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

View ORCID ProfileV. Oury, View ORCID ProfileT. Leroux, View ORCID ProfileO. Turc, View ORCID ProfileR. Chapuis, View ORCID ProfileC. Palaffre, View ORCID ProfileF. Tardieu, View ORCID ProfileS. Alvarez Prado, View ORCID ProfileC. Welcker, View ORCID ProfileS. Lacube
doi: https://doi.org/10.1101/2021.12.20.473433
V. Oury
aPhymea Systems, 453 Rue de l’Espinouse, Montpellier, France
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  • ORCID record for V. Oury
T. Leroux
aPhymea Systems, 453 Rue de l’Espinouse, Montpellier, France
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  • ORCID record for T. Leroux
O. Turc
bLEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
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R. Chapuis
cMELGUEIL, Univ Montpellier, INRAE, Montpellier, France
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C. Palaffre
dUE Maïs, INRAE, Univ. Bordeaux, Saint Martin de Hinx, France
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F. Tardieu
bLEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
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S. Alvarez Prado
eIFEVA-CONICET, Facultad de Agronomía, Universidad de Buenos Aires, Av. San Martín 4453 (C1417DSE), Ciudad de Buenos Aires, Argentina
fCatedral de Sistemas de Cultivos Extensivos-GIMUCE, Facultad de Ciencas Agrarias, Universidad Nacional de Rosario, Campo Experimental Villarino S/N, S21125ZAA, Zavalla, Prov. De Santa Fe, Argentina
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C. Welcker
bLEPSE, Univ Montpellier, INRAE, Institut Agro, Montpellier, France
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S. Lacube
aPhymea Systems, 453 Rue de l’Espinouse, Montpellier, France
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  • ORCID record for S. Lacube
  • For correspondence: sebastien.lacube@phymea-systems.com
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Abstract

Background Characterizing plant genetic resources and their response to the environment through accurate measurement of relevant traits is crucial to genetics and breeding. The 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 categorization. This method opens avenues for high-throughput genetic or functional studies in the context of plant adaptation to a changing environment.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted December 21, 2021.
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Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
V. Oury, T. Leroux, O. Turc, R. Chapuis, C. Palaffre, F. Tardieu, S. Alvarez Prado, C. Welcker, S. Lacube
bioRxiv 2021.12.20.473433; doi: https://doi.org/10.1101/2021.12.20.473433
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Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits
V. Oury, T. Leroux, O. Turc, R. Chapuis, C. Palaffre, F. Tardieu, S. Alvarez Prado, C. Welcker, S. Lacube
bioRxiv 2021.12.20.473433; doi: https://doi.org/10.1101/2021.12.20.473433

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