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
