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# FarmGenius > **Empowering Indian agriculture with AI, real-time data, and a beautiful user experience.** --- ## 🚀 Overview FarmGenius is an advanced, multi-agent agricultural assistant designed for Indian farmers, agri-entrepreneurs, and researchers. It combines AI-driven chat, real-time yield prediction, market price forecasting, and disease diagnosis in a unified, modern platform. - **AI Chat Assistant**: Nationwide agricultural expertise powered by Gemini LLMs - **Market Price Prediction**: Real-time and historical mandi prices, time series forecasting - **Yield Prediction**: Weather-aware, map-based, ML-driven yield estimates - **Disease Diagnosis**: Crop image upload and instant disease detection - **Professional UI/UX**: Responsive, modern design --- ## ✨ Features - **Multi-Agent Chat**: Conversational assistant with intent routing and context memory - **Market Data**: AgMarkNet scraping, static fallback, and forecasting - **Yield Prediction**: Google Maps farm selection, OpenWeather integration, ML models - **Disease Detection**: Deep learning or Gemini Vision analysis of crop images - **Nationwide Coverage**: All major crops, markets, and Indian states - **Modern UI**: Shareable conversations, streaming responses, mobile-first --- ## 🛠️ Tech Stack **Backend:** - Python, FastAPI - Multi-agent system (Gemini LLM integration) - Web scraping (AgMarkNet) - ML models: yield, disease - Caching, static data fallback **Frontend:** - React (Vite) - Modular feature structure - Google Maps JS API, OpenWeather API - Modern CSS Modules, context/hooks --- ## 📁 Project Structure ```plaintext 404/ ├── backend/ │ ├── app/ │ │ ├── api/ # FastAPI endpoints (market, yield, disease, chat) │ │ ├── core/ # Multi-agent logic, AI services, config │ │ ├── data/ # Static/fallback data │ │ ├── models/ # Data models │ │ ├── services/ # Scrapers, ML services │ │ └── main.py # FastAPI app entry point │ ├── requirements.txt │ └── .env ├── frontend/ │ ├── src/ │ │ ├── features/ │ │ │ ├── chat/ # Chat UI, avatars, animation │ │ │ ├── market/ # Market price prediction UI │ │ │ ├── yield/ # Yield prediction with map/weather │ │ ├── components/ # Shared components │ │ ├── services/ # API calls │ │ └── theme/ # Theme (dark/light mode) │ ├── public/ │ ├── package.json │ └── .env └── README.md ``` --- ## 🏗️ Architecture ### System Overview ```mermaid flowchart TD subgraph Frontend [React Frontend] ChatUI[Chat Assistant UI] MarketUI[Market Predictor UI] YieldUI[Yield Predictor UI] DiseaseUI[Disease Diagnosis UI] MapComp[Google Maps Integration] WeatherComp[Weather API Integration] end subgraph Backend [FastAPI Backend] APIMain[main.py] MarketEP[Market Endpoints] YieldEP[Yield Endpoints] DiseaseEP[Disease Endpoints] ChatEP[Chat Endpoints] Agents[Multi-Agent System] Scraper[AgMarkNet Scraper] YieldML[Yield ML Model] DiseaseML[Disease ML Model] StaticData[Static Market Data] end subgraph External GoogleMaps[Google Maps API] OpenWeather[OpenWeather API] AgMarkNet[AgMarkNet Portal] Gemini[Gemini LLM] EarthEngine[Earth Engine API] end ChatUI -->|REST| ChatEP MarketUI -->|REST| MarketEP YieldUI -->|REST| YieldEP DiseaseUI -->|REST| DiseaseEP MapComp --> GoogleMaps WeatherComp --> OpenWeather MarketEP --> Scraper MarketEP --> StaticData YieldEP --> YieldML YieldEP --> WeatherComp YieldEP --> EarthEngine DiseaseEP --> DiseaseML ChatEP --> Agents Agents --> Gemini Scraper --> AgMarkNet ``` --- ## 🖼️ Wireframes ### Home/Dashboard - **Header**: Logo, navigation (Chat, Market, Yield, Disease), dark/light toggle - **Main**: Quick links to features, latest market/yield highlights, user tips ### Chat Assistant - **Left**: Conversation history, agent avatars - **Center**: Chat window (Markdown, streaming), input box, send button - **Right**: Contextual tips, share conversation ### Market Predictor - **Top**: Select commodity, state, market - **Main**: Price chart (historical & predicted), market info, refresh button - **Side**: Data source info, last updated ### Yield Predictor - **Map Panel**: Google Maps with farm selection - **Form**: Crop, season, acreage, weather (auto-filled) - **Output**: Predicted yield, recommendations ### Disease Diagnosis - **Upload**: Image upload box - **Result**: Detected disease, advice, treatment suggestions --- ## ⚡ Setup & Installation ### Backend ```bash cd backend python -m venv venv source venv/bin/activate # or venv\Scripts\activate on Windows pip install -r requirements.txt cp .env.example .env # Add your API keys uvicorn app.main:app --reload ``` ### Frontend ```bash cd frontend npm install cp .env.example .env # Add your API keys npm run dev ``` --- ## ▶️ Usage - Open `http://localhost:5173` for the frontend. - Interact with chat, market, yield, and disease features. - Backend runs on `http://localhost:8000` by default. --- --- ## 💡 Acknowledgements - AgMarkNet, OpenWeather, Google Maps, Gemini LLM - Built for hackathons and real-world agricultural impact --- > For questions or demo requests, open an issue or contact the maintainers. ## 🧠 How FarmGenius Works: Advanced Architecture & Intelligence ### Multi-Agent System & Model Context Protocol FarmGenius uses a **multi-agent architecture** on the backend. Instead of a single AI module, there are multiple specialized agents: - **Yield Agent:** Handles crop yield predictions. - **Disease Agent:** Analyzes plant images for disease detection. - **Market Agent:** Scrapes and predicts market prices. - **Chat Agent:** Provides conversational AI support for agricultural queries. **Why Multi-Agent?** - **Separation of Concerns:** Each agent is focused and easier to maintain/upgrade. - **Extensibility:** New agents (e.g., weather, pest, soil) can be added easily. - **Parallelism:** Agents can work independently or together, improving performance and reliability. #### Model Context Protocol This protocol allows agents to share context and state for smarter responses: - **Context Sharing:** Agents exchange info (e.g., user location, crop type, previous queries) to provide holistic answers. - **Stateful Interactions:** Enables the system to remember past interactions and tailor recommendations. - **Example:** If a user asks for a yield prediction and then asks about disease risk for the same crop, the Disease Agent can access info from the Yield Agent’s context. --- ### 🤖 AI Vision & Image Analysis - **Disease Detection:** Users upload crop images. The backend runs a vision model (deep learning or Gemini Vision API) to analyze the image, returning the detected disease, confidence score, and actionable tips. - **Pipeline:** Image → Preprocessing → AI Model → Analysis → Response --- ### 💬 Multi-Agent Chat Assistance - **Gemini 1.5 Pro:** The chat agent uses Google’s Gemini model for natural, context-aware conversations. - **Coverage:** Answers agri questions for all of India. - **Markdown & Rich Formatting:** Responses are formatted for clarity (bullet points, code blocks, etc.). - **Streaming & Sharing:** The chat UI supports streaming responses and lets users share conversations. - **Intent Routing:** The chat agent can delegate queries to domain agents (e.g., yield, market) and aggregate their responses. - **Context Awareness:** Maintains conversation context for multi-turn dialogues. --- ### 🌐 Real-Time Market Scraping - **AgMarkNet Scraper:** Fetches live mandi prices for major crops across India. - **Nationwide Coverage:** Supports all major agricultural markets. - **Caching:** Avoids excessive requests by caching recent data. - **Integration:** Market data is used for both chat responses and the market predictor UI. --- ### 🗺️ Yield Prediction with Geospatial Intelligence - **Google Maps Integration:** Users can select their farm location for precise predictions. - **Weather & Soil Data:** Uses APIs and default coefficients for region, season, and crop type. - **Recommendations:** Provides actionable tips along with yield estimates. --- ### 🏗️ Extensible & Modern Architecture - **FastAPI Backend:** Modular, async, and production-ready. - **React + Tailwind Frontend:** Beautiful, mobile-first UI with animated transitions. - **REST API:** Clean, versioned endpoints for all features. - **Environment Variables:** Secure management of API keys for Gemini, Google Maps, and OpenWeather. --- **In summary:** 1. **User interacts with the frontend** (uploads image, asks question, selects location). 2. **Frontend calls the backend API** (RESTful endpoints). 3. **Backend routes the request** to the appropriate agent(s) using the multi-agent system. 4. **Agents process the request**, possibly sharing context or fetching external data. 5. **Response is aggregated** and sent back to the frontend. 6. **Frontend displays the result** with rich UI/UX. --- ## Features - **Yield Prediction:** Predict crop yields using AI, Google Maps for location, and weather/soil data. - **Disease Detection:** Upload plant images to detect diseases using AI models. - **Market Price Prediction:** Get real-time prices for crops across India via AgMarkNet scraping. - **AI Chat Assistant:** Ask agricultural questions and get contextual answers (powered by Gemini). - **Modern UI:** Responsive, mobile-first React + Tailwind interface. - **Multi-agent Backend:** Extensible system for integrating more AI/ML services. --- ## Architecture - **Frontend:** React, Tailwind CSS, Vite, React Router, Axios - **Backend:** FastAPI, Python, Gemini API, OpenWeather, Google Maps, AgMarkNet Scraper - **Communication:** REST API (`/api/v1/...`) - **Multi-Agent System:** Context protocol for extensibility ``` [ React + Tailwind ] <--> [ FastAPI Backend ] <--> [ AI/ML APIs, Scrapers, DB ] ``` --- ## Setup & Installation ### Prerequisites - Python 3.10+ - Node.js 18+ - npm 9+ ### Backend Setup ```bash cd backend python -m venv venv source venv/bin/activate # On Windows: venv\Scripts\activate pip install -r requirements.txt cp .env.example .env # Set API keys for Gemini, Google Maps, OpenWeather uvicorn app.main:app --reload ``` ### Frontend Setup ```bash cd frontend npm install cp .env.example .env # Set VITE_GOOGLE_MAPS_API_KEY npm run dev ``` --- ## Usage - **Yield Predictor:** Enter crop, area, state, and soil/weather info. Select location on map. Get instant yield prediction and recommendations. - **Disease Detector:** Upload or drag/drop a plant image. Get AI-powered disease analysis and management tips. - **Market Prices:** View real-time mandi prices for major crops, powered by live scraping. - **AI Chat:** Ask any agri question and get a contextual, India-specific answer. --- ## API Endpoints - `POST /api/v1/yield/predict` — Predict crop yield - `POST /api/v1/disease/detect` — Detect plant disease from image - `GET /api/v1/market/prices` — Get current mandi prices - `POST /api/v1/chat/ask` — AI chat assistant --- ## Tech Stack - **Backend:** FastAPI, Python, Gemini API, OpenWeather, Google Maps, AgMarkNet Web Scraper - **Frontend:** React, Tailwind CSS, Vite, React Router, React Icons, Axios - **Testing:** Pytest (backend), ESLint (frontend) --- ## How it Works ### Yield Prediction - User enters crop, area, state, soil, and weather data. - Optionally selects exact farm location via Google Maps. - Backend uses crop coefficients, weather, and soil factors to predict yield. - Returns yield per hectare and total production, plus actionable recommendations. ### Disease Detection - User uploads a plant image. - AI model analyzes the image and returns disease diagnosis and management advice. ### Market Prices - Backend scrapes AgMarkNet for real-time mandi prices across India. - Data is cached and aggregated for fast, reliable access. ### AI Chat Assistant - Seamlessly integrated into the platform with a modern, responsive chat UI (matching the overall design system). - Powered by Gemini 1.5 Pro for natural, conversational, and context-aware responses. - Supports Markdown formatting, streaming replies, and sharing of conversations. - Understands context from other features (e.g., yield, market, disease) for truly smart, multi-turn assistance. - Designed specifically for Indian agriculture: covers all major crops, regions, and agri practices. ### Multi-Agent System - Backend is built to allow easy addition of more AI/ML agents and features. --- ## Team & Acknowledgements - Built for Hackathons by 404 - Special thanks to: FastAPI, React, Google, OpenAI, AgMarkNet, and the open-source community. --- ## License MIT