Can Environmental Analysis Algorithms Be Improved by Data Fusion and Soil Removal for UAV-Based Buffel Grass Biomass Prediction?

The growing demand for sustainable livestock systems requires efficient methods for monitoring forage biomass. This study evaluated spectral (RGB and multispectral), textural (GLCM), and area attributes derived from unmanned aerial vehicle (UAV) imagery to predict buffelgrass (Cenchrus ciliaris L.) biomass, also testing the effect of soil pixel removal. A comprehensive machine learning pipeline (12 algorithms and 6 feature selection methods) was applied to 14 data combinations. Our results demonstrated that soil removal consistently improved the performance of the applied models. Multispectral (MSI) sensors were the most robust individually, whereas textural (GLCM) attributes did not contribute significantly. Although the MSI and RGB data combination proved complementary, the model with the highest accuracy was obtained with CatBoost using only RGB information after Boruta feature selection, achieving a CCC of 0.83, RMSE of 0.214 kg, and R2 of 0.81 in the test set. The most important variable was vegetation cover area (19.94%), surpassing spectral indices. We conclude that integrating RGB UAVs with robust processing can generate accessible and effective tools for forage monitoring. This approach can support pasture management by optimizing stocking rates, enhancing natural resource efficiency, and supporting data-driven decisions in precision silvopastoral systems.