Crop disease detection poses significant challenges for smallholder farmers in developing countries, as their limited resources hinder their ability to detect diseases accurately and promptly, potentially leading to substantial losses. Millet crops, susceptible to diseases such as leaf blasts and leaf spots, suffer from reduced yield and degraded product quality. Manual identification of visually similar diseases is time-consuming and prone to errors. Hence, there is an urgent need to automate the detection and classification of crop diseases. This research presents a novel approach for detecting Millet crop diseases using Image Processing and Deep Learning techniques. A dataset comprising authentic field images of millet crop leaves is utilized to ensure the practical applicability of the proposed solution. An integrated approach incorporating the GrabCut algorithm with Masking is developed for Image Processing, enabling precise segmentation of diseased regions while eliminating background interference in crop images. Subsequently, various deep learning models are trained using this approach, and their performances are compared. Experimental results demonstrate that ResNet-50 achieves the highest accuracy of 94%, exhibiting excellent precision and recall. The proposed approach holds significant potential to enhance agricultural yield and product quality, providing an effective tool for farmers.