FarmGenius

Empowering Indian agriculture with AI, real-time data, and a beautiful user experience.

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🚀 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

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✨ 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

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🛠️ 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

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📁 Project Structure

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

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🏗️ Architecture

System Overview

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

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🖼️ 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

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⚡ Setup & Installation

Backend

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

cd frontend
npm install
cp .env.example .env  # Add your API keys
npm run dev

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▶️ Usage

• Open http://localhost:5173 for the frontend.
• Interact with chat, market, yield, and disease features.
• Backend runs on http://localhost:8000 by default.

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💡 Acknowledgements

• AgMarkNet, OpenWeather, Google Maps, Gemini LLM
• Built for hackathons and real-world agricultural impact

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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.

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🤖 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

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💬 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.

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🌐 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.

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🗺️ 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.

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🏗️ 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.

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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.

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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.

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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 ]

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Setup & Installation

Prerequisites

• Python 3.10+
• Node.js 18+
• npm 9+

Backend Setup

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

cd frontend
npm install
cp .env.example .env  # Set VITE_GOOGLE_MAPS_API_KEY
npm run dev

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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.

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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

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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)

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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.

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Team & Acknowledgements

• Built for Hackathons by 404
• Special thanks to: FastAPI, React, Google, OpenAI, AgMarkNet, and the open-source community.

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License

MIT