Potato Disease Diagnosis Using YOLOv5 and Web-Based Deployment
| dc.contributor.author | James Alala Munjwok | |
| dc.date.accessioned | 2025-06-12T11:09:52Z | |
| dc.date.available | 2025-06-12T11:09:52Z | |
| dc.date.issued | 2025-05-06 | |
| dc.description.abstract | Agriculture plays a central role in Uganda’s economy, and potatoes are among the most important staple crops. However, potato yields are significantly threatened by diseases such as early blight and late blight. Timely detection of these diseases is critical to reduce losses, minimize pesticide misuse, and enhance food security. This project presents a web-based potato leaf disease diagnosis system using YOLOv5, a state-of-the-art deep learning object detection model. The system classifies potato leaves as healthy, early blight, or late blight. The backend is implemented using FastAPI and deployed to Render, while the frontend is built in React and hosted on Vercel, ensuring accessibility via modern web browsers. The model was trained on the PlantVillage dataset. Evaluation results show that the system achieved high accuracy and fast inference times, making it suitable for use by farmers and agricultural officers. This report details the system design, methodology, model training, deployment, and perfor- mance evaluation. Limitations such as environmental noise, internet dependency, and limited disease coverage are acknowledged, and future work includes expanding the disease scope, offline deployment, and integrating treatment recommendations. This work contributes to the growing field of AI-powered agriculture in Uganda and aligns with the Sustainable Development Goals for food security and smart farming. | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12311/2700 | |
| dc.language.iso | en_US | |
| dc.publisher | Uganda Christian University | |
| dc.title | Potato Disease Diagnosis Using YOLOv5 and Web-Based Deployment | |
| dc.type | Thesis |