Classification of Cattle skin Disease using Deep Learning approach.

Abstract

Cattle skin diseases are particular kinds of diseases caused by tumors, allergies, parasites, autoimmune; bacteria infectious, and viral diseases and many cattle skin diseases are very dangerous, mostly if not treated at their first stage. These diseases have caused economic losses in different aspects especially in day-to-day usage as they reduce milk yield, leather quality, and performance in draft cattle. This is huge loss of livestock population by a disease that undermines the efforts towards achieving food security and poverty reduction and cattle skin diseases are becoming the most infectious diseases occurring in cattle of all ages and often found in humans and animals. In the contribution of the cattle skin disease classification many researches have been conducted even if they have gap Different kind of cattle skin like cattle’s located only in Ethiopia To address this problem, we propose an approach for cattle skin disease diagnosis by integrating image processing using deep learning. In this study, 3 model have been developed 2 of them are based up on pre-trained models while one of the model have been developed by ourselves from scratch and to develop the classification model, we have collected 444 cattle skin disease image from Guji and Jimma Zones that we have classify in to Circle Worm Skin Disease, Lumpy Skin Disease, Wart Skin Disease and one Normal class. We split the dataset into 80% for training and 20% for testing. Our CNN scored 85.8% test accuracy and InceptionV3 Model obtained 95.6% test accuracy while EffeicenitnetB0 Model classifies the input symptom image with 99.1 % accuracy