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Livestock plays a crucial role in improving the economy of farmers but managing and monitoring livestock health pose significant challenges. With the advent of IoT and cloud computing, farmers now have the opportunity to intelligently monitor the health of their cattle. By leveraging machine learning techniques, the analysis of data collected from IoT devices enables the prediction of livestock health status. This paper presents an IoT-based cattle health monitoring system that tracks vital signs such as body temperature and heartbeat. The vital sign data is stored in the cloud and made available to the prediction system. To enhance the accuracy of health status predictions, this study introduces enhanced NeuroFuzzy Systems, which combine Multilayer Perceptron (MLP) and fuzzy inference systems. Experimental results demonstrate that the proposed system significantly improves the accuracy of health status prediction for livestock.
Vigneswari et al. (Wed,) studied this question.