1. Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
- Author
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V. Oury, T. Leroux, O. Turc, R. Chapuis, C. Palaffre, F. Tardieu, S. Alvarez Prado, C. Welcker, S. Lacube, Phymea Systems, Écophysiologie des Plantes sous Stress environnementaux (LEPSE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE)-Institut Agro Montpellier, Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro), Domaine expérimental de Melgueil (MONTP MELGUEIL UE), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité expérimentale du maïs (BORDX ST-MARTIN UE), Instituto de Investigaciones Fisiologicas y Ecologicas Vinculadas a la Agricultura (IFEVA), Consejo Nacional de Investigaciones Científicas y Técnicas [Buenos Aires] (CONICET)-Facultad de Agronomía [Buenos Aires], Universidad de Buenos Aires [Buenos Aires] (UBA)-Universidad de Buenos Aires [Buenos Aires] (UBA), Universidad de Buenos Aires [Buenos Aires] (UBA), This study is a joined effort from Phymea-Systems and the INRAe. For Phymea-Systems, the work was largely financed by the company’s own resources with a participation from Région Occitanie funds. For the INRAe participation, this work was supported by the EU project FP7-244374 (DROPS), the projects ANR-10-BTBR-0001 (Amaizing) and ANR-11-INBS-0012 (Phenome) and the EU project H2020 731013 (EPPN2020)., ANR-10-BTBR-0001,AMAIZING,Développer de nouvelles variétés de maïs pour une agriculture durable: une approche intégrée de la génomique à la sélection(2010), ANR-11-INBS-0012,PHENOME,Centre français de phénomique végétale(2011), European Project: 244374,EC:FP7:KBBE,FP7-KBBE-2009-3,DROPS(2010), and European Project: 731013 ,EPPN2020(2017)
- Subjects
Maize ear imaging ,Grain abortion ,[SDV]Life Sciences [q-bio] ,Genetics ,Plant Science ,CNN-based deep learning ,Zea mays ,Environmental response ,Maize ear spatial organization ,Biotechnology ,Grain set - Abstract
Background Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. Spatial organization of the maize ear provides insights into the response of grain yield to environmental conditions. Current automated methods for phenotyping the maize ear do not capture these spatial features. Results We developed EARBOX, a low-cost, open-source system for automated phenotyping of maize ears. EARBOX integrates open-source technologies for both software and hardware that facilitate its deployment and improvement for specific research questions. The imaging platform consists of a customized box in which ears are repeatedly imaged as they rotate via motorized rollers. With deep learning based on convolutional neural networks, the image analysis algorithm uses a two-step procedure: ear-specific grain masks are first created and subsequently used to extract a range of trait data per ear, including ear shape and dimensions, the number of grains and their spatial organisation, and the distribution of grain dimensions along the ear. The reliability of each trait was validated against ground-truth data from manual measurements. Moreover, EARBOX derives novel traits, inaccessible through conventional methods, especially the distribution of grain dimensions along grain cohorts, relevant for ear morphogenesis, and the distribution of abortion frequency along the ear, relevant for plant response to stress, especially soil water deficit. Conclusions The proposed system provides robust and accurate measurements of maize ear traits including spatial features. Future developments include grain type and colour categorisation. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.
- Published
- 2022