51
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de Jesus Colwell F, Souter J, Bryan GJ, Compton LJ, Boonham N, Prashar A. Development and Validation of Methodology for Estimating Potato Canopy Structure for Field Crop Phenotyping and Improved Breeding. FRONTIERS IN PLANT SCIENCE 2021; 12:612843. [PMID: 33643346 PMCID: PMC7902928 DOI: 10.3389/fpls.2021.612843] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 01/19/2021] [Indexed: 05/30/2023]
Abstract
Traditional phenotyping techniques have long been a bottleneck in breeding programs and genotype- phenotype association studies in potato, as these methods are labor-intensive and time consuming. In addition, depending on the trait measured and metric adopted, they suffer from varying degrees of user bias and inaccuracy, and hence these challenges have effectively prevented the execution of large-scale population-based field studies. This is true not only for commercial traits (e.g., yield, tuber size, and shape), but also for traits strongly associated with plant performance (e.g., canopy development, canopy architecture, and growth rates). This study demonstrates how the use of point cloud data obtained from low-cost UAV imaging can be used to create 3D surface models of the plant canopy, from which detailed and accurate data on plant height and its distribution, canopy ground cover and canopy volume can be obtained over the growing season. Comparison of the canopy datasets at different temporal points enabled the identification of distinct patterns of canopy development, including different patterns of growth, plant lodging, maturity and senescence. Three varieties are presented as exemplars. Variety Nadine presented the growth pattern of an early maturing variety, showing rapid initial growth followed by rapid onset of senescence and plant death. Varieties Bonnie and Bounty presented the pattern of intermediate to late maturing varieties, with Bonnie also showing early canopy lodging. The methodological approach used in this study may alleviate one of the current bottlenecks in the study of plant development, paving the way for an expansion in the scale of future genotype-phenotype association studies.
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Affiliation(s)
- Filipe de Jesus Colwell
- School of Natural Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Jock Souter
- Survey Solutions Scotland, Edinburgh, United Kingdom
| | | | - Lindsey J. Compton
- School of Biosciences, University of Birmingham, Birmingham, United Kingdom
| | - Neil Boonham
- School of Natural Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Ankush Prashar
- School of Natural Environmental Sciences, Newcastle University, Newcastle upon Tyne, United Kingdom
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52
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Thermal Stresses in Maize: Effects and Management Strategies. PLANTS 2021; 10:plants10020293. [PMID: 33557079 PMCID: PMC7913793 DOI: 10.3390/plants10020293] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 01/30/2021] [Accepted: 02/01/2021] [Indexed: 01/03/2023]
Abstract
Climate change can decrease the global maize productivity and grain quality. Maize crop requires an optimal temperature for better harvest productivity. A suboptimal temperature at any critical stage for a prolonged duration can negatively affect the growth and yield formation processes. This review discusses the negative impact of temperature extremes (high and low temperatures) on the morpho-physiological, biochemical, and nutritional traits of the maize crop. High temperature stress limits pollen viability and silks receptivity, leading to a significant reduction in seed setting and grain yield. Likewise, severe alterations in growth rate, photosynthesis, dry matter accumulation, cellular membranes, and antioxidant enzyme activities under low temperature collectively limit maize productivity. We also discussed various strategies with practical examples to cope with temperature stresses, including cultural practices, exogenous protectants, breeding climate-smart crops, and molecular genomics approaches. We reviewed that identified quantitative trait loci (QTLs) and genes controlling high- and low temperature stress tolerance in maize could be introgressed into otherwise elite cultivars to develop stress-tolerant cultivars. Genome editing has become a key tool for developing climate-resilient crops. Moreover, challenges to maize crop improvement such as lack of adequate resources for breeding in poor countries, poor communication among the scientists of developing and developed countries, problems in germplasm exchange, and high cost of advanced high-throughput phenotyping systems are discussed. In the end, future perspectives for maize improvement are discussed, which briefly include new breeding technologies such as transgene-free clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated (Cas)-mediated genome editing for thermo-stress tolerance in maize.
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53
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Emerging approaches to measure photosynthesis from the leaf to the ecosystem. Emerg Top Life Sci 2021; 5:261-274. [PMID: 33527993 PMCID: PMC8166339 DOI: 10.1042/etls20200292] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 01/12/2021] [Accepted: 01/14/2021] [Indexed: 12/03/2022]
Abstract
Measuring photosynthesis is critical for quantifying and modeling leaf to regional scale productivity of managed and natural ecosystems. This review explores existing and novel advances in photosynthesis measurements that are certain to provide innovative directions in plant science research. First, we address gas exchange approaches from leaf to ecosystem scales. Leaf level gas exchange is a mature method but recent improvements to the user interface and environmental controls of commercial systems have resulted in faster and higher quality data collection. Canopy chamber and micrometeorological methods have also become more standardized tools and have an advanced understanding of ecosystem functioning under a changing environment and through long time series data coupled with community data sharing. Second, we review proximal and remote sensing approaches to measure photosynthesis, including hyperspectral reflectance- and fluorescence-based techniques. These techniques have long been used with aircraft and orbiting satellites, but lower-cost sensors and improved statistical analyses are allowing these techniques to become applicable at smaller scales to quantify changes in the underlying biochemistry of photosynthesis. Within the past decade measurements of chlorophyll fluorescence from earth-orbiting satellites have measured Solar Induced Fluorescence (SIF) enabling estimates of global ecosystem productivity. Finally, we highlight that stronger interactions of scientists across disciplines will benefit our capacity to accurately estimate productivity at regional and global scales. Applying the multiple techniques outlined in this review at scales from the leaf to the globe are likely to advance understanding of plant functioning from the organelle to the ecosystem.
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54
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Abstract
Technological developments have revolutionized measurements on plant genotypes and phenotypes, leading to routine production of large, complex data sets. This has led to increased efforts to extract meaning from these measurements and to integrate various data sets. Concurrently, machine learning has rapidly evolved and is now widely applied in science in general and in plant genotyping and phenotyping in particular. Here, we review the application of machine learning in the context of plant science and plant breeding. We focus on analyses at different phenotype levels, from biochemical to yield, and in connecting genotypes to these. In this way, we illustrate how machine learning offers a suite of methods that enable researchers to find meaningful patterns in relevant plant data.
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Affiliation(s)
- Aalt Dirk Jan van Dijk
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen 6708 PB, the Netherlands
- Biometris, Department of Plant Sciences, Wageningen University and Research, Wageningen 6708 PB, the Netherlands
| | - Gert Kootstra
- Farm Technology, Department of Plant Sciences, Wageningen University and Research, Wageningen 6708 PB, the Netherlands
| | - Willem Kruijer
- Biometris, Department of Plant Sciences, Wageningen University and Research, Wageningen 6708 PB, the Netherlands
| | - Dick de Ridder
- Bioinformatics Group, Department of Plant Sciences, Wageningen University and Research, Wageningen 6708 PB, the Netherlands
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55
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Li D, Quan C, Song Z, Li X, Yu G, Li C, Muhammad A. High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field. Front Bioeng Biotechnol 2021; 8:623705. [PMID: 33520974 PMCID: PMC7838587 DOI: 10.3389/fbioe.2020.623705] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Accepted: 12/15/2020] [Indexed: 11/13/2022] Open
Abstract
Food scarcity, population growth, and global climate change have propelled crop yield growth driven by high-throughput phenotyping into the era of big data. However, access to large-scale phenotypic data has now become a critical barrier that phenomics urgently must overcome. Fortunately, the high-throughput plant phenotyping platform (HT3P), employing advanced sensors and data collection systems, can take full advantage of non-destructive and high-throughput methods to monitor, quantify, and evaluate specific phenotypes for large-scale agricultural experiments, and it can effectively perform phenotypic tasks that traditional phenotyping could not do. In this way, HT3Ps are novel and powerful tools, for which various commercial, customized, and even self-developed ones have been recently introduced in rising numbers. Here, we review these HT3Ps in nearly 7 years from greenhouses and growth chambers to the field, and from ground-based proximal phenotyping to aerial large-scale remote sensing. Platform configurations, novelties, operating modes, current developments, as well the strengths and weaknesses of diverse types of HT3Ps are thoroughly and clearly described. Then, miscellaneous combinations of HT3Ps for comparative validation and comprehensive analysis are systematically present, for the first time. Finally, we consider current phenotypic challenges and provide fresh perspectives on future development trends of HT3Ps. This review aims to provide ideas, thoughts, and insights for the optimal selection, exploitation, and utilization of HT3Ps, and thereby pave the way to break through current phenotyping bottlenecks in botany.
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Affiliation(s)
- Daoliang Li
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Chaoqun Quan
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Zhaoyang Song
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Xiang Li
- Department of Psychology, College of Education, Hubei University, Wuhan, China
| | - Guanghui Yu
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Cheng Li
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- Beijing Engineering and Technology Research Centre for Internet of Things in Agriculture, China Agricultural University, Beijing, China
- China-EU Center for Information and Communication Technologies in Agriculture, China Agriculture University, Beijing, China
- Key Laboratory of Agriculture Information Acquisition Technology, Ministry of Agriculture, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
| | - Akhter Muhammad
- National Innovation Center for Digital Fishery, China Agricultural University, Beijing, China
- College of Information and Electrical Engineering, China Agricultural University, Beijing, China
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56
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Yang Y, Saand MA, Huang L, Abdelaal WB, Zhang J, Wu Y, Li J, Sirohi MH, Wang F. Applications of Multi-Omics Technologies for Crop Improvement. FRONTIERS IN PLANT SCIENCE 2021; 12:563953. [PMID: 34539683 PMCID: PMC8446515 DOI: 10.3389/fpls.2021.563953] [Citation(s) in RCA: 57] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Accepted: 08/06/2021] [Indexed: 05/19/2023]
Abstract
Multiple "omics" approaches have emerged as successful technologies for plant systems over the last few decades. Advances in next-generation sequencing (NGS) have paved a way for a new generation of different omics, such as genomics, transcriptomics, and proteomics. However, metabolomics, ionomics, and phenomics have also been well-documented in crop science. Multi-omics approaches with high throughput techniques have played an important role in elucidating growth, senescence, yield, and the responses to biotic and abiotic stress in numerous crops. These omics approaches have been implemented in some important crops including wheat (Triticum aestivum L.), soybean (Glycine max), tomato (Solanum lycopersicum), barley (Hordeum vulgare L.), maize (Zea mays L.), millet (Setaria italica L.), cotton (Gossypium hirsutum L.), Medicago truncatula, and rice (Oryza sativa L.). The integration of functional genomics with other omics highlights the relationships between crop genomes and phenotypes under specific physiological and environmental conditions. The purpose of this review is to dissect the role and integration of multi-omics technologies for crop breeding science. We highlight the applications of various omics approaches, such as genomics, transcriptomics, proteomics, metabolomics, phenomics, and ionomics, and the implementation of robust methods to improve crop genetics and breeding science. Potential challenges that confront the integration of multi-omics with regard to the functional analysis of genes and their networks as well as the development of potential traits for crop improvement are discussed. The panomics platform allows for the integration of complex omics to construct models that can be used to predict complex traits. Systems biology integration with multi-omics datasets can enhance our understanding of molecular regulator networks for crop improvement. In this context, we suggest the integration of entire omics by employing the "phenotype to genotype" and "genotype to phenotype" concept. Hence, top-down (phenotype to genotype) and bottom-up (genotype to phenotype) model through integration of multi-omics with systems biology may be beneficial for crop breeding improvement under conditions of environmental stresses.
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Affiliation(s)
- Yaodong Yang
- Hainan Key Laboratory of Tropical Oil Crops Biology/Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang, China
- *Correspondence: Yaodong Yang
| | - Mumtaz Ali Saand
- Hainan Key Laboratory of Tropical Oil Crops Biology/Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang, China
- Department of Botany, Shah Abdul Latif University, Khairpur, Pakistan
| | - Liyun Huang
- Hainan Key Laboratory of Tropical Oil Crops Biology/Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang, China
| | - Walid Badawy Abdelaal
- Hainan Key Laboratory of Tropical Oil Crops Biology/Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang, China
| | - Jun Zhang
- Hainan Key Laboratory of Tropical Oil Crops Biology/Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang, China
| | - Yi Wu
- Hainan Key Laboratory of Tropical Oil Crops Biology/Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang, China
| | - Jing Li
- Hainan Key Laboratory of Tropical Oil Crops Biology/Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang, China
| | | | - Fuyou Wang
- Hainan Key Laboratory of Tropical Oil Crops Biology/Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang, China
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57
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Singh A, Jones S, Ganapathysubramanian B, Sarkar S, Mueller D, Sandhu K, Nagasubramanian K. Challenges and Opportunities in Machine-Augmented Plant Stress Phenotyping. TRENDS IN PLANT SCIENCE 2021; 26:53-69. [PMID: 32830044 DOI: 10.1016/j.tplants.2020.07.010] [Citation(s) in RCA: 49] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/18/2019] [Revised: 07/15/2020] [Accepted: 07/23/2020] [Indexed: 05/06/2023]
Abstract
Plant stress phenotyping is essential to select stress-resistant varieties and develop better stress-management strategies. Standardization of visual assessments and deployment of imaging techniques have improved the accuracy and reliability of stress assessment in comparison with unaided visual measurement. The growing capabilities of machine learning (ML) methods in conjunction with image-based phenotyping can extract new insights from curated, annotated, and high-dimensional datasets across varied crops and stresses. We propose an overarching strategy for utilizing ML techniques that methodically enables the application of plant stress phenotyping at multiple scales across different types of stresses, program goals, and environments.
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Affiliation(s)
- Arti Singh
- Department of Agronomy, Iowa State University, Ames, IA, USA.
| | - Sarah Jones
- Department of Agronomy, Iowa State University, Ames, IA, USA
| | | | - Soumik Sarkar
- Department of Mechanical Engineering, Iowa State University, Ames, IA, USA
| | - Daren Mueller
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, USA
| | - Kulbir Sandhu
- Department of Agronomy, Iowa State University, Ames, IA, USA
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58
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Deery DM, Jones HG. Field Phenomics: Will It Enable Crop Improvement? PLANT PHENOMICS (WASHINGTON, D.C.) 2021; 2021:9871989. [PMID: 34549194 PMCID: PMC8433881 DOI: 10.34133/2021/9871989] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Accepted: 08/14/2021] [Indexed: 05/19/2023]
Abstract
Field phenomics has been identified as a promising enabling technology to assist plant breeders with the development of improved cultivars for farmers. Yet, despite much investment, there are few examples demonstrating the application of phenomics within a plant breeding program. We review recent progress in field phenomics and highlight the importance of targeting breeders' needs, rather than perceived technology needs, through developing and enhancing partnerships between phenomics researchers and plant breeders.
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Affiliation(s)
| | - Hamlyn G. Jones
- CSIRO Agriculture and Food, Canberra, ACT, Australia
- Division of Plant Sciences, University of Dundee, UK
- School of Agriculture and Environment, University of Western Australia, Australia
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59
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Abstract
In the last few years, large efforts have been made to develop new methods to optimize stress detection in crop fields. Thus, plant phenotyping based on imaging techniques has become an essential tool in agriculture. In particular, leaf temperature is a valuable indicator of the physiological status of plants, responding to both biotic and abiotic stressors. Often combined with other imaging sensors and data-mining techniques, thermography is crucial in the implementation of a more automatized, precise and sustainable agriculture. However, thermal data need some corrections related to the environmental and measuring conditions in order to achieve a correct interpretation of the data. This review focuses on the state of the art of thermography applied to the detection of biotic stress. The work will also revise the most important abiotic stress factors affecting the measurements as well as practical issues that need to be considered in order to implement this technique, particularly at the field scale.
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60
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Image-Based High-Throughput Phenotyping of Cereals Early Vigor and Weed-Competitiveness Traits. REMOTE SENSING 2020. [DOI: 10.3390/rs12233877] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Cereals grains are the prime component of the human diet worldwide. To promote food security and sustainability, new approaches to non-chemical weed control are needed. Early vigor cultivars with enhanced weed-competitiveness ability are a potential tool, nonetheless, the introduction of such trait in breeding may be a long and labor-intensive process. Here, two image-driven plant phenotyping methods were evaluated to facilitate effective and accurate selection for early vigor in cereals. For that purpose, two triticale genotypes differentiating in vigor and growth rate early in the season were selected as model plants: X-1010 (high) and Triticale1 (low). Two modeling approaches, 2-D and 3-D, were applied on the plants offering an evaluation of various morphological growth parameters for the triticale canopy development, under controlled and field conditions. The morphological advantage of X-1010 was observed only at the initial growth stages, which was reflected by significantly higher growth parameter values compared to the Triticale1 genotype. Both modeling approaches were sensitive enough to detect phenotypic differences in growth as early as 21 days after sowing. All growth parameters indicated a faster early growth of X-1010. However, the 2-D related parameter [projected shoot area (PSA)] is the most available one that can be extracted via end user-friendly imaging equipment. PSA provided adequate indication for the triticale early growth under weed-competition conditions and for the improved weed-competition ability. The adequate phenotyping ability for early growth and competition was robust under controlled and field conditions. PSA can be extracted from close and remote sensing platforms, thus, facilitate high throughput screening. Overall, the results of this study may improve cereal breeding for early vigor and weed-competitiveness.
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61
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Lück S, Strickert M, Lorbeer M, Melchert F, Backhaus A, Kilias D, Seiffert U, Douchkov D. "Macrobot": An Automated Segmentation-Based System for Powdery Mildew Disease Quantification. PLANT PHENOMICS (WASHINGTON, D.C.) 2020; 2020:5839856. [PMID: 33313559 PMCID: PMC7706317 DOI: 10.34133/2020/5839856] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 07/27/2020] [Indexed: 05/25/2023]
Abstract
Managing plant diseases is increasingly difficult due to reasons such as intensifying the field production, climatic change-driven expansion of pests, redraw and loss of effectiveness of pesticides, rapid breakdown of the disease resistance in the field, and other factors. The substantial progress in genomics of both plants and pathogens, achieved in the last decades, has the potential to counteract this negative trend, however, only when the genomic data is supported by relevant phenotypic data that allows linking the genomic information to specific traits. We have developed a set of methods and equipment and combined them into a "Macrophenomics facility." The pipeline has been optimized for the quantification of powdery mildew infection symptoms on wheat and barley, but it can be adapted to other diseases and host plants. The Macrophenomics pipeline scores the visible powdery mildew disease symptoms, typically 5-7 days after inoculation (dai), in a highly automated manner. The system can precisely and reproducibly quantify the percentage of the infected leaf area with a theoretical throughput of up to 10000 individual samples per day, making it appropriate for phenotyping of large germplasm collections and crossing populations.
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Affiliation(s)
- Stefanie Lück
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Correnstr. 3, 06466 Seeland, Germany
| | - Marc Strickert
- Physics Institute II, University of Giessen, Heinrich-Buff-Ring 16, 35392 Giessen, Germany
| | - Maximilian Lorbeer
- Julius Kühn Institute for National and International Plant Health, Messeweg 11/12, 38104 Braunschweig, Germany
| | - Friedrich Melchert
- Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstr. 22, 39106 Magdeburg, Germany
| | - Andreas Backhaus
- Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstr. 22, 39106 Magdeburg, Germany
| | - David Kilias
- Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstr. 22, 39106 Magdeburg, Germany
| | - Udo Seiffert
- Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstr. 22, 39106 Magdeburg, Germany
| | - Dimitar Douchkov
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Correnstr. 3, 06466 Seeland, Germany
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62
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McCouch S, Navabi ZK, Abberton M, Anglin NL, Barbieri RL, Baum M, Bett K, Booker H, Brown GL, Bryan GJ, Cattivelli L, Charest D, Eversole K, Freitas M, Ghamkhar K, Grattapaglia D, Henry R, Valadares Inglis MC, Islam T, Kehel Z, Kersey PJ, King GJ, Kresovich S, Marden E, Mayes S, Ndjiondjop MN, Nguyen HT, Paiva SR, Papa R, Phillips PWB, Rasheed A, Richards C, Rouard M, Amstalden Sampaio MJ, Scholz U, Shaw PD, Sherman B, Staton SE, Stein N, Svensson J, Tester M, Montenegro Valls JF, Varshney R, Visscher S, von Wettberg E, Waugh R, Wenzl P, Rieseberg LH. Mobilizing Crop Biodiversity. MOLECULAR PLANT 2020; 13:1341-1344. [PMID: 32835887 DOI: 10.1016/j.molp.2020.08.011] [Citation(s) in RCA: 32] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2020] [Revised: 08/19/2020] [Accepted: 08/19/2020] [Indexed: 05/10/2023]
Affiliation(s)
- Susan McCouch
- Plant Breeding and Genetics, School of Integrated Plant Sciences, Cornell University, Ithaca, NY, 14853, USA
| | - Zahra Katy Navabi
- DivSeek, Global Institute for Food Security, 110 Gymnasium Place, University of Saskatchewan, Saskatoon, SK, S7N 0W9, Canada; Global Institute for Food Security, 110 Gymnasium Place, University of Saskatchewan, Saskatoon, SK, S7N 4J8, Canada
| | - Michael Abberton
- International Institute of Tropical Agriculture (IITA), PMB 5320, Oyo Rd, Ibadan, Nigeria
| | - Noelle L Anglin
- International Potato Center (CIP) 1895 Avenida La Molina, Lima Peru 12, Lima 15023, Peru
| | - Rosa Lia Barbieri
- Embrapa Genetic Resources and Biotechnology, Parque Estação Biológica, Final Av W5 Norte, Caixa Postal 02372, 70770-917 - Brasília DF, Brazil
| | - Michael Baum
- International Center for Agricultural Research in the Dry Areas (ICARDA), Station Exp. INRA-Quich. Rue Hafiane Cherkaoui. Agdal. Rabat - Instituts, 10111, Rabat, Morocco
| | - Kirstin Bett
- Department of Plant Sciences, University of Saskatchewan, 51 Campus Dr., Saskatoon, SK S7N 5A8, Canada
| | - Helen Booker
- Department of Plant Agriculture, University of Guelph, Rm 316, Crop Science Bldg, 50 Stone Rd E, Guelph, ON N1G 2W1, Canada
| | - Gerald L Brown
- Genome Prairie, 111 Research Drive, Suite 101, Saskatoon, SK, S7N 3R2, Canada
| | - Glenn J Bryan
- The James Hutton Institute, Errol Road, Invergowrie, Dundee, DD2 5DA, UK
| | - Luigi Cattivelli
- CREA, Research Centre for Genomics and Bioinformatics, via San Protaso 302, Fiorenzuola d'Arda, 29017, Italy
| | - David Charest
- Genome British Columbia, 400-575 West 8th Avenue, Vancouver, BC, V5Z 0C4, Canada
| | - Kellye Eversole
- International Wheat Genome Sequencing Consortium, 2841 NE Marywood Ct, Lee's Summit, MO, 64086, USA
| | - Marcelo Freitas
- Embrapa Genetic Resources and Biotechnology, Parque Estação Biológica, Final Av W5 Norte, Caixa Postal 02372, 70770-917 - Brasília DF, Brazil
| | - Kioumars Ghamkhar
- Forage Science, Grasslands Research Centre, AgResearch, Palmerston North, 4410, New Zealand
| | - Dario Grattapaglia
- Embrapa Genetic Resources and Biotechnology, Parque Estação Biológica, Final Av W5 Norte, Caixa Postal 02372, 70770-917 - Brasília DF, Brazil
| | - Robert Henry
- Queensland Alliance for Agriculture and Food Innovation, University of Queensland, Brisbane, QLD 4072, Australia
| | - Maria Cleria Valadares Inglis
- Embrapa Genetic Resources and Biotechnology, Parque Estação Biológica, Final Av W5 Norte, Caixa Postal 02372, 70770-917 - Brasília DF, Brazil
| | - Tofazzal Islam
- Institute of Biotechnology and Genetic Engineering (IBGE), Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur 1706, Bangladesh
| | - Zakaria Kehel
- International Center for Agricultural Research in the Dry Areas (ICARDA), Station Exp. INRA-Quich. Rue Hafiane Cherkaoui. Agdal. Rabat - Instituts, 10111, Rabat, Morocco
| | - Paul J Kersey
- Royal Botanic Gardens, Kew, Richmond, Surrey, TW9 3AE, UK
| | - Graham J King
- Southern Cross University, PO Box 157, Lismore, NSW 2480, Australia
| | - Stephen Kresovich
- Feed the Future Innovation Lab for Crop Improvement, 431 Weill Hall, Cornell University, Ithaca, NY, 14853, USA
| | - Emily Marden
- Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, BC V6R 2A5, Canada
| | - Sean Mayes
- Crops For the Future (UK) CIC 76-80 Baddow Road, Chelmsford, Essex, CM2 7PJ, UK
| | - Marie Noelle Ndjiondjop
- Africa Rice Center (AfricaRice), Mbe Research Station, Bouaké, 01 BP 2511 Bouaké, Côte d'Ivoire
| | - Henry T Nguyen
- University of Missouri, Division of Plant Sciences, 25 Agriculture Lab Bldg, College of Agriculture, Food and Natural Resources, University of Missouri, Columbia, MO 65211, USA
| | - Samuel Rezende Paiva
- Embrapa Genetic Resources and Biotechnology, Parque Estação Biológica, Final Av W5 Norte, Caixa Postal 02372, 70770-917 - Brasília DF, Brazil
| | - Roberto Papa
- Università Politecnica delle Marche, D3A-Dipartimento di Scienze Agrarie, Alimentari e Ambientali, Via Brecce Bianche, 60131, Ancona, Italy
| | - Peter W B Phillips
- Johnson Shoyama Graduate School of Public Policy, University of Saskatchewan, 101 Diefenbaker Place, Saskatoon, S7N 5B8, Canada
| | - Awais Rasheed
- CIMMYT-China office, Beijing 100081, Beijing, P.R. China
| | - Christopher Richards
- USDA-ARS National Laboratory for Genetic Resources Preservation, 1111 South Mason St, Fort Collins, CO, 80521, USA
| | - Mathieu Rouard
- Bioversity International, Parc Scientifique Agropolis II, 34397, Montpellier, Cedex 5, France
| | - Maria Jose Amstalden Sampaio
- Embrapa Genetic Resources and Biotechnology, Parque Estação Biológica, Final Av W5 Norte, Caixa Postal 02372, 70770-917 - Brasília DF, Brazil
| | - Uwe Scholz
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Corrensstr. 3, D-06466 Seeland, Germany
| | - Paul D Shaw
- The James Hutton Institute, Errol Road, Invergowrie, Dundee, DD2 5DA, UK
| | - Brad Sherman
- Law School, University of Queensland, St Lucia, QLD, 4072, Australia
| | - S Evan Staton
- Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, BC V6R 2A5, Canada
| | - Nils Stein
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Gatersleben, Corrensstr. 3, D-06466 Seeland, Germany; CiBreed - Center for Integrated Breeding Research, Department of Crop Sciences, Georg-August University Göttingen, Von Siebold Straße 8, D-37075 Göttingen, Germany
| | | | - Mark Tester
- King Abdullah University of Science & Technology (KAUST), Thuwal, 23955-6900, Saudi Arabia
| | - Jose Francisco Montenegro Valls
- Embrapa Genetic Resources and Biotechnology, Parque Estação Biológica, Final Av W5 Norte, Caixa Postal 02372, 70770-917 - Brasília DF, Brazil
| | - Rajeev Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru - 502 324, Telangana State, India
| | - Stephen Visscher
- Global Institute for Food Security, 110 Gymnasium Place, University of Saskatchewan, Saskatoon, SK, S7N 4J8, Canada
| | - Eric von Wettberg
- University of Vermont, 63 Carrigan Drive, Jeffords Hall, Burlington, VT, 05405, USA
| | - Robbie Waugh
- The James Hutton Institute, Errol Road, Invergowrie, Dundee, DD2 5DA, UK; School of Agriculture and Wine & Waite Research Institute, University of Adelaide, Waite Campus, Glen Osmond, SA, 5064, Australia
| | - Peter Wenzl
- Centro Internacional de Agricultura Tropical (CIAT), Km 17 Recta Cali-Palmira, 763537 Cali, Colombia
| | - Loren H Rieseberg
- Department of Botany and Biodiversity Research Centre, University of British Columbia, Vancouver, BC V6R 2A5, Canada.
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Li Z, Guo R, Li M, Chen Y, Li G. A review of computer vision technologies for plant phenotyping. COMPUTERS AND ELECTRONICS IN AGRICULTURE 2020; 176:105672. [PMID: 0 DOI: 10.1016/j.compag.2020.105672] [Citation(s) in RCA: 61] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/27/2023]
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Abstract
Wheat was one of the first grain crops domesticated by humans and remains among the major contributors to the global calorie and protein budget. The rapidly expanding world population demands further enhancement of yield and performance of wheat. Phenotypic information has historically been instrumental in wheat breeding for improved traits. In the last two decades, a steadily growing collection of tools and imaging software have given us the ability to quantify shoot, root, and seed traits with progressively increasing accuracy and throughput. This review discusses challenges and advancements in image analysis platforms for wheat phenotyping at the organ level. Perspectives on how these collective phenotypes can inform basic research on understanding wheat physiology and breeding for wheat improvement are also provided.
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Nguyen GN, Norton SL. Genebank Phenomics: A Strategic Approach to Enhance Value and Utilization of Crop Germplasm. PLANTS (BASEL, SWITZERLAND) 2020; 9:E817. [PMID: 32610615 PMCID: PMC7411623 DOI: 10.3390/plants9070817] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2020] [Revised: 06/25/2020] [Accepted: 06/26/2020] [Indexed: 02/07/2023]
Abstract
Genetically diverse plant germplasm stored in ex-situ genebanks are excellent resources for breeding new high yielding and sustainable crop varieties to ensure future food security. Novel alleles have been discovered through routine genebank activities such as seed regeneration and characterization, with subsequent utilization providing significant genetic gains and improvements for the selection of favorable traits, including yield, biotic, and abiotic resistance. Although some genebanks have implemented cost-effective genotyping technologies through advances in DNA technology, the adoption of modern phenotyping is lagging. The introduction of advanced phenotyping technologies in recent decades has provided genebank scientists with time and cost-effective screening tools to obtain valuable phenotypic data for more traits on large germplasm collections during routine activities. The utilization of these phenotyping tools, coupled with high-throughput genotyping, will accelerate the use of genetic resources and fast-track the development of more resilient food crops for the future. In this review, we highlight current digital phenotyping methods that can capture traits during annual seed regeneration to enrich genebank phenotypic datasets. Next, we describe strategies for the collection and use of phenotypic data of specific traits for downstream research using high-throughput phenotyping technology. Finally, we examine the challenges and future perspectives of genebank phenomics.
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Affiliation(s)
- Giao N. Nguyen
- Australian Grains Genebank, Agriculture Victoria, 110 Natimuk Road, Horsham 3400, Australia;
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66
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Selvaraj MG, Valderrama M, Guzman D, Valencia M, Ruiz H, Acharjee A. Machine learning for high-throughput field phenotyping and image processing provides insight into the association of above and below-ground traits in cassava ( Manihot esculenta Crantz). PLANT METHODS 2020; 16:87. [PMID: 32549903 PMCID: PMC7296968 DOI: 10.1186/s13007-020-00625-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/19/2020] [Accepted: 05/28/2020] [Indexed: 05/08/2023]
Abstract
BACKGROUND Rapid non-destructive measurements to predict cassava root yield over the full growing season through large numbers of germplasm and multiple environments is a huge challenge in Cassava breeding programs. As opposed to waiting until the harvest season, multispectral imagery using unmanned aerial vehicles (UAV) are capable of measuring the canopy metrics and vegetation indices (VIs) traits at different time points of the growth cycle. This resourceful time series aerial image processing with appropriate analytical framework is very important for the automatic extraction of phenotypic features from the image data. Many studies have demonstrated the usefulness of advanced remote sensing technologies coupled with machine learning (ML) approaches for accurate prediction of valuable crop traits. Until now, Cassava has received little to no attention in aerial image-based phenotyping and ML model testing. RESULTS To accelerate image processing, an automated image-analysis framework called CIAT Pheno-i was developed to extract plot level vegetation indices/canopy metrics. Multiple linear regression models were constructed at different key growth stages of cassava, using ground-truth data and vegetation indices obtained from a multispectral sensor. Henceforth, the spectral indices/features were combined to develop models and predict cassava root yield using different Machine learning techniques. Our results showed that (1) Developed CIAT pheno-i image analysis framework was found to be easier and more rapid than manual methods. (2) The correlation analysis of four phenological stages of cassava revealed that elongation (EL) and late bulking (LBK) were the most useful stages to estimate above-ground biomass (AGB), below-ground biomass (BGB) and canopy height (CH). (3) The multi-temporal analysis revealed that cumulative image feature information of EL + early bulky (EBK) stages showed a higher significant correlation (r = 0.77) for Green Normalized Difference Vegetation indices (GNDVI) with BGB than individual time points. Canopy height measured on the ground correlated well with UAV (CHuav)-based measurements (r = 0.92) at late bulking (LBK) stage. Among different image features, normalized difference red edge index (NDRE) data were found to be consistently highly correlated (r = 0.65 to 0.84) with AGB at LBK stage. (4) Among the four ML algorithms used in this study, k-Nearest Neighbours (kNN), Random Forest (RF) and Support Vector Machine (SVM) showed the best performance for root yield prediction with the highest accuracy of R2 = 0.67, 0.66 and 0.64, respectively. CONCLUSION UAV platforms, time series image acquisition, automated image analytical framework (CIAT Pheno-i), and key vegetation indices (VIs) to estimate phenotyping traits and root yield described in this work have great potential for use as a selection tool in the modern cassava breeding programs around the world to accelerate germplasm and varietal selection. The image analysis software (CIAT Pheno-i) developed from this study can be widely applicable to any other crop to extract phenotypic information rapidly.
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Affiliation(s)
| | - Manuel Valderrama
- International Center for Tropical Agriculture (CIAT), A.A. 6713 Cali, Colombia
| | - Diego Guzman
- International Center for Tropical Agriculture (CIAT), A.A. 6713 Cali, Colombia
| | - Milton Valencia
- International Center for Tropical Agriculture (CIAT), A.A. 6713 Cali, Colombia
| | - Henry Ruiz
- Department of Soil and Crop Sciences, Texas A&M University, College Station, TX USA
| | - Animesh Acharjee
- College of Medical and Dental Sciences, Institute of Cancer and Genomic Sciences, Centre for Computational Biology, University of Birmingham, Birmingham, B15 2TT UK
- Institute of Translational Medicine, University Hospitals Birmingham NHS Foundation Trust, Birmingham, B15 2TT UK
- NIHR Surgical Reconstruction and Microbiology Research Centre, University Hospital Birmingham, Birmingham, B15 2WB UK
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Performances Evaluation of a Low-Cost Platform for High-Resolution Plant Phenotyping. SENSORS 2020; 20:s20113150. [PMID: 32498361 PMCID: PMC7308841 DOI: 10.3390/s20113150] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 05/28/2020] [Accepted: 05/30/2020] [Indexed: 12/28/2022]
Abstract
This study aims to test the performances of a low-cost and automatic phenotyping platform, consisting of a Red-Green-Blue (RGB) commercial camera scanning objects on rotating plates and the reconstruction of main plant phenotypic traits via the structure for motion approach (SfM). The precision of this platform was tested in relation to three-dimensional (3D) models generated from images of potted maize, tomato and olive tree, acquired at a different frequency (steps of 4°, 8° and 12°) and quality (4.88, 6.52 and 9.77 µm/pixel). Plant and organs heights, angles and areas were extracted from the 3D models generated for each combination of these factors. Coefficient of determination (R2), relative Root Mean Square Error (rRMSE) and Akaike Information Criterion (AIC) were used as goodness-of-fit indexes to compare the simulated to the observed data. The results indicated that while the best performances in reproducing plant traits were obtained using 90 images at 4.88 µm/pixel (R2 = 0.81, rRMSE = 9.49% and AIC = 35.78), this corresponded to an unviable processing time (from 2.46 h to 28.25 h for herbaceous plants and olive trees, respectively). Conversely, 30 images at 4.88 µm/pixel resulted in a good compromise between a reliable reconstruction of considered traits (R2 = 0.72, rRMSE = 11.92% and AIC = 42.59) and processing time (from 0.50 h to 2.05 h for herbaceous plants and olive trees, respectively). In any case, the results pointed out that this input combination may vary based on the trait under analysis, which can be more or less demanding in terms of input images and time according to the complexity of its shape (R2 = 0.83, rRSME = 10.15% and AIC = 38.78). These findings highlight the reliability of the developed low-cost platform for plant phenotyping, further indicating the best combination of factors to speed up the acquisition and elaboration process, at the same time minimizing the bias between observed and simulated data.
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68
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Reynolds M, Chapman S, Crespo-Herrera L, Molero G, Mondal S, Pequeno DNL, Pinto F, Pinera-Chavez FJ, Poland J, Rivera-Amado C, Saint Pierre C, Sukumaran S. Breeder friendly phenotyping. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2020; 295:110396. [PMID: 32534615 DOI: 10.1016/j.plantsci.2019.110396] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/23/2019] [Revised: 12/12/2019] [Accepted: 12/26/2019] [Indexed: 05/18/2023]
Abstract
The word phenotyping can nowadays invoke visions of a drone or phenocart moving swiftly across research plots collecting high-resolution data sets on a wide array of traits. This has been made possible by recent advances in sensor technology and data processing. Nonetheless, more comprehensive often destructive phenotyping still has much to offer in breeding as well as research. This review considers the 'breeder friendliness' of phenotyping within three main domains: (i) the 'minimum data set', where being 'handy' or accessible and easy to collect and use is paramount, visual assessment often being preferred; (ii) the high throughput phenotyping (HTP), relatively new for most breeders, and requiring significantly greater investment with technical hurdles for implementation and a steeper learning curve than the minimum data set; (iii) detailed characterization or 'precision' phenotyping, typically customized for a set of traits associated with a target environment and requiring significant time and resources. While having been the subject of debate in the past, extra investment for phenotyping is becoming more accepted to capitalize on recent developments in crop genomics and prediction models, that can be built from the high-throughput and detailed precision phenotypes. This review considers different contexts for phenotyping, including breeding, exploration of genetic resources, parent building and translational research to deliver other new breeding resources, and how the different categories of phenotyping listed above apply to each. Some of the same tools and rules of thumb apply equally well to phenotyping for genetic analysis of complex traits and gene discovery.
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Affiliation(s)
| | - Scott Chapman
- CISRO Agriculture and Food, The University of Queensland, Australia
| | | | - Gemma Molero
- International Maize and Wheat Improvement Centre, Mexico
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69
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A Hyperspectral-Physiological Phenomics System: Measuring Diurnal Transpiration Rates and Diurnal Reflectance. REMOTE SENSING 2020. [DOI: 10.3390/rs12091493] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
A novel hyperspectral-physiological system that monitors plants dynamic response to abiotic alterations was developed. The system is a sensor-to-plant platform which can determine the optimal time of day during which physiological traits can be successfully identified via spectral means. The directly measured traits include momentary and daily transpiration rates throughout the daytime and daily and periodical plant weight loss and gain. The system monitored and evaluated pepper plants response to varying levels of potassium fertilization. Significant momentary transpiration rates differences were found between the treatments during 07:00–10:00 and 14:00–17:00. The simultaneous frequently measured high-resolution spectral data provided the means to correlate the two measured data sets. Significant correlation coefficients between the spectra and momentary transpiration rates resulted with a selection of three bands (ρ523, ρ697 and ρ818nm) that were used to capture transpiration rate differences using a normalized difference formula during the morning, noon and the afternoon. These differences also indicated that the best results are not always obtained when spectral (remote or proximal) measurements are typically preformed around noon (when solar illumination is the highest). Valuable information can be obtained when the spectral measurements are timed according to the plants’ dynamic physiological status throughout the day, which may vary among plant species and should be considered when planning remote sensing data acquisition.
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70
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Lyra DH, Virlet N, Sadeghi-Tehran P, Hassall KL, Wingen LU, Orford S, Griffiths S, Hawkesford MJ, Slavov GT. Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform. JOURNAL OF EXPERIMENTAL BOTANY 2020; 71:1885-1898. [PMID: 32097472 PMCID: PMC7094083 DOI: 10.1093/jxb/erz545] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Accepted: 02/19/2020] [Indexed: 05/08/2023]
Abstract
Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5-8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1-4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.
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Affiliation(s)
- Danilo H Lyra
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Nicolas Virlet
- Department of Plant Sciences, Rothamsted Research, Harpenden, UK
| | | | - Kirsty L Hassall
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Luzie U Wingen
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Orford
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Griffiths
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | | | - Gancho T Slavov
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
- Scion, Rotorua, New Zealand
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71
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Lyra DH, Virlet N, Sadeghi-Tehran P, Hassall KL, Wingen LU, Orford S, Griffiths S, Hawkesford MJ, Slavov GT. Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform. JOURNAL OF EXPERIMENTAL BOTANY 2020. [PMID: 32097472 DOI: 10.17632/pkxpkw6j43.2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5-8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1-4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.
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Affiliation(s)
- Danilo H Lyra
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Nicolas Virlet
- Department of Plant Sciences, Rothamsted Research, Harpenden, UK
| | | | - Kirsty L Hassall
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
| | - Luzie U Wingen
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Orford
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | - Simon Griffiths
- John Innes Centre, Norwich Research Park, Colney Lane, Norwich, UK
| | | | - Gancho T Slavov
- Department of Computational & Analytical Sciences, Rothamsted Research, Harpenden, UK
- Scion, Rotorua, New Zealand
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72
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Fabris M, Abbriano RM, Pernice M, Sutherland DL, Commault AS, Hall CC, Labeeuw L, McCauley JI, Kuzhiuparambil U, Ray P, Kahlke T, Ralph PJ. Emerging Technologies in Algal Biotechnology: Toward the Establishment of a Sustainable, Algae-Based Bioeconomy. FRONTIERS IN PLANT SCIENCE 2020; 11:279. [PMID: 32256509 PMCID: PMC7090149 DOI: 10.3389/fpls.2020.00279] [Citation(s) in RCA: 102] [Impact Index Per Article: 25.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2019] [Accepted: 02/24/2020] [Indexed: 05/18/2023]
Abstract
Mankind has recognized the value of land plants as renewable sources of food, medicine, and materials for millennia. Throughout human history, agricultural methods were continuously modified and improved to meet the changing needs of civilization. Today, our rapidly growing population requires further innovation to address the practical limitations and serious environmental concerns associated with current industrial and agricultural practices. Microalgae are a diverse group of unicellular photosynthetic organisms that are emerging as next-generation resources with the potential to address urgent industrial and agricultural demands. The extensive biological diversity of algae can be leveraged to produce a wealth of valuable bioproducts, either naturally or via genetic manipulation. Microalgae additionally possess a set of intrinsic advantages, such as low production costs, no requirement for arable land, and the capacity to grow rapidly in both large-scale outdoor systems and scalable, fully contained photobioreactors. Here, we review technical advancements, novel fields of application, and products in the field of algal biotechnology to illustrate how algae could present high-tech, low-cost, and environmentally friendly solutions to many current and future needs of our society. We discuss how emerging technologies such as synthetic biology, high-throughput phenomics, and the application of internet of things (IoT) automation to algal manufacturing technology can advance the understanding of algal biology and, ultimately, drive the establishment of an algal-based bioeconomy.
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Affiliation(s)
- Michele Fabris
- Climate Change Cluster (C3), University of Technology Sydney, Ultimo, NSW, Australia
- CSIRO Synthetic Biology Future Science Platform, Brisbane, QLD, Australia
| | - Raffaela M. Abbriano
- Climate Change Cluster (C3), University of Technology Sydney, Ultimo, NSW, Australia
| | - Mathieu Pernice
- Climate Change Cluster (C3), University of Technology Sydney, Ultimo, NSW, Australia
| | - Donna L. Sutherland
- Climate Change Cluster (C3), University of Technology Sydney, Ultimo, NSW, Australia
| | - Audrey S. Commault
- Climate Change Cluster (C3), University of Technology Sydney, Ultimo, NSW, Australia
| | - Christopher C. Hall
- Climate Change Cluster (C3), University of Technology Sydney, Ultimo, NSW, Australia
| | - Leen Labeeuw
- Climate Change Cluster (C3), University of Technology Sydney, Ultimo, NSW, Australia
| | - Janice I. McCauley
- Climate Change Cluster (C3), University of Technology Sydney, Ultimo, NSW, Australia
| | | | - Parijat Ray
- Climate Change Cluster (C3), University of Technology Sydney, Ultimo, NSW, Australia
| | - Tim Kahlke
- Climate Change Cluster (C3), University of Technology Sydney, Ultimo, NSW, Australia
| | - Peter J. Ralph
- Climate Change Cluster (C3), University of Technology Sydney, Ultimo, NSW, Australia
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73
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Lück S, Strickert M, Lorbeer M, Melchert F, Backhaus A, Kilias D, Seiffert U, Douchkov D. "Macrobot": An Automated Segmentation-Based System for Powdery Mildew Disease Quantification. PLANT PHENOMICS (WASHINGTON, D.C.) 2020; 2020:5839856. [PMID: 33313559 DOI: 10.1101/2020.03.16.993451] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/10/2019] [Accepted: 07/27/2020] [Indexed: 05/18/2023]
Abstract
Managing plant diseases is increasingly difficult due to reasons such as intensifying the field production, climatic change-driven expansion of pests, redraw and loss of effectiveness of pesticides, rapid breakdown of the disease resistance in the field, and other factors. The substantial progress in genomics of both plants and pathogens, achieved in the last decades, has the potential to counteract this negative trend, however, only when the genomic data is supported by relevant phenotypic data that allows linking the genomic information to specific traits. We have developed a set of methods and equipment and combined them into a "Macrophenomics facility." The pipeline has been optimized for the quantification of powdery mildew infection symptoms on wheat and barley, but it can be adapted to other diseases and host plants. The Macrophenomics pipeline scores the visible powdery mildew disease symptoms, typically 5-7 days after inoculation (dai), in a highly automated manner. The system can precisely and reproducibly quantify the percentage of the infected leaf area with a theoretical throughput of up to 10000 individual samples per day, making it appropriate for phenotyping of large germplasm collections and crossing populations.
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Affiliation(s)
- Stefanie Lück
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Correnstr. 3, 06466 Seeland, Germany
| | - Marc Strickert
- Physics Institute II, University of Giessen, Heinrich-Buff-Ring 16, 35392 Giessen, Germany
| | - Maximilian Lorbeer
- Julius Kühn Institute for National and International Plant Health, Messeweg 11/12, 38104 Braunschweig, Germany
| | - Friedrich Melchert
- Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstr. 22, 39106 Magdeburg, Germany
| | - Andreas Backhaus
- Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstr. 22, 39106 Magdeburg, Germany
| | - David Kilias
- Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstr. 22, 39106 Magdeburg, Germany
| | - Udo Seiffert
- Fraunhofer Institute for Factory Operation and Automation (IFF), Sandtorstr. 22, 39106 Magdeburg, Germany
| | - Dimitar Douchkov
- Leibniz Institute of Plant Genetics and Crop Plant Research (IPK), Correnstr. 3, 06466 Seeland, Germany
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74
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Biomass and Crop Height Estimation of Different Crops Using UAV-Based Lidar. REMOTE SENSING 2019. [DOI: 10.3390/rs12010017] [Citation(s) in RCA: 40] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Phenotyping of crops is important due to increasing pressure on food production. Therefore, an accurate estimation of biomass during the growing season can be important to optimize the yield. The potential of data acquisition by UAV-LiDAR to estimate fresh biomass and crop height was investigated for three different crops (potato, sugar beet, and winter wheat) grown in Wageningen (The Netherlands) from June to August 2018. Biomass was estimated using the 3DPI algorithm, while crop height was estimated using the mean height of a variable number of highest points for each m2. The 3DPI algorithm proved to estimate biomass well for sugar beet (R2 = 0.68, RMSE = 17.47 g/m2) and winter wheat (R2 = 0.82, RMSE = 13.94 g/m2). Also, the height estimates worked well for sugar beet (R2 = 0.70, RMSE = 7.4 cm) and wheat (R2 = 0.78, RMSE = 3.4 cm). However, for potato both plant height (R2 = 0.50, RMSE = 12 cm) and biomass estimation (R2 = 0.24, RMSE = 22.09 g/m2), it proved to be less reliable due to the complex canopy structure and the ridges on which potatoes are grown. In general, for accurate biomass and crop height estimates using those algorithms, the flight conditions (altitude, speed, location of flight lines) should be comparable to the settings for which the models are calibrated since changing conditions do influence the estimated biomass and crop height strongly.
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75
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Costa JM, Marques da Silva J, Pinheiro C, Barón M, Mylona P, Centritto M, Haworth M, Loreto F, Uzilday B, Turkan I, Oliveira MM. Opportunities and Limitations of Crop Phenotyping in Southern European Countries. FRONTIERS IN PLANT SCIENCE 2019; 10:1125. [PMID: 31608085 PMCID: PMC6774291 DOI: 10.3389/fpls.2019.01125] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/09/2019] [Accepted: 08/15/2019] [Indexed: 05/31/2023]
Abstract
The Mediterranean climate is characterized by hot dry summers and frequent droughts. Mediterranean crops are frequently subjected to high evapotranspiration demands, soil water deficits, high temperatures, and photo-oxidative stress. These conditions will become more severe due to global warming which poses major challenges to the sustainability of the agricultural sector in Mediterranean countries. Selection of crop varieties adapted to future climatic conditions and more tolerant to extreme climatic events is urgently required. Plant phenotyping is a crucial approach to address these challenges. High-throughput plant phenotyping (HTPP) helps to monitor the performance of improved genotypes and is one of the most effective strategies to improve the sustainability of agricultural production. In spite of the remarkable progress in basic knowledge and technology of plant phenotyping, there are still several practical, financial, and political constraints to implement HTPP approaches in field and controlled conditions across the Mediterranean. The European panorama of phenotyping is heterogeneous and integration of phenotyping data across different scales and translation of "phytotron research" to the field, and from model species to crops, remain major challenges. Moreover, solutions specifically tailored to Mediterranean agriculture (e.g., crops and environmental stresses) are in high demand, as the region is vulnerable to climate change and to desertification processes. The specific phenotyping requirements of Mediterranean crops have not yet been fully identified. The high cost of HTPP infrastructures is a major limiting factor, though the limited availability of skilled personnel may also impair its implementation in Mediterranean countries. We propose that the lack of suitable phenotyping infrastructures is hindering the development of new Mediterranean agricultural varieties and will negatively affect future competitiveness of the agricultural sector. We provide an overview of the heterogeneous panorama of phenotyping within Mediterranean countries, describing the state of the art of agricultural production, breeding initiatives, and phenotyping capabilities in five countries: Italy, Greece, Portugal, Spain, and Turkey. We characterize some of the main impediments for development of plant phenotyping in those countries and identify strategies to overcome barriers and maximize the benefits of phenotyping and modeling approaches to Mediterranean agriculture and related sustainability.
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Affiliation(s)
| | - Jorge Marques da Silva
- Biosystems and Integrative Sciences Institute (BioISI), Faculty of Sciences, Universidade de Lisboa, Lisbon, Portugal
| | - Carla Pinheiro
- FCT NOVA, Universidade Nova de Lisboa, Monte da Caparica, Portugal
- ITQB NOVA, Universidade Nova de Lisboa, Oeiras, Portugal
| | - Matilde Barón
- Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas (CSIC), Granada, Spain
| | - Photini Mylona
- HAO-DEMETER, Institute of Plant Breeding and Genetic Resources, Thermi, Greece
| | - Mauro Centritto
- Institute for Sustainable Plant Protection, Italian National Research Council (IPSP-CNR), Sesto Fiorentino, Italy
| | | | - Francesco Loreto
- Department of Biology, Agriculture and Food Sciences, CNR, Rome, Italy
| | - Baris Uzilday
- Department of Biology, Faculty of Science, Ege University, I˙zmir, Turkey
| | - Ismail Turkan
- Department of Biology, Faculty of Science, Ege University, I˙zmir, Turkey
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76
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Pérez-Bueno ML, Pineda M, Barón M. Phenotyping Plant Responses to Biotic Stress by Chlorophyll Fluorescence Imaging. FRONTIERS IN PLANT SCIENCE 2019; 10:1135. [PMID: 31620158 PMCID: PMC6759674 DOI: 10.3389/fpls.2019.01135] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/07/2019] [Accepted: 08/16/2019] [Indexed: 05/20/2023]
Abstract
Photosynthesis is a pivotal process in plant physiology, and its regulation plays an important role in plant defense against biotic stress. Interactions with pathogens and pests often cause alterations in the metabolism of sugars and sink/source relationships. These changes can be part of the plant defense mechanisms to limit nutrient availability to the pathogens. In other cases, these alterations can be the result of pests manipulating the plant metabolism for their own benefit. The effects of biotic stress on plant physiology are typically heterogeneous, both spatially and temporarily. Chlorophyll fluorescence imaging is a powerful tool to mine the activity of photosynthesis at cellular, leaf, and whole-plant scale, allowing the phenotyping of plants. This review will recapitulate the responses of the photosynthetic machinery to biotic stress factors, from pathogens (viruses, bacteria, and fungi) to pests (herbivory) analyzed by chlorophyll fluorescence imaging both at the lab and field scale. Moreover, chlorophyll fluorescence imagers and alternative techniques to indirectly evaluate photosynthetic traits used at field scale are also revised.
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Affiliation(s)
- María Luisa Pérez-Bueno
- Department of Biochemistry and Molecular and Cell Biology of Plants, Estación Experimental del Zaidín, Consejo Superior de Investigaciones Científicas, Granada, Spain
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77
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Young SN. A Framework for Evaluating Field-Based, High-Throughput Phenotyping Systems: A Meta-Analysis. SENSORS (BASEL, SWITZERLAND) 2019; 19:E3582. [PMID: 31426499 PMCID: PMC6720174 DOI: 10.3390/s19163582] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 07/29/2019] [Accepted: 08/13/2019] [Indexed: 12/26/2022]
Abstract
This paper presents a framework for the evaluation of system complexity and utility and the identification of bottlenecks in the deployment of field-based, high-throughput phenotyping (FB-HTP) systems. Although the capabilities of technology used for high-throughput phenotyping has improved and costs decreased, there have been few, if any, successful attempts at developing turnkey field-based phenotyping systems. To identify areas for future improvement in developing turnkey FB-HTP solutions, a framework for evaluating their complexity and utility was developed and applied to total of 10 case studies to highlight potential barriers in their development and adoption. The framework performs system factorization and rates the complexity and utility of subsystem factors, as well as each FB-HTP system as a whole, and provides data related to the trends and relationships within the complexity and utility factors. This work suggests that additional research and development are needed focused around the following areas: (i) data handling and management, specifically data transfer from the field to the data processing pipeline, (ii) improved human-machine interaction to facilitate usability across multiple users, and (iii) design standardization of the factors common across all FB-HTP systems to limit the competing drivers of system complexity and utility. This framework can be used to evaluate both previously developed and future proposed systems to approximate the overall system complexity and identify areas for improvement prior to implementation.
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Affiliation(s)
- Sierra N Young
- Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC 27695, USA.
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Beauchêne K, Leroy F, Fournier A, Huet C, Bonnefoy M, Lorgeou J, de Solan B, Piquemal B, Thomas S, Cohan JP. Management and Characterization of Abiotic Stress via PhénoField ®, a High-Throughput Field Phenotyping Platform. FRONTIERS IN PLANT SCIENCE 2019; 10:904. [PMID: 31379897 PMCID: PMC6646674 DOI: 10.3389/fpls.2019.00904] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2018] [Accepted: 06/26/2019] [Indexed: 05/10/2023]
Abstract
In order to evaluate the impact of water deficit in field conditions, researchers or breeders must set up large experiment networks in very restrictive field environments. Experience shows that half of the field trials are not relevant because of climatic conditions that do not allow the stress scenario to be tested. The PhénoField® platform is the first field based infrastructure in the European Union to ensure protection against rainfall for a large number of plots, coupled with the non-invasive acquisition of crops' phenotype. In this paper, we will highlight the PhénoField® production capability using data from 2017-wheat trial. The innovative approach of the PhénoField® platform consists in the use of automatic irrigating rainout shelters coupled with high throughput field phenotyping to complete conventional phenotyping and micrometeorological densified measurements. Firstly, to test various abiotic stresses, automatic mobile rainout shelters allow fine management of fertilization or irrigation by driving daily the intensity and period of the application of the desired limiting factor on the evaluated crop. This management is based on micro-meteorological measurements coupled with a simulation of a carbon, water and nitrogen crop budget. Furthermore, as high-throughput plant-phenotyping under controlled conditions is well advanced, comparable evaluation in field conditions is enabled through phenotyping gantries equipped with various optical sensors. This approach, giving access to either similar or innovative variables compared manual measurements, is moreover distinguished by its capacity for dynamic analysis. Thus, the interactions between genotypes and the environment can be deciphered and better detailed since this gives access not only to the environmental data but also to plant responses to limiting hydric and nitrogen conditions. Further data analyses provide access to the curve parameters of various indicator kinetics, all the more integrative and relevant of plant behavior under stressful conditions. All these specificities of the PhénoField® platform open the way to the improvement of various categories of crop models, the fine characterization of variety behavior throughout the growth cycle and the evaluation of particular sensors better suited to a specific research question.
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Affiliation(s)
| | - Fabien Leroy
- ARVALIS – Institut du Végétal, Ouzouer-le-Marché, France
| | | | - Céline Huet
- ARVALIS – Institut du Végétal, Ouzouer-le-Marché, France
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79
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Zhao C, Zhang Y, Du J, Guo X, Wen W, Gu S, Wang J, Fan J. Crop Phenomics: Current Status and Perspectives. FRONTIERS IN PLANT SCIENCE 2019; 10:714. [PMID: 31214228 PMCID: PMC6557228 DOI: 10.3389/fpls.2019.00714] [Citation(s) in RCA: 130] [Impact Index Per Article: 26.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2018] [Accepted: 05/14/2019] [Indexed: 05/19/2023]
Abstract
Reliable, automatic, multifunctional, and high-throughput phenotypic technologies are increasingly considered important tools for rapid advancement of genetic gain in breeding programs. With the rapid development in high-throughput phenotyping technologies, research in this area is entering a new era called 'phenomics.' The crop phenotyping community not only needs to build a multi-domain, multi-level, and multi-scale crop phenotyping big database, but also to research technical systems for phenotypic traits identification and develop bioinformatics technologies for information extraction from the overwhelming amounts of omics data. Here, we provide an overview of crop phenomics research, focusing on two parts, from phenotypic data collection through various sensors to phenomics analysis. Finally, we discussed the challenges and prospective of crop phenomics in order to provide suggestions to develop new methods of mining genes associated with important agronomic traits, and propose new intelligent solutions for precision breeding.
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Reynolds M, Schurr U. The 4th International Plant Phenotyping Symposium. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2019; 282:1. [PMID: 31003605 DOI: 10.1016/j.plantsci.2019.01.025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/09/2023]
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