In this project, we present SpudScan, a comprehensive approach for the semantic segmentation of potato leaf diseases. Utilizing a custom dataset collected and meticulously annotated by our team, we trained and evaluated three state-of-the-art models: Segformer, Unet, and Deeplab V3. Our objective was to accurately identify and segment various disease-affected regions on potato leaves to facilitate early detection and treatment. Comparative analysis of the models highlights their respective strengths and weaknesses in terms of performance, processing time, and robustness. The results demonstrate significant potential for integrating deep learning techniques in agricultural disease management, paving the way for more efficient crop monitoring and health assessment.