The 'Integrated Plant Disease Detection System (IPDDS)' presents a comprehensive approach to plant health assessment, combining Image Processing and Convolutional Neural Networks (CNN) for species classification and disease identification. In the initial phase, input images are subjected to preprocessing to remove noise and enhance clarity, followed by segmentation using k-means clustering for effective region identification. A CNN classifier then utilizes deep learning techniques to categorize the plants into distinct species such as apple, core, grape, pepper bell, potato, or tomato. Subsequently, the system employs an improved CNN architecture adapt for disease classification, distinguishing various diseases affecting each plant species. For instance, diseases like Black Rot, Scab, and Cedar Rust are identified in apples, while Common Rust, Northern Blight, and Cercospora are detected in corn. This methodology enhances accuracy and reliability in disease detection, enabling timely interventions to mitigate crop losses. Furthermore, the system suggests suitable fertilizers based on disease diagnosis, facilitating targeted disease management strategies. This integrated approach offers a promising solution for effective plant disease detection, contributing to sustainable agriculture and food security.