Machine learning and environmental sustainability of agricultural residues: A review of wheat straw, rice straw, sugarcane bagasse, and cotton stalk valorization pathways

Agricultural residues represent abundant lignocellulosic resources, yet ineffective disposal practices generate severe greenhouse gas emissions. In the present study, a multi-stage computational modeling method was adopted to evaluate machine learning (ML) in predicting the environmental sustainability and valorization efficiency of wheat straw, rice straw, sugarcane bagasse, and cotton stalks. Physicochemical feature vectors were standardized on a dry basis and coupled with life-cycle assessment (LCA) matrices using advanced eXtreme Gradient Boosting (XGBoost) and Random Forest architectures. The results demonstrated that sugarcane bagasse exhibited optimal thermochemical characteristics, yielding a peak Environmental Sustainability Score (ESS = 0.054) and a dominant Valorization Index (VI = 88.50) due to an elevated cellulose fraction (42.5%) and a low ash ceiling (3.5%). Conversely, the elevated structural ash content in rice straw (14.5%) acted as a severe thermochemical hindrance, escalating processing carbon footprints to 485 kg CO₂-eq/t. One-way Analysis of Variance (ANOVA) and Tukey’s HSD post-hoc testing proved that the advanced tree-based ensemble models resided in a statistically superior performance tier (p < 0.001) relative to conventional linear equations. Regularized XGBoost achieved the absolute highest validation accuracy (R² = 0.95) and minimized error propagation (MSE = 0.010).Variance Inflation Factor (VIF) filtering successfully neutralized operational multicollinearity bias, while a 10-fold cross-validation routine confirmed outstanding model stability, yielding a tight 95% confidence interval window (0.914, 0.962). Ultimately, this investigation establishes a rigorous, data-driven roadmap to accelerate decentralized biomass loop-closing strategies within a sustainable circular bioeconomy.