AI Based disease prediction and cultivation advisory system for millet crops

The detection and treatment of diseases in millet crops is crucial for ensuring healthy yields. This project aims to develop an automated system that can identify diseases like Rust and Blast in millet crops by analyzing images. The system leverages deep learning algorithms, including ResNet, MobileNet, and DenseNet, to classify crop health based on uploaded images. After detecting the disease, the system provides static suggestions on how to treat the disease, as well as information about pesticides, cultivation techniques, and precautionary measures. These suggestions are provided in three languages: Telugu, Hindi, and English, making the system accessible to a diverse range of farmers. The backend of the system is powered by Python with Flask, while the frontend utilizes HTML, CSS, and JavaScript for an interactive user interface. The proposed solution aims to provide farmers with quick, actionable insights, improving crop health and maximizing yield potential. By leveraging deep learning and multi-language support, this system offers a user-friendly platform for millet farmers to manage crop diseases effectively, thus contributing to agricultural sustainability and improving overall crop productivity.

Keywords: Millet crops, Disease detection, Rust, Blast, Deep learning, ResNet, MobileNet, DenseNet, Crop health, Pesticides.