Gradient boosting for yield prediction of elite maize hybrid ZhengDan 958.

ABSTRACT: Understanding accurate methods for predicting yields in complex agricultural systems is critical for effective nutrient management and crop growth. Machine learning has proven to be an important tool in this context. Numerous studies have investigated its potential for predicting yields under different conditions. Among these algorithms, Random Forest (RF) has gained prominence due to its ability to manage large data sets with high dimensions, as well as its ability to uncover complicated non-linear relationships and interactions between variables. RF is particularly suitable for scenarios with categorical variables and missing data. Given the complex web of management practices and their nonlinear effects on yield prediction, it is important to investigate new machine learning algorithms.

SUBMITTER: Ennaji O 

PROVIDER: S-EPMC11651618 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature