Published Paper Details:

Agricultural disease management remains vital in the field because plant diseases directly affect crop production levels and they create threats for food safety together with economic stability. The conventional approach to plant disease diagnosis consists of agricultural experts conducting visual examinations which prove to be irregular yet lengthy and prevents adequate services to farmers who reside beyond normal accessibility. The Smart Agro-Cure project specifies the development of an AI-powered pesticide recommendation system based on Convolutional Neural Networks which function through image input. The system uses leaf images for automatic disease detection which then recommends appropriate pesticides to improve both accuracy and efficiency in accessible disease management operations. The deep learning model reaches high precision identification of diseases because it receives training on multiple plant disease varieties within its diverse dataset. A curated pesticide database within this system enables users to get green and optimal treatment suggestions. Image preprocessing methods including resizing, normalization and augmentation help the project reach higher accuracy because they enhance model generalization abilities. Through the Flask-based user-friendly graphical interface (GUI) system users can easily upload images to get immediate disease analysis. The software demonstrates excellent scalability together with deployment features that make it suitable for mobile applications and edge computing platforms to serve farmers worldwide.