Intelligent Prediction Of Environmental Stress And Disease Risk In Sustainable Livestock Systems

Abstract

Environmental stress and disease occurrence are major challenges affecting animal welfare, productivity, and sustainability in modern livestock production systems. This study proposes an intelligent machine learning-based framework for predicting environmental stress and associated disease risk through the integrated analysis of environmental, behavioral, physiological, and health-related livestock data. The framework incorporates data acquisition, preprocessing, feature engineering, environmental stress prediction, disease-risk assessment, and sustainability-oriented decision support. Environmental variables such as temperature, humidity, air quality, ventilation conditions, and Temperature-Humidity Index are analyzed together with animal-level indicators including body temperature, respiration rate, activity, feeding behavior, water consumption, and rumination. Multiple machine learning algorithms, including Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, Gradient Boosting, and Artificial Neural Network, are comparatively evaluated. The results indicate that Gradient Boosting provides the strongest predictive performance, achieving 95.21% accuracy for environmental stress prediction and 94.36% accuracy for disease-risk prediction. The proposed framework enables early identification of potentially unfavorable conditions and supports preventive interventions related to environmental control, animal monitoring, and health management. The integration of intelligent prediction with livestock monitoring can contribute to improved animal welfare, reduced health-related losses, efficient resource utilization, and more sustainable livestock production.