Abstract:
Cattle farming is a major source of income for many people in the Sub-Harran
region, and the livestock sector played a vital role in supporting the region’s
economy and food security [2]. However, the prevalence of infectious cattle
diseases, particularly Lumpy Skin Disease (LSD) and Foot and Mouth
Disease (FMD), posed serious threats to food security, farmer livelihoods, and
animal productivity. Addressing the impacts of these diseases required early
and accurate diagnosis [1].
This study presents a computer vision-based diagnostic model that utilizes
deep learning techniques to detect and classify FMD and LSD in cattle. The
proposed system captured the visible signs of infection using a Convolutional
Neural Network (CNN) trained on a dataset of annotated cattle images [3].
The model achieved high levels of accuracy and robustness under diverse
environmental conditions and was capable of distinguishing between healthy
cattle and those infected with FMD or LSD.
To improve accessibility, the trained model was integrated into a mobile
application, enabling rural farmers and veterinary practitioners to perform
real-time diagnosis without the need for specialized equipment. The study
also addressed challenges related to image data collection in the Sub-Harran
region, such as variations in cattle breeds, lighting conditions, and disease
presentation. Data augmentation and transfer learning techniques were
employed to improve the model’s generalization.
The model’s performance will be evaluated using standard metrics, including
accuracy, precision, recall, and F1-score, with results indicating strong
potential for practical deployment [4]. Overall, this study will offer a scalable
solution for improving cattle disease management through the application of
artificial intelligence, with implications for enhancing disease surveillance,
reducing economic losses, and improving animal health in
resource-constrained settings.
