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CATTLE DISEASE DIAGNOSIS USING DEEP LEARNING

Drethi Shetty, Megha, Prajna S Puthran, Rakshitha

A J Institute of Engineering and Technology, Mangalore

MR. Rakesh M R

(Project Guide)

Assistant Professor, Department of Information Science and Engineering,

A J Institute of Engineering and Technology, Mangalore

Email: rakesh@ajiet.edu.in

Abstract-- Dairy cow productivity to produce milk decreases due to increase in diseases. In usual conditions, dairy cows can produce 12 to 15 litres of milk every day, while dairy cows that are affected by the disease are only able to produce milk 5 to 10 litres every day. The difficulty of early analysis and managing of cows is to monitor the condition of cows that are not carried out at any time and minimum knowledge of Breeders about the illness. Handling the dairy farm is not a simple thing since farmer should manage cattle’s health, food and should produce quality milk for attention of market. At current situation, dairy farmer in rural area needs to go a long distance to buy food for cattle and it is difficult to contact doctor in case of emergency. The precision and the selectivity of the conventional clinical assessment of cattle diseases leave much to be convenient. On the other side, the rural early analysis of diseases with the support of advanced technological systems can improve noticeably the precision and scheduling of disease analysis.