs linked to 18 metabolites. Candidate genes were identified near 100 Kb of QTLs, providing insights into several metabolic and biosynthetic pathways. CONCLUSIONS We established the metabolome inventory of 130 germplasm lines of buckwheat, identified QTLs through marker trait association and positions of potential candidate genes. This will pave the way for future dissection of complex economic traits in buckwheat.
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Affiliation(s)
- Sajad Majeed Zargar
- Proteomics Laboratory, Division of Plant Biotechnology, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar, Jammu and Kashmir, India.
| | - Madhiya Manzoor
- Proteomics Laboratory, Division of Plant Biotechnology, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar, Jammu and Kashmir, India
| | - Basharat Bhat
- Division of Animal Biotechnology, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, India
| | - Amir Bashir Wani
- Proteomics Laboratory, Division of Plant Biotechnology, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar, Jammu and Kashmir, India
| | - Parvaze Ahmad Sofi
- Division of Genetics and Plant Breeding, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, Srinagar, India
| | - Jebi Sudan
- Proteomics Laboratory, Division of Plant Biotechnology, Sher-E-Kashmir University of Agricultural Sciences and Technology of Kashmir, Shalimar, Srinagar, Jammu and Kashmir, India
| | - Leonard Barnabas Ebinezer
- Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padova, Padua, Italy
| | - Stefano Dall'Acqua
- Department of Pharmaceutical and Pharmacological Sciences, University of Padova, Padua, Italy
| | - Gregorio Peron
- Department of Molecular and Translational Medicine (DMMT), University of Brescia, Brescia, Italy
| | - Antonio Masi
- Department of Agronomy, Food, Natural Resources, Animals, and Environment, University of Padova, Padua, Italy.
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Sinha D, Maurya AK, Abdi G, Majeed M, Agarwal R, Mukherjee R, Ganguly S, Aziz R, Bhatia M, Majgaonkar A, Seal S, Das M, Banerjee S, Chowdhury S, Adeyemi SB, Chen JT. Integrated Genomic Selection for Accelerating Breeding Programs of Climate-Smart Cereals. Genes (Basel) 2023; 14:1484. [PMID: 37510388 PMCID: PMC10380062 DOI: 10.3390/genes14071484] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 07/14/2023] [Accepted: 07/18/2023] [Indexed: 07/30/2023] Open
Abstract
Rapidly rising population and climate changes are two critical issues that require immediate action to achieve sustainable development goals. The rising population is posing increased demand for food, thereby pushing for an acceleration in agricultural production. Furthermore, increased anthropogenic activities have resulted in environmental pollution such as water pollution and soil degradation as well as alterations in the composition and concentration of environmental gases. These changes are affecting not only biodiversity loss but also affecting the physio-biochemical processes of crop plants, resulting in a stress-induced decline in crop yield. To overcome such problems and ensure the supply of food material, consistent efforts are being made to develop strategies and techniques to increase crop yield and to enhance tolerance toward climate-induced stress. Plant breeding evolved after domestication and initially remained dependent on phenotype-based selection for crop improvement. But it has grown through cytological and biochemical methods, and the newer contemporary methods are based on DNA-marker-based strategies that help in the selection of agronomically useful traits. These are now supported by high-end molecular biology tools like PCR, high-throughput genotyping and phenotyping, data from crop morpho-physiology, statistical tools, bioinformatics, and machine learning. After establishing its worth in animal breeding, genomic selection (GS), an improved variant of marker-assisted selection (MAS), has made its way into crop-breeding programs as a powerful selection tool. To develop novel breeding programs as well as innovative marker-based models for genetic evaluation, GS makes use of molecular genetic markers. GS can amend complex traits like yield as well as shorten the breeding period, making it advantageous over pedigree breeding and marker-assisted selection (MAS). It reduces the time and resources that are required for plant breeding while allowing for an increased genetic gain of complex attributes. It has been taken to new heights by integrating innovative and advanced technologies such as speed breeding, machine learning, and environmental/weather data to further harness the GS potential, an approach known as integrated genomic selection (IGS). This review highlights the IGS strategies, procedures, integrated approaches, and associated emerging issues, with a special emphasis on cereal crops. In this domain, efforts have been taken to highlight the potential of this cutting-edge innovation to develop climate-smart crops that can endure abiotic stresses with the motive of keeping production and quality at par with the global food demand.
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Affiliation(s)
- Dwaipayan Sinha
- Department of Botany, Government General Degree College, Mohanpur 721436, India
| | - Arun Kumar Maurya
- Department of Botany, Multanimal Modi College, Modinagar, Ghaziabad 201204, India
| | - Gholamreza Abdi
- Department of Biotechnology, Persian Gulf Research Institute, Persian Gulf University, Bushehr 75169, Iran
| | - Muhammad Majeed
- Department of Botany, University of Gujrat, Punjab 50700, Pakistan
| | - Rachna Agarwal
- Applied Genomics Section, Bhabha Atomic Research Centre, Mumbai 400085, India
| | - Rashmi Mukherjee
- Research Center for Natural and Applied Sciences, Department of Botany (UG & PG), Raja Narendralal Khan Women's College, Gope Palace, Midnapur 721102, India
| | - Sharmistha Ganguly
- Department of Dravyaguna, Institute of Post Graduate Ayurvedic Education and Research, Kolkata 700009, India
| | - Robina Aziz
- Department of Botany, Government, College Women University, Sialkot 51310, Pakistan
| | - Manika Bhatia
- TERI School of Advanced Studies, New Delhi 110070, India
| | - Aqsa Majgaonkar
- Department of Botany, St. Xavier's College (Autonomous), Mumbai 400001, India
| | - Sanchita Seal
- Department of Botany, Polba Mahavidyalaya, Polba 712148, India
| | - Moumita Das
- V. Sivaram Research Foundation, Bangalore 560040, India
| | - Swastika Banerjee
- Department of Botany, Kairali College of +3 Science, Champua, Keonjhar 758041, India
| | - Shahana Chowdhury
- Department of Biotechnology, Faculty of Engineering Sciences, German University Bangladesh, TNT Road, Telipara, Chandona Chowrasta, Gazipur 1702, Bangladesh
| | - Sherif Babatunde Adeyemi
- Ethnobotany/Phytomedicine Laboratory, Department of Plant Biology, Faculty of Life Sciences, University of Ilorin, Ilorin P.M.B 1515, Nigeria
| | - Jen-Tsung Chen
- Department of Life Sciences, National University of Kaohsiung, Kaohsiung 811, Taiwan
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John GP, Garnica-Díaz CJ. Embracing the complexity of leaf shape: a commentary on 'Anatomical determinants of gas exchange and hydraulics vary with leaf shape in soybean'. ANNALS OF BOTANY 2023; 131:i-iii. [PMID: 37283295 PMCID: PMC10332391 DOI: 10.1093/aob/mcad059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
This article comments on:Bishal G. Tamang, Yanqun Zhang, Michelle A. Zambrano and Elizabeth A. Ainsworth Anatomical determinants of gas exchange and hydraulics vary with leaf shape in soybean, Annals of Botany, Volume 131, Issue 6, 9 May 2023, Pages 909–920, https://doi.org/10.1093/aob/mcac118
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Affiliation(s)
- Grace P John
- Department of Biology, University of Florida, 220 Bartram Hall, PO Box 118525, Gainesville, FL 32611-8525, USA
| | - Claudia J Garnica-Díaz
- Department of Biology, University of Florida, 220 Bartram Hall, PO Box 118525, Gainesville, FL 32611-8525, USA
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Qin J, Monje O, Nugent MR, Finn JR, O’Rourke AE, Wilson KD, Fritsche RF, Baek I, Chan DE, Kim MS. A hyperspectral plant health monitoring system for space crop production. FRONTIERS IN PLANT SCIENCE 2023; 14:1133505. [PMID: 37469773 PMCID: PMC10352677 DOI: 10.3389/fpls.2023.1133505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/29/2022] [Accepted: 06/07/2023] [Indexed: 07/21/2023]
Abstract
Compact and automated sensing systems are needed to monitor plant health for NASA's controlled-environment space crop production. A new hyperspectral system was designed for early detection of plant stresses using both reflectance and fluorescence imaging in visible and near-infrared (VNIR) wavelength range (400-1000 nm). The prototype system mainly includes two LED line lights providing VNIR broadband and UV-A (365 nm) light for reflectance and fluorescence measurement, respectively, a line-scan hyperspectral camera, and a linear motorized stage with a travel range of 80 cm. In an overhead sensor-to-sample arrangement, the stage translates the lights and camera over the plants to acquire reflectance and fluorescence images in sequence during one cycle of line-scan imaging. System software was developed using LabVIEW to realize hardware parameterization, data transfer, and automated imaging functions. The imaging unit was installed in a plant growth chamber at NASA Kennedy Space Center for health monitoring studies for pick-and-eat salad crops. A preliminary experiment was conducted to detect plant drought stress for twelve Dragoon lettuce samples, of which half were well-watered and half were under-watered while growing. A machine learning method using an optimized discriminant classifier based on VNIR reflectance spectra generated classification accuracies over 90% for the first four days of the stress treatment, showing great potential for early detection of the drought stress on lettuce leaves before any visible symptoms and size differences were evident. The system is promising to provide useful information for optimization of growth environment and early mitigation of stresses in space crop production.
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Affiliation(s)
- Jianwei Qin
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD, United States
| | - Oscar Monje
- Amentum, NASA Kennedy Space Center, Merritt Island, FL, United States
| | - Matthew R. Nugent
- Amentum, NASA Kennedy Space Center, Merritt Island, FL, United States
| | - Joshua R. Finn
- Amentum, NASA Kennedy Space Center, Merritt Island, FL, United States
| | - Aubrie E. O’Rourke
- Exploration Research and Technology, NASA Kennedy Space Center, Merritt Island, FL, United States
| | - Kristine D. Wilson
- Exploration Research and Technology, NASA Kennedy Space Center, Merritt Island, FL, United States
| | - Ralph F. Fritsche
- Exploration Research and Technology, NASA Kennedy Space Center, Merritt Island, FL, United States
| | - Insuck Baek
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD, United States
| | - Diane E. Chan
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD, United States
| | - Moon S. Kim
- Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, U.S. Department of Agriculture, Beltsville, MD, United States
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Zhang C, Kong J, Wu D, Guan Z, Ding B, Chen F. Wearable Sensor: An Emerging Data Collection Tool for Plant Phenotyping. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0051. [PMID: 37408737 PMCID: PMC10318905 DOI: 10.34133/plantphenomics.0051] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 06/09/2023] [Indexed: 07/07/2023]
Abstract
The advancement of plant phenomics by using optical imaging-based phenotyping techniques has markedly improved breeding and crop management. However, there remains a challenge in increasing the spatial resolution and accuracy due to their noncontact measurement mode. Wearable sensors, an emerging data collection tool, present a promising solution to address these challenges. By using a contact measurement mode, wearable sensors enable in-situ monitoring of plant phenotypes and their surrounding environments. Although a few pioneering works have been reported in monitoring plant growth and microclimate, the utilization of wearable sensors in plant phenotyping has yet reach its full potential. This review aims to systematically examine the progress of wearable sensors in monitoring plant phenotypes and the environment from an interdisciplinary perspective, including materials science, signal communication, manufacturing technology, and plant physiology. Additionally, this review discusses the challenges and future directions of wearable sensors in the field of plant phenotyping.
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Affiliation(s)
- Cheng Zhang
- College of Engineering,
Nanjing Agricultural University, Nanjing 210095, China
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture,
Nanjing Agricultural University, Nanjing 210095, China
- Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing 210014, China
| | - Jingjing Kong
- College of Engineering,
Nanjing Agricultural University, Nanjing 210095, China
| | - Daosheng Wu
- College of Engineering,
Nanjing Agricultural University, Nanjing 210095, China
| | - Zhiyong Guan
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture,
Nanjing Agricultural University, Nanjing 210095, China
- Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing 210014, China
| | - Baoqing Ding
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture,
Nanjing Agricultural University, Nanjing 210095, China
- Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing 210014, China
| | - Fadi Chen
- State Key Laboratory of Crop Genetics & Germplasm Enhancement and Utilization, Key Laboratory of Biology of Ornamental Plants in East China, National Forestry and Grassland Administration, College of Horticulture,
Nanjing Agricultural University, Nanjing 210095, China
- Zhongshan Biological Breeding Laboratory, No.50 Zhongling Street, Nanjing 210014, China
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Singh B, Kumar S, Elangovan A, Vasht D, Arya S, Duc NT, Swami P, Pawar GS, Raju D, Krishna H, Sathee L, Dalal M, Sahoo RN, Chinnusamy V. Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches. FRONTIERS IN PLANT SCIENCE 2023; 14:1214801. [PMID: 37448870 PMCID: PMC10337996 DOI: 10.3389/fpls.2023.1214801] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 06/07/2023] [Indexed: 07/15/2023]
Abstract
Introduction Phenomics has emerged as important tool to bridge the genotype-phenotype gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its component traits over a different growth phase of plant will immensely help dissect genetic basis of biomass production. Based on RGB images, models have been developed to predict biomass recently. However, it is very challenging to find a model performing stable across experiments. In this study, we recorded RGB and NIR images of wheat germplasm and Recombinant Inbred Lines (RILs) of Raj3765xHD2329, and examined the use of multimodal images from RGB, NIR sensors and machine learning models to predict biomass and leaf area non-invasively. Results The image-based traits (i-Traits) containing geometric features, RGB based indices, RGB colour classes and NIR features were categorized into architectural traits and physiological traits. Total 77 i-Traits were selected for prediction of biomass and leaf area consisting of 35 architectural and 42 physiological traits. We have shown that different biomass related traits such as fresh weight, dry weight and shoot area can be predicted accurately from RGB and NIR images using 16 machine learning models. We applied the models on two consecutive years of experiments and found that measurement accuracies were similar suggesting the generalized nature of models. Results showed that all biomass-related traits could be estimated with about 90% accuracy but the performance of model BLASSO was relatively stable and high in all the traits and experiments. The R2 of BLASSO for fresh weight prediction was 0.96 (both year experiments), for dry weight prediction was 0.90 (Experiment 1) and 0.93 (Experiment 2) and for shoot area prediction 0.96 (Experiment 1) and 0.93 (Experiment 2). Also, the RMSRE of BLASSO for fresh weight prediction was 0.53 (Experiment 1) and 0.24 (Experiment 2), for dry weight prediction was 0.85 (Experiment 1) and 0.25 (Experiment 2) and for shoot area prediction 0.59 (Experiment 1) and 0.53 (Experiment 2). Discussion Based on the quantification power analysis of i-Traits, the determinants of biomass accumulation were found which contains both architectural and physiological traits. The best predictor i-Trait for fresh weight and dry weight prediction was Area_SV and for shoot area prediction was projected shoot area. These results will be helpful for identification and genetic basis dissection of major determinants of biomass accumulation and also non-invasive high throughput estimation of plant growth during different phenological stages can identify hitherto uncovered genes for biomass production and its deployment in crop improvement for breaking the yield plateau.
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Affiliation(s)
- Biswabiplab Singh
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Sudhir Kumar
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Allimuthu Elangovan
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Devendra Vasht
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Sunny Arya
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Nguyen Trung Duc
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
- Vietnam National University of Agriculture, Hanoi, Vietnam
| | - Pooja Swami
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Godawari Shivaji Pawar
- Division of Agricultural Botany, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani, India
| | - Dhandapani Raju
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Hari Krishna
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Lekshmy Sathee
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Monika Dalal
- ICAR-National Institute for Plant Biotechnology, New Delhi, India
| | - Rabi Narayan Sahoo
- Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Viswanathan Chinnusamy
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
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Krishna TPA, Veeramuthu D, Maharajan T, Soosaimanickam M. The Era of Plant Breeding: Conventional Breeding to Genomics-assisted Breeding for Crop Improvement. Curr Genomics 2023; 24:24-35. [PMID: 37920729 PMCID: PMC10334699 DOI: 10.2174/1389202924666230517115912] [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: 01/02/2023] [Revised: 03/31/2023] [Accepted: 04/14/2023] [Indexed: 11/04/2023] Open
Abstract
Plant breeding has made a significant contribution to increasing agricultural production. Conventional breeding based on phenotypic selection is not effective for crop improvement. Because phenotype is considerably influenced by environmental factors, which will affect the selection of breeding materials for crop improvement. The past two decades have seen tremendous progress in plant breeding research. Especially the availability of high-throughput molecular markers followed by genomic-assisted approaches significantly contributed to advancing plant breeding. Integration of speed breeding with genomic and phenomic facilities allowed rapid quantitative trait loci (QTL)/gene identifications and ultimately accelerated crop improvement programs. The advances in sequencing technology helps to understand the genome organization of many crops and helped with genomic selection in crop breeding. Plant breeding has gradually changed from phenotype-to-genotype-based to genotype-to-phenotype-based selection. High-throughput phenomic platforms have played a significant role in the modern breeding program and are considered an essential part of precision breeding. In this review, we discuss the rapid advance in plant breeding technology for efficient crop improvements and provide details on various approaches/platforms that are helpful for crop improvement. This review will help researchers understand the recent developments in crop breeding and improvements.
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Affiliation(s)
| | - Duraipandiyan Veeramuthu
- Division of Plant Biotechnology, Entomology Research Institute, Loyola College, Chennai, Tamil Nadu, India
| | - Theivanayagam Maharajan
- Division of Plant Biotechnology, Entomology Research Institute, Loyola College, Chennai, Tamil Nadu, India
| | - Mariapackiam Soosaimanickam
- Division of Plant Biotechnology, Entomology Research Institute, Loyola College, Chennai, Tamil Nadu, India
- Department of Advanced Zoology & Biotechnology, Loyola College, Nungambakkam, Chennai, 600034, India
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Großkinsky DK, Faure JD, Gibon Y, Haslam RP, Usadel B, Zanetti F, Jonak C. The potential of integrative phenomics to harness underutilized crops for improving stress resilience. FRONTIERS IN PLANT SCIENCE 2023; 14:1216337. [PMID: 37409292 PMCID: PMC10318926 DOI: 10.3389/fpls.2023.1216337] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Accepted: 06/08/2023] [Indexed: 07/07/2023]
Affiliation(s)
- Dominik K. Großkinsky
- AIT Austrian Institute of Technology, Center for Health and Bioresources, Bioresources Unit, Tulln a. d. Donau, Austria
| | - Jean-Denis Faure
- Université Paris-Saclay, INRAE, AgroParisTech, Institut Jean-Pierre Bourgin, Versailles, France
| | - Yves Gibon
- INRAE, Univ. Bordeaux, UMR BFP, Villenave d’Ornon, France
- Bordeaux Metabolome, INRAE, Univ. Bordeaux, Villenave d’Ornon, France
| | | | - Björn Usadel
- IBG-4 Bioinformatics, CEPLAS, Forschungszentrum, Jülich, Germany
- Biological Data Science, Heinrich Heine University, Universitätsstrasse 1, Düsseldorf, Germany
| | - Federica Zanetti
- Department of Agricultural and Food Sciences (DISTAL), Alma Mater Studiorum - Università di Bologna, Bologna, Italy
| | - Claudia Jonak
- AIT Austrian Institute of Technology, Center for Health and Bioresources, Bioresources Unit, Tulln a. d. Donau, Austria
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78
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Abebe AM, Kim Y, Kim J, Kim SL, Baek J. Image-Based High-Throughput Phenotyping in Horticultural Crops. PLANTS (BASEL, SWITZERLAND) 2023; 12:2061. [PMID: 37653978 PMCID: PMC10222289 DOI: 10.3390/plants12102061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2023] [Revised: 05/12/2023] [Accepted: 05/18/2023] [Indexed: 09/02/2023]
Abstract
Plant phenotyping is the primary task of any plant breeding program, and accurate measurement of plant traits is essential to select genotypes with better quality, high yield, and climate resilience. The majority of currently used phenotyping techniques are destructive and time-consuming. Recently, the development of various sensors and imaging platforms for rapid and efficient quantitative measurement of plant traits has become the mainstream approach in plant phenotyping studies. Here, we reviewed the trends of image-based high-throughput phenotyping methods applied to horticultural crops. High-throughput phenotyping is carried out using various types of imaging platforms developed for indoor or field conditions. We highlighted the applications of different imaging platforms in the horticulture sector with their advantages and limitations. Furthermore, the principles and applications of commonly used imaging techniques, visible light (RGB) imaging, thermal imaging, chlorophyll fluorescence, hyperspectral imaging, and tomographic imaging for high-throughput plant phenotyping, are discussed. High-throughput phenotyping has been widely used for phenotyping various horticultural traits, which can be morphological, physiological, biochemical, yield, biotic, and abiotic stress responses. Moreover, the ability of high-throughput phenotyping with the help of various optical sensors will lead to the discovery of new phenotypic traits which need to be explored in the future. We summarized the applications of image analysis for the quantitative evaluation of various traits with several examples of horticultural crops in the literature. Finally, we summarized the current trend of high-throughput phenotyping in horticultural crops and highlighted future perspectives.
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Affiliation(s)
| | | | | | | | - Jeongho Baek
- Department of Agricultural Biotechnology, National Institute of Agricultural Science, Rural Development Administration, Jeonju 54874, Republic of Korea
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Samal I, Bhoi TK, Raj MN, Majhi PK, Murmu S, Pradhan AK, Kumar D, Paschapur AU, Joshi DC, Guru PN. Underutilized legumes: nutrient status and advanced breeding approaches for qualitative and quantitative enhancement. Front Nutr 2023; 10:1110750. [PMID: 37275642 PMCID: PMC10232757 DOI: 10.3389/fnut.2023.1110750] [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: 11/29/2022] [Accepted: 05/02/2023] [Indexed: 06/07/2023] Open
Abstract
Underutilized/orphan legumes provide food and nutritional security to resource-poor rural populations during periods of drought and extreme hunger, thus, saving millions of lives. The Leguminaceae, which is the third largest flowering plant family, has approximately 650 genera and 20,000 species and are distributed globally. There are various protein-rich accessible and edible legumes, such as soybean, cowpea, and others; nevertheless, their consumption rate is far higher than production, owing to ever-increasing demand. The growing global urge to switch from an animal-based protein diet to a vegetarian-based protein diet has also accelerated their demand. In this context, underutilized legumes offer significant potential for food security, nutritional requirements, and agricultural development. Many of the known legumes like Mucuna spp., Canavalia spp., Sesbania spp., Phaseolus spp., and others are reported to contain comparable amounts of protein, essential amino acids, polyunsaturated fatty acids (PUFAs), dietary fiber, essential minerals and vitamins along with other bioactive compounds. Keeping this in mind, the current review focuses on the potential of discovering underutilized legumes as a source of food, feed and pharmaceutically valuable chemicals, in order to provide baseline data for addressing malnutrition-related problems and sustaining pulse needs across the globe. There is a scarcity of information about underutilized legumes and is restricted to specific geographical zones with local or traditional significance. Around 700 genera and 20,000 species remain for domestication, improvement, and mainstreaming. Significant efforts in research, breeding, and development are required to transform existing local landraces of carefully selected, promising crops into types with broad adaptability and economic viability. Different breeding efforts and the use of biotechnological methods such as micro-propagation, molecular markers research and genetic transformation for the development of underutilized crops are offered to popularize lesser-known legume crops and help farmers diversify their agricultural systems and boost their profitability.
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Affiliation(s)
- Ipsita Samal
- Department of Entomology, Faculty of Agriculture, Sri Sri University, Cuttack, Odisha, India
| | - Tanmaya Kumar Bhoi
- Forest Protection Division, ICFRE-Arid Forest Research Institute, Jodhpur, India
| | - M. Nikhil Raj
- Division of Entomology, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Prasanta Kumar Majhi
- Regional Research and Technology Transfer Station, Odisha University of Agriculture and Technology, Keonjhar, Odisha, India
| | - Sneha Murmu
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | | | - Dilip Kumar
- ICAR-National Institute of Agricultural Economics and Policy Research, New Delhi, India
| | | | | | - P. N. Guru
- ICAR-Central Institute of Post-Harvest Engineering and Technology, Ludhiana, India
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80
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Wang W, Man Z, Li X, Chen R, You Z, Pan T, Dai X, Xiao H, Liu F. Response mechanism and rapid detection of phenotypic information in rice root under heavy metal stress. JOURNAL OF HAZARDOUS MATERIALS 2023; 449:131010. [PMID: 36801724 DOI: 10.1016/j.jhazmat.2023.131010] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Revised: 02/11/2023] [Accepted: 02/14/2023] [Indexed: 06/18/2023]
Abstract
The root is an important organ affecting cadmium accumulation in grains, but there is no comprehensive research involving rice root phenotype under cadmium stress yet. To assess the effect of cadmium on root phenotypes, this paper investigated the response mechanism of phenotypic information including cadmium accumulation, adversity physiology, morphological parameters, and microstructure characteristics, and explored rapid detection methods of cadmium accumulation and adversity physiology. We found that cadmium had the effect of "low-promotion and high-inhibition" on root phenotypes. In addition, the rapid detection of cadmium (Cd), soluble protein (SP), and malondialdehyde (MDA) were achieved based on spectroscopic technology and chemometrics, where the optimal prediction model was least squares support vector machine (LS-SVM) based on the full spectrum (Rp=0.9958) for Cd, competitive adaptive reweighted sampling-extreme learning machine (CARS-ELM) (Rp=0.9161) for SP and CARS-ELM (Rp=0.9021) for MDA, all with Rp higher than 0.9. Surprisingly, it took only about 3 min, which was more than 90% reduction in detection time compared with laboratory analysis, demonstrating the excellent ability of spectroscopy for root phenotype detection. These results reveal response mechanism to heavy metal and provide rapid detection method for phenotypic information, which can substantially contribute to crop heavy metal control and food safety supervision.
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Affiliation(s)
- Wei Wang
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, China
| | - Zun Man
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Xiaolong Li
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Rongqin Chen
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Zhengkai You
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Tiantian Pan
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China
| | - Xiaorong Dai
- College of Biological and Environmental Sciences, Zhejiang Wanli University, Ningbo 315100, China
| | - Hang Xiao
- Key Laboratory of Urban Environment and Health, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China; Zhejiang Key Laboratory of Urban Environmental Processes and Pollution Control, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo 315830, China
| | - Fei Liu
- College of Biosystems Engineering and Food Science, Zhejiang University, 866 Yuhangtang Road, Hangzhou 310058, China; State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310058, China.
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81
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Li D, Zhang Z, Gao X, Zhang H, Bai D, Wang Q, Zheng T, Li YH, Qiu LJ. The elite variations in germplasms for soybean breeding. MOLECULAR BREEDING : NEW STRATEGIES IN PLANT IMPROVEMENT 2023; 43:37. [PMID: 37312749 PMCID: PMC10248635 DOI: 10.1007/s11032-023-01378-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Accepted: 04/03/2023] [Indexed: 06/15/2023]
Abstract
The genetic base of soybean cultivars (Glycine max (L.) Merr.) has been narrowed through selective domestication and specific breeding improvement, similar to other crops. This presents challenges in breeding new cultivars with improved yield and quality, reduced adaptability to climate change, and increased susceptibility to diseases. On the other hand, the vast collection of soybean germplasms offers a potential source of genetic variations to address those challenges, but it has yet to be fully leveraged. In recent decades, rapidly improved high-throughput genotyping technologies have accelerated the harness of elite variations in soybean germplasm and provided the important information for solving the problem of a narrowed genetic base in breeding. In this review, we will overview the situation of maintenance and utilization of soybean germplasms, various solutions provided for different needs in terms of the number of molecular markers, and the omics-based high-throughput strategies that have been used or can be used to identify elite alleles. We will also provide an overall genetic information generated from soybean germplasms in yield, quality traits, and pest resistance for molecular breeding.
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Affiliation(s)
- Delin Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Zhengwei Zhang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Xinyue Gao
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Hao Zhang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Dong Bai
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Qi Wang
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
- College of Agriculture, Northeast Agricultural University, Harbin, 150030 China
| | - Tianqing Zheng
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Ying-Hui Li
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
| | - Li-Juan Qiu
- The National Key Facility for Crop Gene Resources and Genetic Improvement (NFCRI)/Key Laboratory of Grain Crop Genetic Resources Evaluation and Utilization, Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing, 100081 China
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82
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Hu Y, Schmidhalter U. Opportunity and challenges of phenotyping plant salt tolerance. TRENDS IN PLANT SCIENCE 2023; 28:552-566. [PMID: 36628656 DOI: 10.1016/j.tplants.2022.12.010] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 12/03/2022] [Accepted: 12/15/2022] [Indexed: 05/22/2023]
Abstract
Salinity is a key factor limiting agricultural production worldwide. Recent advances in field phenotyping have enabled the recording of the environmental history and dynamic response of plants by considering both genotype × environment (G×E) interactions and envirotyping. However, only a few studies have focused on plant salt tolerance phenotyping. Therefore, we analyzed the potential opportunities and major challenges in improving plant salt tolerance using advanced field phenotyping technologies. RGB imaging and spectral and thermal sensors are the most useful and important sensing techniques for assessing key morphological and physiological traits of plant salt tolerance. However, field phenotyping faces challenges owing to its practical applications and high costs, limiting its use in early generation breeding and in developing countries.
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Affiliation(s)
- Yuncai Hu
- Chair of Plant Nutrition, School of Life Sciences, Technical University of Munich, D-85354 Freising, Germany.
| | - Urs Schmidhalter
- Chair of Plant Nutrition, School of Life Sciences, Technical University of Munich, D-85354 Freising, Germany
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83
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Zhao L, Walkowiak S, Fernando WGD. Artificial Intelligence: A Promising Tool in Exploring the Phytomicrobiome in Managing Disease and Promoting Plant Health. PLANTS (BASEL, SWITZERLAND) 2023; 12:plants12091852. [PMID: 37176910 PMCID: PMC10180744 DOI: 10.3390/plants12091852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/06/2023] [Revised: 04/25/2023] [Accepted: 04/27/2023] [Indexed: 05/15/2023]
Abstract
There is increasing interest in harnessing the microbiome to improve cropping systems. With the availability of high-throughput and low-cost sequencing technologies, gathering microbiome data is becoming more routine. However, the analysis of microbiome data is challenged by the size and complexity of the data, and the incomplete nature of many microbiome databases. Further, to bring microbiome data value, it often needs to be analyzed in conjunction with other complex data that impact on crop health and disease management, such as plant genotype and environmental factors. Artificial intelligence (AI), boosted through deep learning (DL), has achieved significant breakthroughs and is a powerful tool for managing large complex datasets such as the interplay between the microbiome, crop plants, and their environment. In this review, we aim to provide readers with a brief introduction to AI techniques, and we introduce how AI has been applied to areas of microbiome sequencing taxonomy, the functional annotation for microbiome sequences, associating the microbiome community with host traits, designing synthetic communities, genomic selection, field phenotyping, and disease forecasting. At the end of this review, we proposed further efforts that are required to fully exploit the power of AI in studying phytomicrobiomes.
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Affiliation(s)
- Liang Zhao
- Department of Plant Science, University of Manitoba, Winnipeg, MB R3T 2N2, Canada
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84
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Buelvas RM, Adamchuk VI, Lan J, Hoyos-Villegas V, Whitmore A, Stromvik MV. Development of a Quick-Install Rapid Phenotyping System. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23094253. [PMID: 37177457 PMCID: PMC10181467 DOI: 10.3390/s23094253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 04/13/2023] [Accepted: 04/21/2023] [Indexed: 05/15/2023]
Abstract
In recent years, there has been a growing need for accessible High-Throughput Plant Phenotyping (HTPP) platforms that can take measurements of plant traits in open fields. This paper presents a phenotyping system designed to address this issue by combining ultrasonic and multispectral sensing of the crop canopy with other diverse measurements under varying environmental conditions. The system demonstrates a throughput increase by a factor of 50 when compared to a manual setup, allowing for efficient mapping of crop status across a field with crops grown in rows of any spacing. Tests presented in this paper illustrate the type of experimentation that can be performed with the platform, emphasizing the output from each sensor. The system integration, versatility, and ergonomics are the most significant contributions. The presented system can be used for studying plant responses to different treatments and/or stresses under diverse farming practices in virtually any field environment. It was shown that crop height and several vegetation indices, most of them common indicators of plant physiological status, can be easily paired with corresponding environmental conditions to facilitate data analysis at the fine spatial scale.
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Affiliation(s)
- Roberto M Buelvas
- Department of Bioresource Engineering, Macdonald Campus, McGill University, 21 111 Lakeshore, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada
| | - Viacheslav I Adamchuk
- Department of Bioresource Engineering, Macdonald Campus, McGill University, 21 111 Lakeshore, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada
| | - John Lan
- Department of Bioresource Engineering, Macdonald Campus, McGill University, 21 111 Lakeshore, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada
| | - Valerio Hoyos-Villegas
- Department of Plant Science, Macdonald Campus, McGill University, 21 111 Lakeshore, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada
| | - Arlene Whitmore
- Department of Plant Science, Macdonald Campus, McGill University, 21 111 Lakeshore, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada
| | - Martina V Stromvik
- Department of Plant Science, Macdonald Campus, McGill University, 21 111 Lakeshore, Ste-Anne-de-Bellevue, QC H9X 3V9, Canada
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85
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Li Y, Wen W, Fan J, Gou W, Gu S, Lu X, Yu Z, Wang X, Guo X. Multi-Source Data Fusion Improves Time-Series Phenotype Accuracy in Maize under a Field High-Throughput Phenotyping Platform. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0043. [PMID: 37223316 PMCID: PMC10202381 DOI: 10.34133/plantphenomics.0043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/14/2022] [Accepted: 03/26/2023] [Indexed: 05/25/2023]
Abstract
The field phenotyping platforms that can obtain high-throughput and time-series phenotypes of plant populations at the 3-dimensional level are crucial for plant breeding and management. However, it is difficult to align the point cloud data and extract accurate phenotypic traits of plant populations. In this study, high-throughput, time-series raw data of field maize populations were collected using a field rail-based phenotyping platform with light detection and ranging (LiDAR) and an RGB (red, green, and blue) camera. The orthorectified images and LiDAR point clouds were aligned via the direct linear transformation algorithm. On this basis, time-series point clouds were further registered by the time-series image guidance. The cloth simulation filter algorithm was then used to remove the ground points. Individual plants and plant organs were segmented from maize population by fast displacement and region growth algorithms. The plant heights of 13 maize cultivars obtained using the multi-source fusion data were highly correlated with the manual measurements (R2 = 0.98), and the accuracy was higher than only using one source point cloud data (R2 = 0.93). It demonstrates that multi-source data fusion can effectively improve the accuracy of time series phenotype extraction, and rail-based field phenotyping platforms can be a practical tool for plant growth dynamic observation of phenotypes in individual plant and organ scales.
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Affiliation(s)
- Yinglun Li
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Weiliang Wen
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Jiangchuan Fan
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Wenbo Gou
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Shenghao Gu
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Xianju Lu
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Zetao Yu
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Xiaodong Wang
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
| | - Xinyu Guo
- Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
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86
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Li Y, Zhan X, Liu S, Lu H, Jiang R, Guo W, Chapman S, Ge Y, Solan B, Ding Y, Baret F. Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage. PLANT PHENOMICS (WASHINGTON, D.C.) 2023; 5:0041. [PMID: 37223315 PMCID: PMC10202135 DOI: 10.34133/plantphenomics.0041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 03/17/2023] [Indexed: 05/25/2023]
Abstract
The number of leaves at a given time is important to characterize plant growth and development. In this work, we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB images. The digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages (150,000 images with over 2 million labels). The realism of the images was then improved using domain adaptation methods before training deep learning models. The results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset, collecting measurements from 5 countries obtained under different environments, growth stages, and lighting conditions with different cameras (450 images with over 2,162 labels). Among the 6 combinations of deep learning models and domain adaptation techniques, the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance (R2 = 0.94, root mean square error = 8.7). Complementary studies show that it is essential to simulate images with sufficient realism (background, leaf texture, and lighting conditions) before applying domain adaptation techniques. Furthermore, the spatial resolution should be better than 0.6 mm per pixel to identify leaf tips. The method is claimed to be self-supervised since no manual labeling is required for model training. The self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping problems. The trained networks are available at https://github.com/YinglunLi/Wheat-leaf-tip-detection.
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Affiliation(s)
- Yinglun Li
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production,
Nanjing Agricultural University, Nanjing, China
| | - Xiaohai Zhan
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production,
Nanjing Agricultural University, Nanjing, China
| | - Shouyang Liu
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production,
Nanjing Agricultural University, Nanjing, China
| | - Hao Lu
- Key Laboratory of Image Processing and Intelligent Control, School of Artificial Intelligence and Automation,
Huazhong University of Science and Technology, Wuhan, China
| | - Ruibo Jiang
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production,
Nanjing Agricultural University, Nanjing, China
| | - Wei Guo
- Graduate School of Agricultural and Life Sciences,
The University of Tokyo, 1-1-1 Midori-cho, Nishitokyo City, Tokyo, Japan
| | - Scott Chapman
- School of Agriculture and Food Sciences,
The University of Queensland, St. Lucia, Queensland 4072, Australia
| | - Yufeng Ge
- Department of Biological Systems Engineering,
University of Nebraska-Lincoln, Lincoln, Nebraska 68583, United States
| | - Benoit Solan
- INRAE,
Avignon Université, UMR EMMAH, UMT CAPTE, 228, route de l’aérodrome - CS 40509, 84914 Avignon Cedex 9, France
- ARVALIS Institut du végétal, 3 rue Joseph et Marie Hackin, 75116 Paris, France
| | - Yanfeng Ding
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production,
Nanjing Agricultural University, Nanjing, China
| | - Frédéric Baret
- Plant Phenomics Research Centre, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production,
Nanjing Agricultural University, Nanjing, China
- INRAE,
Avignon Université, UMR EMMAH, UMT CAPTE, 228, route de l’aérodrome - CS 40509, 84914 Avignon Cedex 9, France
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87
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Li X, Chen Z, Wei X, Zhao T, Jin J. Development of a Target-to-Sensor Mode Multispectral Imaging Device for High-Throughput and High-Precision Touch-Based Leaf-Scale Soybean Phenotyping. SENSORS (BASEL, SWITZERLAND) 2023; 23:3756. [PMID: 37050815 PMCID: PMC10098662 DOI: 10.3390/s23073756] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/14/2023] [Revised: 03/30/2023] [Accepted: 04/03/2023] [Indexed: 06/19/2023]
Abstract
Image-based spectroscopy phenotyping is a rapidly growing field that investigates how genotype, environment and management interact using remote or proximal sensing systems to capture images of a plant under multiple wavelengths of light. While remote sensing techniques have proven effective in crop phenotyping, they can be subject to various noise sources, such as varying lighting conditions and plant physiological status, including leaf orientation. Moreover, current proximal leaf-scale imaging devices require the sensors to accommodate the state of the samples during imaging which induced extra time and labor cost. Therefore, this study developed a proximal multispectral imaging device that can actively attract the leaf to the sensing area (target-to-sensor mode) for high-precision and high-throughput leaf-scale phenotyping. To increase the throughput and to optimize imaging results, this device innovatively uses active airflow to reposition and flatten the soybean leaf. This novel mechanism redefines the traditional sensor-to-target mode and has relieved the device operator from the labor of capturing and holding the leaf, resulting in a five-fold increase in imaging speed compared to conventional proximal whole leaf imaging device. Besides, this device uses artificial lights to create stable and consistent lighting conditions to further improve the quality of the images. Furthermore, the touch-based imaging device takes full advantage of proximal sensing by providing ultra-high spatial resolution and quality of each pixel by blocking the noises induced by ambient lighting variances. The images captured by this device have been tested in the field and proven effective. Specifically, it has successfully identified nitrogen deficiency treatment at an earlier stage than a typical remote sensing system. The p-value of the data collected by the device (p = 0.008) is significantly lower than that of a remote sensing system (p = 0.239).
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88
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Krug AS, B. M. Drummond E, Van Tassel DL, Warschefsky EJ. The next era of crop domestication starts now. Proc Natl Acad Sci U S A 2023; 120:e2205769120. [PMID: 36972445 PMCID: PMC10083606 DOI: 10.1073/pnas.2205769120] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/29/2023] Open
Abstract
Current food systems are challenged by relying on a few input-intensive, staple crops. The prioritization of yield and the loss of diversity during the recent history of domestication has created contemporary crops and cropping systems that are ecologically unsustainable, vulnerable to climate change, nutrient poor, and socially inequitable. For decades, scientists have proposed diversity as a solution to address these challenges to global food security. Here, we outline the possibilities for a new era of crop domestication, focused on broadening the palette of crop diversity, that engages and benefits the three elements of domestication: crops, ecosystems, and humans. We explore how the suite of tools and technologies at hand can be applied to renew diversity in existing crops, improve underutilized crops, and domesticate new crops to bolster genetic, agroecosystem, and food system diversity. Implementing the new era of domestication requires that researchers, funders, and policymakers boldly invest in basic and translational research. Humans need more diverse food systems in the Anthropocene-the process of domestication can help build them.
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Affiliation(s)
| | - Emily B. M. Drummond
- Department of Botany, University of British Columbia, Vancouver, BCV6T 1Z4, Canada
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89
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Young TJ, Jubery TZ, Carley CN, Carroll M, Sarkar S, Singh AK, Singh A, Ganapathysubramanian B. "Canopy fingerprints" for characterizing three-dimensional point cloud data of soybean canopies. FRONTIERS IN PLANT SCIENCE 2023; 14:1141153. [PMID: 37063230 PMCID: PMC10090282 DOI: 10.3389/fpls.2023.1141153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/10/2023] [Accepted: 02/28/2023] [Indexed: 06/19/2023]
Abstract
Advances in imaging hardware allow high throughput capture of the detailed three-dimensional (3D) structure of plant canopies. The point cloud data is typically post-processed to extract coarse-scale geometric features (like volume, surface area, height, etc.) for downstream analysis. We extend feature extraction from 3D point cloud data to various additional features, which we denote as 'canopy fingerprints'. This is motivated by the successful application of the fingerprint concept for molecular fingerprints in chemistry applications and acoustic fingerprints in sound engineering applications. We developed an end-to-end pipeline to generate canopy fingerprints of a three-dimensional point cloud of soybean [Glycine max (L.) Merr.] canopies grown in hill plots captured by a terrestrial laser scanner (TLS). The pipeline includes noise removal, registration, and plot extraction, followed by the canopy fingerprint generation. The canopy fingerprints are generated by splitting the data into multiple sub-canopy scale components and extracting sub-canopy scale geometric features. The generated canopy fingerprints are interpretable and can assist in identifying patterns in a database of canopies, querying similar canopies, or identifying canopies with a certain shape. The framework can be extended to other modalities (for instance, hyperspectral point clouds) and tuned to find the most informative fingerprint representation for downstream tasks. These canopy fingerprints can aid in the utilization of canopy traits at previously unutilized scales, and therefore have applications in plant breeding and resilient crop production.
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Affiliation(s)
- Therin J. Young
- Department of Mechanical Engineering, Iowa State University, Ames, IA, United States
| | | | - Clayton N. Carley
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Matthew Carroll
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, IA, United States
- Translational AI Center, Iowa State University, Ames, IA, United States
| | - Asheesh K. Singh
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Arti Singh
- Department of Agronomy, Iowa State University, Ames, IA, United States
| | - Baskar Ganapathysubramanian
- Department of Mechanical Engineering, Iowa State University, Ames, IA, United States
- Translational AI Center, Iowa State University, Ames, IA, United States
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90
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Wong CYS, Jones T, McHugh DP, Gilbert ME, Gepts P, Palkovic A, Buckley TN, Magney TS. TSWIFT: Tower Spectrometer on Wheels for Investigating Frequent Timeseries for high-throughput phenotyping of vegetation physiology. PLANT METHODS 2023; 19:29. [PMID: 36978119 PMCID: PMC10044391 DOI: 10.1186/s13007-023-01001-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 02/24/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Remote sensing instruments enable high-throughput phenotyping of plant traits and stress resilience across scale. Spatial (handheld devices, towers, drones, airborne, and satellites) and temporal (continuous or intermittent) tradeoffs can enable or constrain plant science applications. Here, we describe the technical details of TSWIFT (Tower Spectrometer on Wheels for Investigating Frequent Timeseries), a mobile tower-based hyperspectral remote sensing system for continuous monitoring of spectral reflectance across visible-near infrared regions with the capacity to resolve solar-induced fluorescence (SIF). RESULTS We demonstrate potential applications for monitoring short-term (diurnal) and long-term (seasonal) variation of vegetation for high-throughput phenotyping applications. We deployed TSWIFT in a field experiment of 300 common bean genotypes in two treatments: control (irrigated) and drought (terminal drought). We evaluated the normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), and SIF, as well as the coefficient of variation (CV) across the visible-near infrared spectral range (400 to 900 nm). NDVI tracked structural variation early in the growing season, following initial plant growth and development. PRI and SIF were more dynamic, exhibiting variation diurnally and seasonally, enabling quantification of genotypic variation in physiological response to drought conditions. Beyond vegetation indices, CV of hyperspectral reflectance showed the most variability across genotypes, treatment, and time in the visible and red-edge spectral regions. CONCLUSIONS TSWIFT enables continuous and automated monitoring of hyperspectral reflectance for assessing variation in plant structure and function at high spatial and temporal resolutions for high-throughput phenotyping. Mobile, tower-based systems like this can provide short- and long-term datasets to assess genotypic and/or management responses to the environment, and ultimately enable the spectral prediction of resource-use efficiency, stress resilience, productivity and yield.
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Affiliation(s)
| | - Taylor Jones
- Department of Earth & Environment, Boston University, Boston, MA 02215 USA
| | - Devin P. McHugh
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Matthew E. Gilbert
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Paul Gepts
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Antonia Palkovic
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Thomas N. Buckley
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
| | - Troy S. Magney
- Department of Plant Sciences, University of California, Davis, Davis, CA 95616 USA
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91
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Fan J, Li Y, Yu S, Gou W, Guo X, Zhao C. Application of Internet of Things to Agriculture-The LQ-FieldPheno Platform: A High-Throughput Platform for Obtaining Crop Phenotypes in Field. RESEARCH (WASHINGTON, D.C.) 2023; 6:0059. [PMID: 36951796 PMCID: PMC10027232 DOI: 10.34133/research.0059] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/10/2022] [Accepted: 01/07/2023] [Indexed: 01/22/2023]
Abstract
The lack of efficient crop phenotypic measurement methods has become a bottleneck in the field of breeding and precision cultivation. However, high-throughput and accurate phenotypic measurement could accelerate the breeding and improve the existing cultivation management technology. In view of this, this paper introduces a high-throughput crop phenotype measurement platform named the LQ-FieldPheno, which was developed by China National Agricultural Information Engineering Technology Research Centre. The proposed platform represents a mobile phenotypic high-throughput automatic acquisition system based on a field track platform, which introduces the Internet of Things (IoT) into agricultural breeding. The proposed platform uses the crop phenotype multisensor central imaging unit as a core and integrates different types of equipment, including an automatic control system, upward field track, intelligent navigation vehicle, and environmental sensors. Furthermore, it combines an RGB camera, a 6-band multispectral camera, a thermal infrared camera, a 3-dimensional laser radar, and a deep camera. Special software is developed to control motions and sensors and to design run lines. Using wireless sensor networks and mobile communication wireless networks of IoT, the proposed system can obtain phenotypic information about plants in their growth period with a high-throughput, automatic, and high time sequence. Moreover, the LQ-FieldPheno has the characteristics of multiple data acquisition, vital timeliness, remarkable expansibility, high-cost performance, and flexible customization. The LQ-FieldPheno has been operated in the 2020 maize growing season, and the collected point cloud data are used to estimate the maize plant height. Compared with the traditional crop phenotypic measurement technology, the LQ-FieldPheno has the advantage of continuously and synchronously obtaining multisource phenotypic data at different growth stages and extracting different plant parameters. The proposed platform could contribute to the research of crop phenotype, remote sensing, agronomy, and related disciplines.
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Affiliation(s)
- Jiangchuan Fan
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
| | - Yinglun Li
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
| | - Shuan Yu
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
| | - Wenbo Gou
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
| | - Xinyu Guo
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
| | - Chunjiang Zhao
- Beijing Key Laboratory of Digital Plant,
National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China
- Beijing Research Center for Information Technology in Agriculture,
Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China
- China National Engineering Research Center for Information Technology in Agriculture (NERCITA), Beijing 100097, China
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92
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Choudhary P, Pramitha L, Aggarwal PR, Rana S, Vetriventhan M, Muthamilarasan M. Biotechnological interventions for improving the seed longevity in cereal crops: progress and prospects. Crit Rev Biotechnol 2023; 43:309-325. [PMID: 35443842 DOI: 10.1080/07388551.2022.2027863] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Seed longevity is a measure of the viability of seeds during long-term storage and is crucial for germplasm conservation and crop improvement programs. Also, longevity is an important trait for ensuring food and nutritional security. Thus, a better understanding of various factors regulating seed longevity is requisite to improve this trait and to minimize the genetic drift during the regeneration of germplasm. In particular, seed deterioration of cereal crops during storage adversely affects agricultural productivity and food security. The irreversible process of seed deterioration involves a complex interplay between different genes and regulatory pathways leading to: loss of DNA integrity, membrane damage, inactivation of storage enzymes and mitochondrial dysfunction. Identifying the genetic determinants of seed longevity and manipulating them using biotechnological tools hold the key to ensuring prolonged seed storage. Genetics and genomics approaches had identified several genomic regions regulating the longevity trait in major cereals such as: rice, wheat, maize and barley. However, very few studies are available in other Poaceae members, including millets. Deploying omics tools, including genomics, proteomics, metabolomics, and phenomics, and integrating the datasets will pinpoint the precise molecular determinants affecting the survivability of seeds. Given this, the present review enumerates the genetic factors regulating longevity and demonstrates the importance of integrated omics strategies to dissect the molecular machinery underlying seed deterioration. Further, the review provides a roadmap for deploying biotechnological approaches to manipulate the genes and genomic regions to develop improved cultivars with prolonged storage potential.
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Affiliation(s)
- Pooja Choudhary
- Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Lydia Pramitha
- School of Agriculture and Biosciences, Karunya Institute of Technology and Sciences, Coimbatore, India
| | - Pooja Rani Aggarwal
- Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Sumi Rana
- Department of Plant Sciences, School of Life Sciences, University of Hyderabad, Hyderabad, India
| | - Mani Vetriventhan
- International Crops Research Institute for the Semi-Arid Tropics, Patancheru, India
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93
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Liu Y, Yuan H, Zhao X, Fan C, Cheng M. Fast reconstruction method of three-dimension model based on dual RGB-D cameras for peanut plant. PLANT METHODS 2023; 19:17. [PMID: 36843020 PMCID: PMC9969713 DOI: 10.1186/s13007-023-00998-z] [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: 07/31/2022] [Accepted: 02/20/2023] [Indexed: 06/18/2023]
Abstract
BACKGROUND Plant shape and structure are important factors in peanut breeding research. Constructing a three-dimension (3D) model can provide an effective digital tool for comprehensive and quantitative analysis of peanut plant structure. Fast and accurate are always the goals of the plant 3D model reconstruction research. RESULTS We proposed a 3D reconstruction method based on dual RGB-D cameras for the peanut plant 3D model quickly and accurately. The two Kinect v2 were mirror symmetry placed on both sides of the peanut plant, and the point cloud data obtained were filtered twice to remove noise interference. After rotation and translation based on the corresponding geometric relationship, the point cloud acquired by the two Kinect v2 was converted to the same coordinate system and spliced into the 3D structure of the peanut plant. The experiment was conducted at various growth stages based on twenty potted peanuts. The plant traits' height, width, length, and volume were calculated through the reconstructed 3D models, and manual measurement was also carried out during the experiment processing. The accuracy of the 3D model was evaluated through a synthetic coefficient, which was generated by calculating the average accuracy of the four traits. The test result showed that the average accuracy of the reconstructed peanut plant 3D model by this method is 93.42%. A comparative experiment with the iterative closest point (ICP) algorithm, a widely used 3D modeling algorithm, was additionally implemented to test the rapidity of this method. The test result shows that the proposed method is 2.54 times faster with approximated accuracy compared to the ICP method. CONCLUSIONS The reconstruction method for the 3D model of the peanut plant described in this paper is capable of rapidly and accurately establishing a 3D model of the peanut plant while also meeting the modeling requirements for other species' breeding processes. This study offers a potential tool to further explore the 3D model for improving traits and agronomic qualities of plants.
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Affiliation(s)
- Yadong Liu
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071001, China
| | - Hongbo Yuan
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071001, China
| | - Xin Zhao
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071001, China
| | - Caihu Fan
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071001, China
| | - Man Cheng
- College of Mechanical and Electrical Engineering, Hebei Agricultural University, Baoding, 071001, China.
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94
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Abstract
Over the past decade, advances in plant genotyping have been critical in enabling the identification of genetic diversity, in understanding evolution, and in dissecting important traits in both crops and native plants. The widespread popularity of single-nucleotide polymorphisms (SNPs) has prompted significant improvements to SNP-based genotyping, including SNP arrays, genotyping by sequencing, and whole-genome resequencing. More recent approaches, including genotyping structural variants, utilizing pangenomes to capture species-wide genetic diversity and exploiting machine learning to analyze genotypic data sets, are pushing the boundaries of what plant genotyping can offer. In this chapter, we highlight these innovations and discuss how they will accelerate and advance future genotyping efforts.
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95
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Colpo A, Demaria S, Baldisserotto C, Pancaldi S, Brestič M, Živčak M, Ferroni L. Long-Term Alleviation of the Functional Phenotype in Chlorophyll-Deficient Wheat and Impact on Productivity: A Semi-Field Phenotyping Experiment. PLANTS (BASEL, SWITZERLAND) 2023; 12:822. [PMID: 36840171 PMCID: PMC9964019 DOI: 10.3390/plants12040822] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/09/2023] [Revised: 02/04/2023] [Accepted: 02/09/2023] [Indexed: 06/18/2023]
Abstract
Wheat mutants with a reduced chlorophyll synthesis are affected by a defective control of the photosynthetic electron flow, but tend to recover a wild-type phenotype. The sensitivity of some mutants to light fluctuations suggested that cultivation outdoors could significantly impact productivity. Six mutant lines of Triticum durum or Triticum aestivum with their respective wild-type cultivars were cultivated with a regular seasonal cycle (October-May) in a semi-field experiment. Leaf chlorophyll content and fluorescence parameters were analysed at the early (November) and late (May) developmental stages, and checked for correlation with morphometric and grain-production parameters. The alleviation of the phenotype severity concerned primarily the recovery of the photosynthetic-membrane functionality, but not the leaf chlorophyll content. Photosystem II (PSII) was less photoprotected in the mutants, but a moderate PSII photoinhibition could help control the electron flow into the chain. The accumulation of interchain electron carriers was a primary acclimative response towards the naturally fluctuating environment, maximally exploited by the mature durum-wheat mutants. The mutation itself and/or the energy-consuming compensatory mechanisms markedly influenced the plant morphogenesis, leading especially to reduced tillering, which in turn resulted in lower grain production per plant. Consistently with the interrelation between early photosynthetic phenotype and grain-yield per plant, chlorophyll-fluorescence indexes related to the level of photoprotective thermal dissipation (pNPQ), photosystem II antenna size (ABS/RC), and pool of electron carriers (Sm) are proposed as good candidates for the in-field phenotyping of chlorophyll-deficient wheat.
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Affiliation(s)
- Andrea Colpo
- Department of Environmental and Prevention Sciences, University of Ferrara, Corso Ercole I d’Este 32, 44121 Ferrara, Italy
- Institute of Plant and Environmental Sciences, Slovak University of Agriculture, Trieda A. Hlinku 2, 949 76 Nitra, Slovakia
| | - Sara Demaria
- Department of Environmental and Prevention Sciences, University of Ferrara, Corso Ercole I d’Este 32, 44121 Ferrara, Italy
| | - Costanza Baldisserotto
- Department of Environmental and Prevention Sciences, University of Ferrara, Corso Ercole I d’Este 32, 44121 Ferrara, Italy
| | - Simonetta Pancaldi
- Department of Environmental and Prevention Sciences, University of Ferrara, Corso Ercole I d’Este 32, 44121 Ferrara, Italy
| | - Marian Brestič
- Institute of Plant and Environmental Sciences, Slovak University of Agriculture, Trieda A. Hlinku 2, 949 76 Nitra, Slovakia
| | - Marek Živčak
- Institute of Plant and Environmental Sciences, Slovak University of Agriculture, Trieda A. Hlinku 2, 949 76 Nitra, Slovakia
| | - Lorenzo Ferroni
- Department of Environmental and Prevention Sciences, University of Ferrara, Corso Ercole I d’Este 32, 44121 Ferrara, Italy
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96
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Harfouche AL, Nakhle F, Harfouche AH, Sardella OG, Dart E, Jacobson D. A primer on artificial intelligence in plant digital phenomics: embarking on the data to insights journey. TRENDS IN PLANT SCIENCE 2023; 28:154-184. [PMID: 36167648 DOI: 10.1016/j.tplants.2022.08.021] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Revised: 08/22/2022] [Accepted: 08/25/2022] [Indexed: 06/16/2023]
Abstract
Artificial intelligence (AI) has emerged as a fundamental component of global agricultural research that is poised to impact on many aspects of plant science. In digital phenomics, AI is capable of learning intricate structure and patterns in large datasets. We provide a perspective and primer on AI applications to phenome research. We propose a novel human-centric explainable AI (X-AI) system architecture consisting of data architecture, technology infrastructure, and AI architecture design. We clarify the difference between post hoc models and 'interpretable by design' models. We include guidance for effectively using an interpretable by design model in phenomic analysis. We also provide directions to sources of tools and resources for making data analytics increasingly accessible. This primer is accompanied by an interactive online tutorial.
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Affiliation(s)
- Antoine L Harfouche
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy.
| | - Farid Nakhle
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy
| | - Antoine H Harfouche
- Unité de Formation et de Recherche en Sciences Économiques, Gestion, Mathématiques, et Informatique, Université Paris Nanterre, 92001 Nanterre, France
| | - Orlando G Sardella
- Department for Innovation in Biological, Agro-Food, and Forest Systems, University of Tuscia, Viterbo, VT 01100, Italy
| | - Eli Dart
- Energy Sciences Network (ESnet), Lawrence Berkeley National Laboratory, Berkeley, CA 94720, USA
| | - Daniel Jacobson
- Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA
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97
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Yang Z, Qin F. The battle of crops against drought: Genetic dissection and improvement. JOURNAL OF INTEGRATIVE PLANT BIOLOGY 2023; 65:496-525. [PMID: 36639908 DOI: 10.1111/jipb.13451] [Citation(s) in RCA: 25] [Impact Index Per Article: 25.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 01/12/2023] [Indexed: 06/17/2023]
Abstract
With ongoing global climate change, water scarcity-induced drought stress remains a major threat to agricultural productivity. Plants undergo a series of physiological and morphological changes to cope with drought stress, including stomatal closure to reduce transpiration and changes in root architecture to optimize water uptake. Combined phenotypic and multi-omics studies have recently identified a number of drought-related genetic resources in different crop species. The functional dissection of these genes using molecular techniques has enriched our understanding of drought responses in crops and has provided genetic targets for enhancing resistance to drought. Here, we review recent advances in the cloning and functional analysis of drought resistance genes and the development of technologies to mitigate the threat of drought to crop production.
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Affiliation(s)
- Zhirui Yang
- State Key Laboratory of Plant Environmental Resilience, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
| | - Feng Qin
- State Key Laboratory of Plant Environmental Resilience, College of Biological Sciences, China Agricultural University, Beijing, 100193, China
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98
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Zhang G, Zhou J, Peng Y, Tan Z, Zhang Y, Zhao H, Liu D, Liu X, Li L, Yu L, Jin C, Fang S, Shi J, Geng Z, Yang S, Chen G, Liu K, Yang Q, Feng H, Guo L, Yang W. High-throughput phenotyping-based quantitative trait loci mapping reveals the genetic architecture of the salt stress tolerance of Brassica napus. PLANT, CELL & ENVIRONMENT 2023; 46:549-566. [PMID: 36354160 DOI: 10.1111/pce.14485] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 03/01/2022] [Accepted: 04/26/2022] [Indexed: 06/16/2023]
Abstract
Salt stress is a major limiting factor that severely affects the survival and growth of crops. It is important to understand the salt stress tolerance ability of Brassica napus and explore the underlying related genetic resources. We used a high-throughput phenotyping platform to quantify 2111 image-based traits (i-traits) of a natural population under three different salt stress conditions and an intervarietal substitution line (ISL) population under nine different stress conditions to monitor and evaluate the salt stress tolerance of B. napus over time. We finally identified 928 high-quality i-traits associated with the salt stress tolerance of B. napus. Moreover, we mapped the salt stress-related loci in the natural population via a genome-wide association study and performed a linkage analysis associated with the ISL population, respectively. These results revealed 234 candidate genes associated with salt stress response, and two novel candidate genes, BnCKX5 and BnERF3, were experimentally verified to regulate the salt stress tolerance of B. napus. This study demonstrates the feasibility of using high-throughput phenotyping-based quantitative trait loci mapping to accurately and comprehensively quantify i-traits associated with B. napus. The mapped loci could be used for genomics-assisted breeding to genetically improve the salt stress tolerance of B. napus.
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Affiliation(s)
- Guofang Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Jinzhi Zhou
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Yan Peng
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Zengdong Tan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Yuting Zhang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Hu Zhao
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Dongxu Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Xiao Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Long Li
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Liangqian Yu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Cheng Jin
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Shuai Fang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Jiawei Shi
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Zedong Geng
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Shanjing Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Guoxing Chen
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Kede Liu
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Qingyong Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Hui Feng
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Liang Guo
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Wanneng Yang
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
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99
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Tills O, Holmes LA, Quinn E, Everett T, Truebano M, Spicer JI. Phenomics enables measurement of complex responses of developing animals to global environmental drivers. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 858:159555. [PMID: 36283519 DOI: 10.1016/j.scitotenv.2022.159555] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 09/29/2022] [Accepted: 10/14/2022] [Indexed: 06/16/2023]
Abstract
Phenomics offers technological advances for high-dimensional phenotyping, facilitating rapid, high-throughput assessment of physiological performance and has proven invaluable in global research challenges including drug discovery and food security. However, this rapidly growing discipline has remained largely inaccessible to the increasingly urgent challenge of assessing organismal functional sensitivity to global change drivers. Here, we investigate the response of an ecologically important marine invertebrate to multiple environmental drivers using Energy Proxy Traits (EPTs), a new approach for measuring complex phenotypes captured on video as a spectrum of energy levels across different temporal frequencies in fluctuating pixel values. We imaged three developmental stages of the common prawn Palaemon serratus at different salinities and temperatures, and measured EPTs and heart rate, a major proxy of physiological performance in ectotherms present across stages. Significant interactions were detected between temperature, developmental stage and salinity in frequency-specific energy levels. Despite cardiac activity being a significant contributor to the EPT spectra, treatment interactions were different from those observed on EPTs, highlighting additional phenotypic drivers of EPTs. Elevated temperature resulted in a shift of the EPT spectra towards higher frequency signals, indicating a reallocation of resources within the phenome. Using a non-linear dimensionality reduction, we interrogated the responses of EPT spectra in high-dimensional space. We discovered complex developmental-stage specific sensitivities, highlighting both the complexity of phenotypic responses, and the limits of using univariate approaches with pre-selected traits to assess responses to multiple global environmental drivers. EPTs are a high-dimensional, transferrable method of phenotyping, and are therefore highly relevant to addressing the current limitations of traditional methods of phenotyping applied to assessing biological sensitivity to drivers of global change. We predict that EPTs will become an important tool for indiscriminate phenotyping, transferrable between species, developmental stages and experimental designs.
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Affiliation(s)
- Oliver Tills
- Marine Biology and Ecology Research Centre, School of Biological and Marine Sciences, University of Plymouth, Devon PL4 8AA, United Kingdom.
| | - Luke A Holmes
- Marine Biology and Ecology Research Centre, School of Biological and Marine Sciences, University of Plymouth, Devon PL4 8AA, United Kingdom
| | - Elliot Quinn
- Marine Biology and Ecology Research Centre, School of Biological and Marine Sciences, University of Plymouth, Devon PL4 8AA, United Kingdom
| | - Tony Everett
- Marine Biology and Ecology Research Centre, School of Biological and Marine Sciences, University of Plymouth, Devon PL4 8AA, United Kingdom
| | - Manuela Truebano
- Marine Biology and Ecology Research Centre, School of Biological and Marine Sciences, University of Plymouth, Devon PL4 8AA, United Kingdom
| | - John I Spicer
- Marine Biology and Ecology Research Centre, School of Biological and Marine Sciences, University of Plymouth, Devon PL4 8AA, United Kingdom
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100
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Yan J, Wang X. Machine learning bridges omics sciences and plant breeding. TRENDS IN PLANT SCIENCE 2023; 28:199-210. [PMID: 36153276 DOI: 10.1016/j.tplants.2022.08.018] [Citation(s) in RCA: 21] [Impact Index Per Article: 21.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Revised: 08/15/2022] [Accepted: 08/23/2022] [Indexed: 06/16/2023]
Abstract
Some of the biological knowledge obtained from fundamental research will be implemented in applied plant breeding. To bridge basic research and breeding practice, machine learning (ML) holds great promise to translate biological knowledge and omics data into precision-designed plant breeding. Here, we review ML for multi-omics analysis in plants, including data dimensionality reduction, inference of gene-regulation networks, and gene discovery and prioritization. These applications will facilitate understanding trait regulation mechanisms and identifying target genes potentially applicable to knowledge-driven molecular design breeding. We also highlight applications of deep learning in plant phenomics and ML in genomic selection-assisted breeding, such as various ML algorithms that model the correlations among genotypes (genes), phenotypes (traits), and environments, to ultimately achieve data-driven genomic design breeding.
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Affiliation(s)
- Jun Yan
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing 100094, China
| | - Xiangfeng Wang
- National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing 100094, China; Frontiers Science Center for Molecular Design Breeding, China Agricultural University, Beijing 100094, China.
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