A Case Study on CALVEASE: AI Driven IoT Framework for Dystocia Prediction

The ability to predict cow calving easiness cost-effectively, especially in the dairy industry where cattle suffer from a variety of unpredictable deadly illnesses and high breeding expenses assist farmers in improving reproductive performance and, thereby, farmers profitability. In this research work, machine learning and Internet-of-Things (IOT) based CALVEASE framework for monitoring dairy herd and predicting calving easiness is studied as a multi-classification problem. The CALVEASE framework collects activity information through sensors deployed in cow’s collar belt in the first stage. The collected information stored at the cloud platform is utilized to train machine learning models for predicting the cow calving easiness in the second stage. On empirically testing the various models during field testing, random forest outperforms the other models showing an accuracy of 99.58% hence, the tested framework was found to be efficacious for boosting the dairy herd productivity.