Combining novel feature selection strategy and hyperspectral vegetation indices to predict crop yield.

ABSTRACT:

Background

Wheat is an important food crop globally, and timely prediction of wheat yield in breeding efforts can improve selection efficiency. Traditional yield prediction method based on secondary traits is time-consuming, costly, and destructive. It is urgent to develop innovative methods to improve selection efficiency and accelerate genetic gains in the breeding cycle.

Results

Crop yield prediction using remote sensing has gained popularity in recent years. This paper proposed a novel ensemble feature selection (EFS) method to improve yield prediction from hyperspectral data. For this, 207 wheat cultivars and breeding lines were grown under full and limited irrigation treatments respectively, and their canopy hyperspectral reflectance was measured at the flowering, earl

SUBMITTER: Fei S 

PROVIDER: S-EPMC9641855 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature