Abstract
Lumpy skin disease is a viral infection of cattle. Originally found in Africa, it has also spread to countries in the Middle East, Asia, and Eastern Europe. Clinical signs include fever, lacrimation, hyper salivation, and characteristic skin eruptions. Diagnosis is by histopathology, virus isolation, or PCR. Disease in animals is now a common problem. There are several types of disease in animals, so the identification of disease is important, and diagnosis will be done at a timely pace. Lumpy skin disease in cows is caused by a virus called Neethling. By the affection of these diseases the cattle permanent damage to their skin. The disease often results in reduce milk projection, infertility, poor growth, abortion and sometimes death. The proposed concept is architecture using machine learning techniques to propose the disease or detect the disease. This framework uses tools like VGG-16, VGG-19and Inception-v3 for extracting the features. The work is tested on dataset and is measured with other advanced methodology KNN, SVM, NB, ANN and LR which results in considerable performance in extraction of features.

OBJECTIVES

The main objective of the proposed system are:
• To develop deep learning model which is capable of detecting skin diseases in cows.
• To develop a model which is user friendly to train and test the lumpy skin using image processing.