Wheat physiology predictor: predicting physiological traits in wheat from hyperspectral reflectance measurements using deep learning.

ABSTRACT:

Background

The need for rapid in-field measurement of key traits contributing to yield over many thousands of genotypes is a major roadblock in crop breeding. Recently, leaf hyperspectral reflectance data has been used to train machine learning models using partial least squares regression (PLSR) to rapidly predict genetic variation in photosynthetic and leaf traits across wheat populations, among other species. However, the application of published PLSR spectral models is limited by a fixed spectral wavelength range as input and the requirement of separate custom-built models for each trait and wavelength range. In addition, the use of reflectance spectra from the short-wave infrared region requires expensive multiple detector spectrometers. The ability to train a model that can a

SUBMITTER: Furbank RT 

PROVIDER: S-EPMC8527791 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature