Ecotoxico logical Assessment of Industrial Agricultural Practices Using Machine Learning: A Data-Driven Framework for Environmental Risk Prediction

Abstract

Industrial agricultural systems have significantly increased global food production but have simultaneously intensified environmental degradation through excessive pesticide application, fertilizer runoff, and soil contamination. Such common practices exacerbate the accumulation of long-lasting chemical residues into soil and aquatic ecosystems, posing intricate ecotoxicological consequences that are hard to evaluate through traditional analytical strategies. We propose a machine learning (ML) based ecotoxicological assessment framework that can better capture the environmental risk of industrial agricultural practices. A hybrid dataset containing pesticide concentration levels, soil physicochemical parameters, water quality indices, and biological toxicity endpoints was simulated by gathering real-world data described in the peer reviewed literature to calibrate. Various ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting Executive Manager system with Decision Trees (XGBoost), and Artificial Neural Networks ANN), were applied to generate expected values of ecotoxicological risk indices ERI. The results show that the best models were obtained from XGBoost (R² = 0.93) and Random Forest (R² = 0.91), confirming an effective representation of complex nonlinear ecological interactions in these species assemblages. The feature importance analysis identified pesticide persistence, soil organic carbon, and nitrate leaching as the three main contributors to ecological toxicity. The outer data show that ML-based approaches outperform classical statistical techniques for modeling multivariate ecological interactions across large datasets. Machine learning provides a powerful, scalable, and interpretable framework for ecotoxicological assessment in industrial agriculture. The technique also facilitates early warning of environmental contamination and assists sustainable agricultural decisions.