Multi-source information fusion-driven corn yield prediction using the Random Forest from the perspective of Agricultural and Forestry Economic Management.

ABSTRACT: The objective of this study is to promptly and accurately allocate resources, scientifically guide grain distribution, and enhance the precision of crop yield prediction (CYP), particularly for corn, along with ensuring application stability. The digital camera is selected to capture the digital image of a 60 m × 10 m experimental cornfield. Subsequently, the obtained data on corn yield and statistical growth serve as inputs for the multi-source information fusion (MSIF). The study proposes an MSIF-based CYP Random Forest model by amalgamating the fluctuating corn yield dataset. In relation to the spatial variability of the experimental cornfield, the fitting degree and prediction ability of the proposed MSIF-based CYP Random Forest are analyzed, with statistics collected from 1-hectare, 1

SUBMITTER: Yang X 

PROVIDER: S-EPMC11325042 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature