Predicting yellow rust in wheat breeding trials by proximal phenotyping and machine learning.

ABSTRACT:

Background

High-throughput plant phenotyping (HTPP) methods have the potential to speed up the crop breeding process through the development of cost-effective, rapid and scalable phenotyping methods amenable to automation. Crop disease resistance breeding stands to benefit from successful implementation of HTPP methods, as bypassing the bottleneck posed by traditional visual phenotyping of disease, enables the screening of larger and more diverse populations for novel sources of resistance. The aim of this study was to use HTPP data obtained through proximal phenotyping to predict yellow rust scores in a large winter wheat field trial.

Results

The results show that 40-42 spectral vegetation indices (SVIs) derived from spectroradiometer data are sufficient to predict yellow r

SUBMITTER: Koc A 

PROVIDER: S-EPMC8922805 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature