Real time Plant Disease detection and Classification using CNN Integrated IOT Framework

Urban agriculture and smart-city ecosystems require efficient solutions for plant health monitoring and waste management. This paper presents a Smart Plant Disease Detection and Waste Management System developed as an Android application using Java/XML, Firebase Authentication, and Firebase Realtime Database. The system integrates a lightweight TensorFlow Lite (TFLite) Convolutional Neural Network (CNN) model to detect plant diseases from leaf images in real time. By employing a mobile-optimized deep learning pipeline, the application provides instant disease classification along with suitable treatment recommendations, enabling early intervention for urban farmers and home gardeners. In addition to plant disease detection, the application incorporates an intelligent waste reporting module that allows users to submit complaints with images, descriptions, and GPS-based location information. Administrators can monitor, manage, and update complaint statuses in real time through cloud-based synchronization. The integration of Google Maps API further enhances the system by displaying nearby waste collectors and providing navigation support for efficient disposal services. By combining image processing, cloud computing, and intelligent automation within a single platform, the proposed system improves crop productivity, enhances urban waste handling efficiency, and supports sustainable smart-city development.