AI Image-based System for Lumpy Skin Disease Detection in Cattle
| dc.contributor.author | Amos Mugabi | |
| dc.date.accessioned | 2025-06-12T11:30:32Z | |
| dc.date.available | 2025-06-12T11:30:32Z | |
| dc.date.issued | 2025 | |
| dc.description.abstract | Lumpy Skin Disease (LSD) remains a significant threat to cattle health across Uganda, with conventional disease detection methods being slow, centralized, and reliant on clinical expertise that is often unavailable in field settings. This project proposes an innovative solution through an AI-powered, image-based detection system capable of identifying LSD from cattle images. The system employs a twostage deep learning architecture: a YOLOv8 object detection model locates individual cattle within images, followed by a convolutional neural network (CNN) that classifies each animal as either healthy or infected based on visible skin lesions. Trained on a diverse dataset of annotated cattle images, the integrated model achieved a high detection precision and classification accuracy, demonstrating strong reliability in recognizing signs of LSD. Furthermore, the system offers real-time feedback via an interactive web interface, enabling farmers and veterinary personnel to quickly assess cattle health with images. This approach not only enhances detection and control measures but also sets the stage for broader adoption of AI in livestock health management within low-resource environments. The system’s design aligns with global goals of smart agriculture, offering a scalable tool that supports both food security and disease resilience. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12311/2702 | |
| dc.language.iso | en | |
| dc.publisher | Uganda Christian University | |
| dc.title | AI Image-based System for Lumpy Skin Disease Detection in Cattle | |
| dc.type | Dissertation |