Crop diseases are a major threat to agricultural productivity and food security worldwide, directly impacting the achievement of SDG 2 – Zero Hunger established by the United Nations. Early detection of plant diseases is crucial to prevent yield loss, enhance food availability, and ensure sustainable farming practices. Traditional methods of identifying crop diseases rely on manual inspection by experts, which can be time-consuming, costly, and prone to human error. This project presents an intelligent system for early detection of crop diseases using image processing techniques and Convolutional Neural Networks (CNN). The proposed method begins with capturing high-resolution images of crop leaves using a mobile camera. The images undergo pre-processing steps such as noise removal, resizing, and contrast enhancement to improve image quality. After pre-processing, image segmentation is performed to isolate the diseased region from the healthy portion of the leaf. Feature extraction is automatically handled by the CNN model, which identifies patterns related to color variations, texture irregularities, and shape distortions. The extracted features are passed into the trained CNN classifier, which categorizes the leaf as healthy or affected by a specific disease. The system provides accurate and real-time disease prediction. By enabling early intervention, this approach reduces dependency on manual diagnosis, minimizes crop loss, increases agricultural productivity, and strengthens food security. Therefore, the proposed system directly contributes to achieving SDG 2 – Zero Hunger by supporting sustainable agriculture and improving global food production through precision farming technologies.