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High-throughput and accurate phenotyping for maize

Fast, simple and reliable access to phenotypic variables of interest.

 

We leverage AI systems to bring you the best possible measurements for hard-to-reach traits, usually only accessible via time-consuming and labor-intensive means.

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EB benefits

Ear and grain data

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The data are produced at 2 scales:

  • ears

  • grains along the ear


For ears, all data are measured or calculated for each of the 6 images captured by the system. The 6 images can be considered as repetitions, and averaged to obtain values at the ear scale, and indices (e.g. standard deviation, etc.) concerning their stability around the ear.


For grains, it is possible to make averages according to their "level" (cohort number) from the base of the ear. You can also process grain data according to their position (cm) in relation to the base of the ear.

Data

Access to yield components

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For maize, access to precise yield components usually requires time-consuming manual measurements, with low repeatability.

The EARBOX system has been build to fit into phenotyping pipelines of experimental stations, while speeding it, complementing it and scaling it to new heights:

  • More data : to increase the power of genotypic studies and most statistical analyses

  • Accurate data : to correctly characterise varieties, differences and similarities between them, biotic and abiotic stresses

  • Standardized data : to regroup, merge and treat large datasets from multi-site trials

Yield components

Fast and Friendly

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Total acquisition time per ear <30s

A smart and friendly tool

Based on open source technologies

Fast

Accurate and reliable

Earbox is a tool developed by scientists for scientists.

 

Our development methodology shows our rigor and our requirement to offer research a useful tool that can feed tomorrow's reflections and experiments. In this logic, the methodologies of phenotypic data extraction are presented in the method article: Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits. We scanned and trained the neural network on the widest possible phenotypic diversity, so that it would be able to segment maize kernels regardless of their appearance. We then developed an algorithm to extract in post-processing all the phenotypic data, which were all compared to manual data or via other machines.


This project was initiated in partnership with the INRAE of Montpellier (France) and more particularly the LEPSE (Laboratory of ecophysiology of plants under environmental stress) and the experimental station of Mauguio, with whom the EARBOX was defined, tested and validated.