High-throughput and accurate phenotyping for maize
Ear and grain data
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The data are produced at 2 scales:
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ears
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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.
Access to yield components
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:
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More data : to increase the power of genotypic studies and most statistical analyses
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Accurate data : to correctly characterise varieties, differences and similarities between them, biotic and abiotic stresses
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Standardized data : to regroup, merge and treat large datasets from multi-site trials
Fast and Friendly
Developed with the French agronomic institute
(INRAE)
Acquisition time <30sec. (~26sec.)
Base on open source and affordable technologies
Flexible ear identification: by ear or by plot
Automatic check for duplicates and acquisition inconsistencies
Developed by scientists for scientists and professionals:
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 (2022 - Plant Methods): Earbox, an open tool for high-throughput measurement of the spatial organization of maize ears and inference of novel traits .We scanned and trained a 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, validated and adopted.
Accurate and reliable data
Specifications
6 ears version (for French market only)
Image cabine
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6 ears slot
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Cameras: Pi Noir Camera V2 x2
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2 LED spectra: CRI 90 visible and infrared 940nm
Hardware
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System: Raspberry PI x2
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2 stepper motors: rollers and door
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21" screen
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Aluminum profil structure
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500Go HDD drive
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Maintenance-free belt drive for the rollers
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Dimensions:
Software
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Raspbian OS
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Python program
Connectics
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3 usb slot + 1 specific slot for HDD drive
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Power supply 220V
Production lead time
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2-3 months
3 ears version
Image cabine
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3 ears slot
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Camera: Pi Noir Camera V2
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2 LED spectra: CRI 90 visible and nfrared 940nm
Hardware
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System: Raspberry PI
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1 stepper motor: rollers
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Aluminum profil structure
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500Go HDD drive
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3D printed gears for the rollers
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Dimensions:
Software
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Raspbian OS
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Python program
Connectics
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Raspberry Pi Slots
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HDMI male connectic (1m)
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Main power supply (DC 12V) for Raspberry Pi system and motor: 140-220V (50-60Hz)
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Led power supply (DC 12V): 140-220V (50-60Hz)
Production lead time
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2-3 months
An evolutive solution
Characterization of qualitative traits for maize
We are currently working internally on the recognition of qualitative traits in ears using the UPOV method. We hope to be able to make these variables available for the 2024 harvest.
Characterization of biotic stresses for maize
We are currently working with the University of Angers (LARIS - Laboratoire Angevin de Recherche en Ingénierie des Systèmes) and Cérom (CANADA-QUEBEC) to integrate the recognition and characterization of biotic stresses such as fusarium and smut, codling moth attacks and Western bean cutworm.
Characterization of new species
In 2021 we collaborated with the LARIS (Laboratoire Angevin de Recherche en Ingénierie des Systèmes) of the University of Angers and the French GEVES (Groupe d'étude et de contrôle des variétés et de semences), in order to adapt the system to agronomic species such as wheat, barley, spelt, and triticale.Despite conclusive results on these species, we have stopped at the feasibility study stage, as we have not found any organization interested in automatic phenotyping of ears of these species. If you are interested, please do not hesitate to contact us, we will be delighted to discuss your requirements.
Collaboration & Open Source
We are totally open to collaboration, and our project is open source. But for a scientific tool, it's risky to measure and compare things without the certainty that the measurements are perfectly standardized.
A tool like this requires complex implementation. Providing codes and drawings would require extensive documentation on the manufacture, installation and commissioning of the system. Unfortunately, Phymea is still a small team working on many projects in parallel, and we don't have the time available for all these tasks. But a good way to encourage us in this direction is to adopt an Earbox in your home ;).
However, we are willing to share all the information we need with external collaborative projects (e.g. companies, universities and research laboratories) aimed at making concrete improvements to existing systems, so that all Earbox users can benefit. If you are interested, please contact us!
