Integrating Temporal Hyperspectral Data and Machine Learning for Wheat Yield Prediction Across Contrasting Environments

This supplementary package contains source data, R and Python analysis scripts, and reference results for wheat grain yield prediction using hyperspectral measurements at vegetative, heading, and grain-filling stages. It includes random forest and ridge regression models, multiple validation schemes, saved predictions, performance metrics, bootstrap intervals, and spectral importance analyses. A README, analysis protocol, dependency information, and file checksums support independent review and reproduction.