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@article{199154,
        author = {Komal Gopal Chavan and Prof. S. N. Vidhate},
        title = {Livestock Health Monitoring for Cattle},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {11},
        pages = {12960-12963},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=199154},
        abstract = {Cattle health monitoring is a critical part of modern livestock management, yet most farmers still rely on manual observation to detect diseases or abnormal behavior. This often leads to delayed treatment, reduced productivity, and increased economic loss. With growing concerns about animal welfare and farm efficiency, there is a need for an automated and reliable system that can continuously track cattle health. To address this, we developed an intelligent livestock health monitoring system that uses IoT sensors and Machine Learning (ML) to detect abnormal temperature, movement patterns, and distress sounds in cattle. The system integrates the ESP32 microcontroller, DS18B20 temperature sensor, MPU6050 motion sensor, and INMP441 microphone for real-time data collection. A CNN-based audio classifier detects abnormal vocalization patterns, while an MLP-based sensor classifier analyzes temperature and motion anomalies. A hybrid decision fusion model combines both predictions to determine the cow’s final health status with high reliability. All processed data and predictions are stored in Firebase, and the farmer is notified instantly through a dedicated mobile/web dashboard. This approach provides farmers with accurate insights, early disease detection, continuous monitoring, and easy access to health reports in a simple and user-friendly platform.},
        keywords = {Cattle Health, IoT Sensors, Temperature Monitoring, Motion Analysis, CNN, MLP, Audio Classification, Firebase, Livestock Management.},
        month = {April},
        }