omroy07/AgriTech

**Technical Summary:** The platform combines Flask-based REST APIs with TensorFlow CNNs for disease detection and scikit-learn ensemble models (Random Forest/XGBoost) for crop recommendations and yield forecasting, processing soil data and plant images through a modular ML inference pipeline. It integrates Firebase for authentication and data persistence, Google Gemini API for an AI chatbot with image analysis capabilities, and deploys via Docker with GitHub Actions CI/CD. The dual-stack architecture uses a static HTML/CSS/JavaScript frontend served independently while the backend handles ML model routing and community features like farmer forums and shopkeeper listings.

100 stars.