Cow Disease Recognition Using Convolutional Neural Network

Abstract

In the South Asian region, cow illness is a regular occurrence. Every season, poor rural residents, as well as dairy farm owners, face the same issue. Dairy farming is a large and developing agricultural sector in Bangladesh - a country of South Asia. A large portion of the population relies on cattle for a living. Cow milk is a source of pure nutrition in Bangladesh hence cows provide the bulk of milk and meat. Every year, farmers lose a lot of money due to cow diseases. Cow diseases reduce the amount of milk and meat produced. Recognizing cow diseases has thus become a critical undertaking to be fulfilled. In recent years, deep learning has been gaining a lot of popularity because of its superior accuracy when trained with a lot of data. Using deep learning, we will develop a system to recognize cow diseases. We have begun by reading many online publications, journals, and relevant studies before heading out to the field to tour dairy farms and village habitats. Brucellosis, lumpy skin disease, foot & mouth disease, and mastitis diseases are the four most frequently occurring cow diseases in Bangladesh. Then we have collected pictures of both healthy and diseased cows. We have proceeded to our dataset with five classes after gathering all of the picture data. To recognize cow diseases, we have used three deep learning pre-trained models with our dataset. We have achieved satisfactory results with the MobileNetV2 by attaining accuracy of 95.43%. For this study, we have also used two more pre-trained models, Alexnet and VGG16 which exhibit the accuracy of 86.27% and 90.85% respectively. The MobileNetV2 has not only performed the best in terms of accuracy but also some other indicative performance metrics like sensitivity, specificity, and precision. This research can help us recognize cow diseases quickly and avoid the unexpected loss of our livestock. As well, it also has a bright future in both the domestic and international markets.