Enhanced Crop Leaf Area Index Estimation via Random Forest Regression: Bayesian Optimization and Feature Selection Approach

Abstract

The Leaf Area Index (LAI) is a crucial structural parameter linked to the photosynthetic capacity and biomass of crops. While integrating machine learning algorithms with spectral variables has improved LAI estimation over large areas, excessive input parameters can lead to data redundancy and reduced generalizability across different crop species. To address these challenges, we propose a novel framework based on Bayesian-Optimized Random Forest Regression (Bayes-RFR) for enhanced LAI estimation. This framework employs a tree model-based feature selection method to identify critical features, reducing redundancy and improving model interpretability. A Gaussian process serves as a prior model to optimize the hyperparameters of the Random Forest Regression. The field experiments conducted over two years on maize and wheat involved collecting LAI, hyperspectral, multispectral, and RGB data. The results indicate that the tree model-based feature selection outperformed the traditional correlation analysis and Recursive Feature Elimination (RFE). The Bayes-RFR model demonstrated a superior validation accuracy compared to the standard Random Forest Regression and Pso-optimized models, with the R2 values increasing by 27% for the maize hyperspectral data, 12% for the maize multispectral data, and 47% for the wheat hyperspectral data. These findings suggest that the proposed Bayes-RFR framework significantly enhances the stability and predictive capability of LAI estimation across various crop types, offering valuable insights for precision agriculture and crop monitoring.

Keywords

• leaf area index
• hyperparameter optimization
• Bayesian algorithm
• random forests