Machine learning and sensor fusion approaches to set up phenotyping proxies from 2D, 3D and spectral images: case study of morpho-physiological traits underlying crop growth and yield variability with sorghum as a mode

Abstract

This project will support innovation in the mathematical and algorithmic methodologies required to develop spectral/lidar based proxies of phenotypic traits up to now not accessible based on standard imaging (rgb); it will thus generate innovation in the area of field crop phenotyping and accordingly improve the capacity to study the genetic and physiological architecture of complex traits and to predict GxE.

Plant phenotyping relevance

2D・3D・スペクトル画像とセンサーフュージョンを用いて、従来取得困難だった作物形質の表現型プロキシを開発する研究であり、手法開発が中心である。

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