AI Image-based System for Lumpy Skin Disease Detection in Cattle
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Date
2025
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Publisher
Uganda Christian University
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.