Abstract
Lumpy skin disease (LSD) is a highly contagious viral disease of cattle and causes significant economic losses in the livestock industry, around the globe. Early and accurate detection is critical for effective disease management and control in a timely manner. Early-warning digital detection approach such as Convolutional Neural Networks (CNNs) have shown promising results in medical and veterinary diagnostics, Vision Transformers (ViT) remain relatively unexplored in this field. In the proposed research, we used a total of 8000 images of cattle (retrieved from Kaggle) and trained the model to achieve the optimal detection of infections. We used data pre-processing techniques of resizing, normalisation, and augmentation followed by the ViT architecture as a classifier. The model provided excellent performance that showed a 98.12% accuracy, 98.5% precision, 98.5% recall, and a 98.5 F1 score. We have shown that ViT achieves better results in LSD classification compared to multiple approaches that are traditionally used, providing greater accuracy and precision. To encourage adaptation and apply model easier in the field conditions, a mobile application was created and validated on PyTorch Lite and Flutter. Collectively, this powerful approach would change the health management of livestock and allow swifter, and more accurate diagnosis not only to contain the infection but also to counteract its transmission in susceptible livestock.