Lumpy Skin Disease (LSD) in bovine detection using Convolutional Neural Network / Muhammad Danish Alim Muhammad Azlan Lim

Lumpy Skin Disease (LSD) is a serious viral infection that affects cattle and water buffalo, leading to major financial losses in the livestock industry. This study focuses on creating a tool to help detect LSD using a computer system that analyses images. The aim is to make it easier and faster for farmers and veterinarians to identify infected animals and take action. The project follows a clear process that includes collecting images of cattle, improving the quality of these images, and building a disease detection prototype that can recognize signs of the disease. The prototype uses a method called Convolutional Neural Network (CNN) to analyse the images. The design of the system was guided by information from existing studies and tested carefully to ensure it works well. The results show that the tool can accurately identify LSD from images of cattle, making it a useful option for farmers and veterinarians. Testing shows the system is reliable and performs well in real-life situations as the accuracy rate had reached approximately 83%. In conclusion, this study provides an easy-to-use tool for detecting LSD, showing how technology can help improve animal care. Future improvements should include expanding the dataset to incorporate images of buffalo and goats to enhance the model's generalization capability.