International Journal of Scientific Progress and Technology Research
Predictive Livestock Localization and Early Disease Detection Using IoT and Data Analytics
Abstract
Prabha S Naik
The livestock industry plays a critical role in the agricultural economy, where animal health, productivity, and mobility directly influence farmer income. Traditional monitoring methods rely heavily on manual observation, which is time- consuming labor-dependent, and prone to delays in detecting early signs of disease or abnormal behavior. With advancements in automation and intelligent sensing, the integration of Internet of Things (IoT) and Machine Learning (ML) enables real-time, data- driven livestock monitoring. This paper presents an IoT-based smart cattle monitoring system capable of tracking cow location, collecting physiological and environmental parameters, and predicting health risk categories using a Random Forest classifier. Additionally, a Random Forest regressor is used to estimate milk yield based on temperature, activity levels, and feed patterns. The system incorporates GPS temperature, and accelerometer sensors to collect live data, which is processed and visualized on a stream lit dashboard for improved decision-making. Results demonstrate successful implementation of real-time monitoring, accurate disease risk classification, and consistent milk yield prediction, enabling proactive management and improved farm efficiency. This approach contributes towards scalable, automated and cost-effective dairy solutions suitable for small to large-scale farms. Index terms IoT, Machine Learning, Random Forest, Livestock Monitoring, Disease Prediction, GPS Tracking, Smart Dairy Farming.
