CATTLEX – An AI-Integrated, Solar-Powered IoT System Enhanced with Predictive Analytics for Multi-Disease Management in Livestock

The paper describes an AI-driven system for the monitoring of livestock health using sensors from the Internet of Things and machine learning for promoting sustainable agricultural practices. In this approach, real-time data is gathered by pulse, temperature, and accelerometer sensors to detect anomalies in the health conditions of animals. Disease prediction is performed using Random Forest, Gaussian Naïve Bayes, and Decision Tree classifiers. Experimental evaluation on 2,044 cattle health records spanning 27 diseases shows that the Random Forest classifier achieves the best performance with an accuracy of 92.31%, precision of 89.74%, recall of 92.31%, and F1-score of 90.38%. The proposed framework enables early disease detection and proactive livestock health management while reducing mortality rates and improving productivity. Veterinary consultation support is also integrated for timely expert intervention.