Multispectral UAV imagery and machine learning for morphological differentiation of two tropical C4-forage grasses in an integrated crop-livestock system in the State of Goias, Brazil

<b>Context.</b> Mixed tropical pastures containing both bunchgrasses (<i>Megathyrsus maximus</i>) and lightly sod-forming grasses (<i>Urochloa bryzantha</i>) present significant management challenges due to their contrasting growth habits and grazing requirements. Traditional visual assessment methods are inadequate for large-scale monitoring of grass growth forms. <b>Aims.</b> To develop and evaluate an automated system for detecting and mapping bunchgrass distribution in tropical pastures using multispectral UAV imagery and machine learning algorithms, enabling precision grazing management. <b>Methods.</b> Multispectral imagery (green, red, red edge, NIR bands) was acquired using a DJI Mavic 3M drone over 6.30 hectares of mixed pasture. We extracted 65 features, including spectral, vegetation indices, height, and metrics for Gray-Level Co-occurrence matrix (GLCM) texture. Three machine learning algorithms (Random Forest – RF, SVM, XGBoost) were compared using 200,000 annotated pixels. Feature selection experiments evaluated accuracy-efficiency trade-offs. <b>Key results.</b> XGBoost achieved superior performance (F1-score = 0.69), representing 9.73 % improvement over RF. Red edge-based GLCM texture features were the most important predictors. Feature-optimized models using only 15 features retained 91.3 % of full model performance while reducing processing time by 77 %. Fast spectral-only models achieved 400× speedup with 67.9 % performance retention. Spatial analysis revealed 28.14 % bunchgrass coverage across the study area. <b>Conclusions.</b> Machine learning, particularly XGBoost with texture features, effectively discriminates between tropical grass growth forms. Feature selection enables practical deployment without substantial accuracy loss. <b>Implications.</b> This methodology enables objective assessment of pasture composition, supporting precision grazing strategies, targeted fertilization, and the sustainable intensification of tropical livestock systems. The approach is scalable for operational deployment across extensive ranching operations.<br>For instructions download attached word files above.