Feasability of bad-good genotypes screening using NIRS. High-throughput phenotyping protocols (HTPP), WP3

Cornet Denis, Dardenne Pierre, Davrieux Fabrice, Alamu Emmanuel Oladeji. 2021. Feasability of bad-good genotypes screening using NIRS. High-throughput phenotyping protocols (HTPP), WP3. Montpellier : RTBfoods Project-CIRAD, 8 p.

https://doi.org/10.18167/agritrop/00671

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RTBfoods_SOP_NIRS screening for bad & good genotypes.pdf
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Résumé : The current protocol's main objective is to determine the feasibility to calibrate a qualitative classification model allowing the distinction between good and bad genotypes using NIRS spectra collected on different product states (i.e. raw intact organ, chopped, puree). This protocol focuses on pounded yam and boiled cassava, but if the proof of concept is achieved, it can be extended to RTB product profiles. The main principle is based on the existing traditional knowledge of varieties of good and bad qualities. This knowledge allows us to choose genotypes from both categories and train a binary classification model to predict their belonging. To test feasibility as soon as possible, this preliminary protocol focuses on the already available database. The idea is to train and test a classification model predicting good or bad genotypes using a convolutional neural network (CNN) with a binary cross-entropy loss function. Indeed, this type of algorithms showed excellent results for similar studies. In order to avoid a confusion effect of storage length on tuber quality, the only tuber with similar physiological age (i.e. meaning same storage length) should be kept in each database.

Mots-clés libres : Overall product quality, Varietal selection, Near infrared spectroscopy, Convolutional neural network, Binary classification

Agences de financement hors UE : Bill and Melinda Gates Foundation, Centre de Coopération Internationale en Recherche Agronomique pour le Développement, Centro Internacional de Agricultura Tropical, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, James Hutton Institute

Projets sur financement : (FRA) Breeding RTB Products for End User Preferences

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