We examine the integration of deep learning and image processing techniques for the detection of plant leaf diseases, with a focus on the incorporation of saliency maps to enhance model interpretability. Through an analysis of various methodologies and classification techniques, we highlight the significance of considering crop-specific characteristics in disease segmentation. Despite notable advancements, a crucial research gap persists in the interpretability of models, necessitating further refinement of saliency map techniques. By addressing this challenge, we envision a transformative impact on agricultural disease detection, fostering global food security and sustainable farming practices through informed interventions and minimized crop losses.