Predicting Structural Traits and Chemical Composition of Urochloa decumbens Using Aerial Imagery and Machine Learning

Precision agriculture, including sensors and artificial intelligence, is transforming agricultural monitoring. This study developed predictive models for fresh and dry forage mass, canopy height, forage density, dry matter (%DM), and crude protein (%CP) concentrations in signalgrass [Urochloa decumbens (Stapf) R.D. Webster] pastures using machine learning and UAV-based multispectral imagery. The experiment was conducted at the Federal University of Viçosa (2019–2020), applying nitrogen doses after each harvest to promote variability. Multiple Linear Regression (MLR), Support Vector Regressor (SVR), and Random Forest Regressor (RFR) models were trained with multispectral and meteorological data. The best results were obtained for fresh forage mass with RFR (R2 = 0.82, RMSE = 2894.10 kg ha−1), dry forage mass with SVR (R2 = 0.68, RMSE = 719.87 kg ha−1), and dry matter concentration with MLR (R2 = 0.64, RMSE = 3.83%). Forage density showed moderate performance (R2 = 0.56), while canopy height demonstrated limited accuracy (R2 = 0.44). Crude protein was not adequately predicted by any model, highlighting multispectral sensor limitations and suggesting hyperspectral sensors usage. Results demonstrate the applicability of remote sensing combined with machine learning in forage management, but indicate the need to expand temporal and spatial data variability and integrate different sensor types to increase model robustness.