Diseases are the causes of the decrease dairy cow productivity to produce milk. in normal conditions, dairy cows can produce as much as 12 until 15 liters of milk every day, while dairy cows that are affected by the disease are only able to produce milk as much as 5 until 10 liters every day. The difficulty of early detection and handling of cows that are affected by disease is caused by monitoring the condition of cows that are not carried out at any time, as well as limited knowledge of Breeders about the disease. This study aims to develop a dairy cow health management system, from health monitoring until the detection and handling of cows that have been affected by the disease. We combine monitoring systems and detection systems into one application utilizing Internet of Things and Intelligent System technology. The monitoring system processes the temperature and heart rate data of cows from the sensor, then gives results of a cow's health condition, normal or abnormal. The detection system processes symptom data in cows inputted by Breeders, then gives results of estimates of disease diagnoses, treatment and prevention methods. Experiments shows that the monitoring system can monitor health conditions in dairy cows based on temperature and heart rate with an error rate of 0.6 degrees Celsius and 3.5 Beats perminute. Experiments on the detection system shows that the detection system can diagnose diseases in dairy cows based on physical symptoms with an accuracy rate of 90 percent.
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Faruq et al. (2019) studied this question.