Diagnosing foot and mouth disease and lumpy skin disease in cattle using computer vision

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.