IoT based Animal Health Monitoring with Naive Bayes Classification

Tejaswinee A. Shinde, Dr. Jayashree R. Prasad


Agriculture is the major supply of financial gain in rural areas. It is therefore essential for farmers to execute efficient and technical methods to increase productivity, reduces the animal husbandry and crop-related problems. The advances in computing and information storage have provided vast amounts of data. The challenge has been to extract knowledge from this raw data that has lead to new methods and techniques such as data mining that can bridge the knowledge gap. The proposed system aimed to assess the data mining techniques and apply them to Animal database to establish meaningful relationships. This study focuses on the Naive Bayes Classification method of data mining to classify the animal sensor data. The proposed system consist of animal health care benefiting the farmers by using Wireless Sensor Network technology and IoT applications. Wireless sensor devices mounted in the farm will collect the physical information of sensors (temperature, heart Rate, load cell) of animal and this data pass it onto the Micro-controller individually. And the Sensor Calibration (SC) technique shows the values (temperature, humidity, moisture) in voltage. The data import API uses Internet HTTP connection over GPRS, Wi-Fi, or wired connection. The IoT platform is responsible for the integration and management of sensor data. The database contains all sensor readings. Now by using Naive Bayes classification method, new sensor readings compare with initial readings and if any drastic change in readings then displays the notification on user PC. Finally this proposed system the Animal Health Monitoring will help to enhance the productivity and save the time and efforts of the farmer.

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