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.

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