Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits

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 me...

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Bibliographic Details
Published in:	Plant methods Vol. 18; no. 1; pp. 1 - 96
Main Authors:	Oury, V, Leroux, T, Turc, O, Chapuis, R, Palaffre, C, Tardieu, F, Prado, S. Alvarez, Welcker, C, Lacube, S
Format:	Journal Article
Language:	English
Published:	London BioMed Central Ltd 28-07-2022 BioMed Central BMC
Subjects:	Abortion Agricultural production Algorithms Analysis Artificial neural networks Automation Biodiversity Changing environments CNN-based deep learning Corn Crop yield Deep learning Environmental changes Environmental conditions Environmental response Genetic aspects Genetic resources Genetics Grain abortion Grain set Grain size Growth Image analysis Image processing Life Sciences Machine learning Maize ear imaging Methodology Methods Moisture content Morphogenesis Neural networks Phenotyping Software Soil stresses Soil water Water deficit Zea mays Argentina France Zea mays Maize ear spatial organization Grain set Environmental response Maize ear imaging Grain abortion CNN-based deep learning
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Summary:	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. 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. 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.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1746-4811 1746-4811
DOI:	10.1186/s13007-022-00925-8