Teat and Udder Disease Detection on Cattle using Machine Learning

In high milk yielding cattle udder and teat diseases are common. Due to this the farmers are facing more problem in production and quality of milk. In agriculture, research of automatic cow disease is essential in monitoring large population of cows, and If symptoms appear on the udder or teat, the disease should be automatically detected immediately. There is a need for automatic system where the Cow Teat and Udder diseases are detected, the effect of disease on milk yield and what medicines should be used for its cure. The aim of this work is to solve the problem of detecting and preventing diseases of different breed of cows. To determine the optimal architecture for deep learning, several models are considered. As a source of the training data, the cow teat and udder images of cow in villages are used. For this approach automatic classifier Convolutional Neural Networks (CNN) model will be used for training the model based on learning with some training samples. The developed model is then considered for classification using Support Vector Machine (SVM) deployed as a web Application which can detect 3 types of diseases among cows viz. Mastitis, Udder score and Frozen teats also classifying healthy cows among the datasets. Validation Accuracy Results show that the system can show the accuracy 93.50 % and Training Accuracy is approximately 97.87%