Plant diseases pose a significant threat to agriculture worldwide, impacting both productivity and food security. Effective disease management relies on early detection and accurate diagnosis. Traditional methods, which depend on visual inspection, are often slow and subjective. However, recent advancements in computer vision and machine learning offer promising alternatives. This paper presents an enhanced framework for plant disease segmentation that combines preprocessing with segmentation techniques. The initial preprocessing stage uses median filtering to refine the data, while the segmentation stage employs the Adaptive Pixel Integration in Joint Segmentation (APIJS) method. This approach, a variant of DJS, is designed to accurately isolate disease-affected regions in plant images. By improving the precision and effectiveness of plant disease segmentation, this framework contributes to advancing sustainable agriculture and strengthening global food security.