Classification Of Goat And Sheep Skin Diseases Using Deep Learning Approaches

Abstract

Animal farm has played one of critical roles in the socio economic wellbeing of most developing countries, Ethiopia being one of them as sheep and goats play major role in providing food security, generate incomes and export income. Nevertheless, there are a number of skin diseases, which have a severe impact on the quality, and yields of animal products. In the rural set ups, they are not handled well because of the inaccessibility of veterinary clinics and shortage of expertise in the field. In this paper, an enhanced method of deep learning approach provided to identify and classify skin diseases in sheep and goats with deep learning. The system automation CNN architectures mean that features are automatically trained by skin images by which skin diseases can be classified and classified to either bacteria and virus, parasite or healthy. The evaluated and tested model underwent testing and evaluation on the standard measures, which include accuracy, precision, recall and F1-score. The data employed in this thesis is 2341 images of data. The training dataset classified as 80% (1873 images), 10% (234 images) and 10% (234 images) in training, validation and test performance respectively.