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Harandi N, Vandenberghe B, Vankerschaver J, Depuydt S, Van Messem A. How to make sense of 3D representations for plant phenotyping: a compendium of processing and analysis techniques. PLANT METHODS 2023; 19:60. [PMID: 37353846 DOI: 10.1186/s13007-023-01031-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/19/2023] [Indexed: 06/25/2023]
Abstract
Computer vision technology is moving more and more towards a three-dimensional approach, and plant phenotyping is following this trend. However, despite its potential, the complexity of the analysis of 3D representations has been the main bottleneck hindering the wider deployment of 3D plant phenotyping. In this review we provide an overview of typical steps for the processing and analysis of 3D representations of plants, to offer potential users of 3D phenotyping a first gateway into its application, and to stimulate its further development. We focus on plant phenotyping applications where the goal is to measure characteristics of single plants or crop canopies on a small scale in research settings, as opposed to large scale crop monitoring in the field.
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Affiliation(s)
- Negin Harandi
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, 119 Songdomunhwa-ro, Yeonsu-gu, Incheon, South Korea
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, Ghent, Belgium
| | | | - Joris Vankerschaver
- Center for Biosystems and Biotech Data Science, Ghent University Global Campus, 119 Songdomunhwa-ro, Yeonsu-gu, Incheon, South Korea
- Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, Ghent, Belgium
| | - Stephen Depuydt
- Erasmus Applied University of Sciences and Arts, Campus Kaai, Nijverheidskaai 170, Anderlecht, Belgium
| | - Arnout Van Messem
- Department of Mathematics, Université de Liège, Allée de la Découverte 12, Liège, Belgium.
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Stanschewski CS, Rey E, Fiene G, Craine EB, Wellman G, Melino VJ, S. R. Patiranage D, Johansen K, Schmöckel SM, Bertero D, Oakey H, Colque-Little C, Afzal I, Raubach S, Miller N, Streich J, Amby DB, Emrani N, Warmington M, Mousa MAA, Wu D, Jacobson D, Andreasen C, Jung C, Murphy K, Bazile D, Tester M. Quinoa Phenotyping Methodologies: An International Consensus. PLANTS (BASEL, SWITZERLAND) 2021; 10:1759. [PMID: 34579292 PMCID: PMC8472428 DOI: 10.3390/plants10091759] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 08/09/2021] [Accepted: 08/12/2021] [Indexed: 11/30/2022]
Abstract
Quinoa is a crop originating in the Andes but grown more widely and with the genetic potential for significant further expansion. Due to the phenotypic plasticity of quinoa, varieties need to be assessed across years and multiple locations. To improve comparability among field trials across the globe and to facilitate collaborations, components of the trials need to be kept consistent, including the type and methods of data collected. Here, an internationally open-access framework for phenotyping a wide range of quinoa features is proposed to facilitate the systematic agronomic, physiological and genetic characterization of quinoa for crop adaptation and improvement. Mature plant phenotyping is a central aspect of this paper, including detailed descriptions and the provision of phenotyping cards to facilitate consistency in data collection. High-throughput methods for multi-temporal phenotyping based on remote sensing technologies are described. Tools for higher-throughput post-harvest phenotyping of seeds are presented. A guideline for approaching quinoa field trials including the collection of environmental data and designing layouts with statistical robustness is suggested. To move towards developing resources for quinoa in line with major cereal crops, a database was created. The Quinoa Germinate Platform will serve as a central repository of data for quinoa researchers globally.
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Affiliation(s)
- Clara S. Stanschewski
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Elodie Rey
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Gabriele Fiene
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Evan B. Craine
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA; (E.B.C.); (K.M.)
| | - Gordon Wellman
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Vanessa J. Melino
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
| | - Dilan S. R. Patiranage
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
- Plant Breeding Institute, Christian-Albrechts-University of Kiel, 24118 Kiel, Germany; (N.E.); (C.J.)
| | - Kasper Johansen
- Water Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia;
| | - Sandra M. Schmöckel
- Department Physiology of Yield Stability, Institute of Crop Science, University of Hohenheim, 70599 Stuttgart, Germany;
| | - Daniel Bertero
- Department of Plant Production, School of Agriculture, University of Buenos Aires, Buenos Aires C1417DSE, Argentina;
| | - Helena Oakey
- Robinson Research Institute, Adelaide Medical School, University of Adelaide, Adelaide, SA 5005, Australia;
| | - Carla Colque-Little
- Department of Plant and Environmental Sciences, University of Copenhagen, DK-2630 Taastrup, Denmark; (C.C.-L.); (D.B.A.); (C.A.)
| | - Irfan Afzal
- Department of Agronomy, University of Agriculture, Faisalabad 38000, Pakistan;
| | - Sebastian Raubach
- Department of Information and Computational Sciences, The James Hutton Institute, Invergowrie, Dundee AB15 8QH, UK;
| | - Nathan Miller
- Department of Botany, University of Wisconsin, 430 Lincoln Dr, Madison, WI 53706, USA;
| | - Jared Streich
- Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA; (J.S.); (D.J.)
| | - Daniel Buchvaldt Amby
- Department of Plant and Environmental Sciences, University of Copenhagen, DK-2630 Taastrup, Denmark; (C.C.-L.); (D.B.A.); (C.A.)
| | - Nazgol Emrani
- Plant Breeding Institute, Christian-Albrechts-University of Kiel, 24118 Kiel, Germany; (N.E.); (C.J.)
| | - Mark Warmington
- Department of Primary Industries and Regional Development, Agriculture and Food, Kununurra, WA 6743, Australia;
| | - Magdi A. A. Mousa
- Department of Arid Land Agriculture, Faculty of Meteorology, Environment and Arid Land Agriculture, King Abdulaziz University, Jeddah 21589, Saudi Arabia;
- Department of Vegetables, Faculty of Agriculture, Assiut University, Assiut 71526, Egypt
| | - David Wu
- Shanxi Jiaqi Agri-Tech Co., Ltd., Taiyuan 030006, China;
| | - Daniel Jacobson
- Biosciences, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA; (J.S.); (D.J.)
| | - Christian Andreasen
- Department of Plant and Environmental Sciences, University of Copenhagen, DK-2630 Taastrup, Denmark; (C.C.-L.); (D.B.A.); (C.A.)
| | - Christian Jung
- Plant Breeding Institute, Christian-Albrechts-University of Kiel, 24118 Kiel, Germany; (N.E.); (C.J.)
| | - Kevin Murphy
- Department of Crop and Soil Sciences, Washington State University, Pullman, WA 99164, USA; (E.B.C.); (K.M.)
| | - Didier Bazile
- CIRAD, UMR SENS, 34398 Montpellier, France;
- SENS, CIRAD, IRD, University Paul Valery Montpellier 3, 34090 Montpellier, France
| | - Mark Tester
- Center for Desert Agriculture, Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal 23955, Saudi Arabia; (C.S.S.); (E.R.); (G.F.); (G.W.); (V.J.M.); (D.S.R.P.)
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Fritsche-Neto R, Galli G, Borges KLR, Costa-Neto G, Alves FC, Sabadin F, Lyra DH, Morais PPP, Braatz de Andrade LR, Granato I, Crossa J. Optimizing Genomic-Enabled Prediction in Small-Scale Maize Hybrid Breeding Programs: A Roadmap Review. FRONTIERS IN PLANT SCIENCE 2021; 12:658267. [PMID: 34276721 PMCID: PMC8281958 DOI: 10.3389/fpls.2021.658267] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/25/2021] [Accepted: 05/10/2021] [Indexed: 06/13/2023]
Abstract
The usefulness of genomic prediction (GP) for many animal and plant breeding programs has been highlighted for many studies in the last 20 years. In maize breeding programs, mostly dedicated to delivering more highly adapted and productive hybrids, this approach has been proved successful for both large- and small-scale breeding programs worldwide. Here, we present some of the strategies developed to improve the accuracy of GP in tropical maize, focusing on its use under low budget and small-scale conditions achieved for most of the hybrid breeding programs in developing countries. We highlight the most important outcomes obtained by the University of São Paulo (USP, Brazil) and how they can improve the accuracy of prediction in tropical maize hybrids. Our roadmap starts with the efforts for germplasm characterization, moving on to the practices for mating design, and the selection of the genotypes that are used to compose the training population in field phenotyping trials. Factors including population structure and the importance of non-additive effects (dominance and epistasis) controlling the desired trait are also outlined. Finally, we explain how the source of the molecular markers, environmental, and the modeling of genotype-environment interaction can affect the accuracy of GP. Results of 7 years of research in a public maize hybrid breeding program under tropical conditions are discussed, and with the great advances that have been made, we find that what is yet to come is exciting. The use of open-source software for the quality control of molecular markers, implementing GP, and envirotyping pipelines may reduce costs in an efficient computational manner. We conclude that exploring new models/tools using high-throughput phenotyping data along with large-scale envirotyping may bring more resolution and realism when predicting genotype performances. Despite the initial costs, mostly for genotyping, the GP platforms in combination with these other data sources can be a cost-effective approach for predicting the performance of maize hybrids for a large set of growing conditions.
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Affiliation(s)
- Roberto Fritsche-Neto
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Giovanni Galli
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Karina Lima Reis Borges
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Germano Costa-Neto
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Filipe Couto Alves
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, United States
| | - Felipe Sabadin
- Laboratory of Allogamous Plant Breeding, Genetics Department, Luiz de Queiroz College of Agriculture, University of São Paulo, Piracicaba, Brazil
| | - Danilo Hottis Lyra
- Department of Computational and Analytical Sciences, Rothamsted Research, Harpenden, United Kingdom
| | | | | | - Italo Granato
- Laboratoire d'Ecophysiologie des Plantes sous Stress Environnementaux (LEPSE), Institut National de la Recherche Agronomique (INRA), Univ. Montpellier, SupAgro, Montpellier, France
| | - Jose Crossa
- Biometrics and Statistics Unit, International Maize and Wheat Improvement Center (CIMMYT), Carretera México - Veracruz, Texcoco, Mexico
- Colegio de Posgraduado, Montecillo, Mexico
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