Application of machine learning algorithms and feature selection in rapeseed (Brassica napus L.) breeding for seed yield.

ABSTRACT:

Background

Studying the relationships between rapeseed seed yield (SY) and its yield-related traits can assist rapeseed breeders in the efficient indirect selection of high-yielding varieties. However, since the conventional and linear methods cannot interpret the complicated relations between SY and other traits, employing advanced machine learning algorithms is inevitable. Our main goal was to find the best combination of machine learning algorithms and feature selection methods to maximize the efficiency of indirect selection for rapeseed SY.

Results

To achieve that, twenty-five regression-based machine learning algorithms and six feature selection methods were employed. SY and yield-related data from twenty rapeseed genotypes were collected from field experiments over a

SUBMITTER: Shahsavari M 

PROVIDER: S-EPMC10273667 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature