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.
