AgriSense - AI Powered Smart Farming 🌱

AgriSense is a comprehensive, AI-driven agricultural platform designed to empower farmers with real-time insights, intelligent recommendations, and community support. It bridges the gap between traditional farming and modern technology, ensuring better yields, sustainable practices, and improved profitability.

Rebuild Roadmap (Active)

The repository is currently entering a staged rebuild to support backend-focused production architecture:

• Main API service: Node.js + Express (backend-node/)
• Frontend application: React (frontend/, migration in progress)
• ML inference service: Python (ml-service/)
• Planning and contracts: coreplan.md and shared-docs/

Legacy implementation remains available in existing backend/ and frontend/ assets during migration.

🌟 Key Features

AgriSense offers a wide array of tools tailored for the modern farmer:

🧠 AI & Machine Learning Core

• Targeted Crop Recommendation: Uses Random Forest algorithms to suggest the most suitable crops based on NPK levels, pH, rainfall, temperature, and location.
• Smart NPK Auto-Fill: Automatically fetches average soil nutrient profiles for districts in North Karnataka, simplifying data entry.
• Pest & Disease Detection: Upload photos of affected crops to instantly identify diseases or pests and receive organic/chemical treatment advice.
• AI Farming Assistant: A 24/7 Chatbot powered by LLMs (OpenRouter) capable of answering complex farming queries in real-time.
• Voice-First Interface: integrated with Deepgram for high-accuracy voice-to-text, allowing farmers to interact with the app using voice commands in their native language.

🚜 Farming Tools

• Profit & Loss Calculator: A dedicated financial tool to track farming expenses (seeds, fertilizers, labor) vs. revenue, helping farmers analyze their profit margins.
• Soil Testing Kit: Integrated service to request affordable soil testing kits directly to the farm.
• Crop Calendar: Customized schedules for sowing, irrigation, and harvesting based on the selected crop and season.
• Fertilizer Guide: Detailed instructions on fertilizer application rates and methods.

🌐 Information & Community

• Real-time Market Prices: Live updates on crop prices in various markets (APMC) to ensure fair trade.
• Government Schemes: A curated list of agricultural subsidies and government welfare schemes with application details.
• Helpline Directory: Direct access to Kisan Call Centers and State Agriculture Departments.
• Learning Hub: A repository of farming tutorials, guides, and best practices.
• Community Forum: A platform for farmers to connect, share success stories, upload photos, and ask for peer advice.

📱 Accessibility & Design

• Multilingual Support: Fully localized in English, Hindi (हिंदी), and Kannada (ಕನ್ನಡ).
• Offline Capability: Designed to work partially offline in low-connectivity rural areas.
• Responsive UI: Built with Tailwind CSS for a seamless experience on mobile devices and desktops.

🛠️ Technology Stack

• Frontend: HTML5, Tailwind CSS, Vanilla JavaScript (Modular ES6 architecture)
• Backend: Python, Flask (REST API)
• Machine Learning: Scikit-Learn (Random Forest), Pandas, NumPy
• AI Services:
  • OpenRouter (LLM for Chatbot)
  • Deepgram (Voice Transcription)
• Deployment: Ready for Render/Heroku (Gunicorn WSGI)

📂 Project Structure

agrisense/
├── backend/
│   ├── app.py               # Main Application Server (Flask)
│   ├── model/               # Serialized ML Models (.pkl)
│   ├── data/                # Datasets (Soil averages, Crop content)
│   └── requirements.txt     # Python Dependencies
├── frontend/
│   ├── index.html           # Landing Home Page
│   ├── crop-recommendation.html  # Prediction Tool
│   ├── pest-detection.html  # Image Analysis Tool
│   ├── profit-loss.html     # Calculator Tool
│   ├── community.html       # Social Forum
│   ├── js/
│   │   ├── config.js        # API Configuration
│   │   ├── common.js        # Shared Navigation & Validations
│   │   └── chatbot.js       # AI Chat Logic
│   └── ... (other pages)
└── README.md                # Documentation

🚀 Getting Started

Prerequisites

• Python 3.8+
• Git

Local Installation

1. Clone the Repository

   git clone https://github.com/Basavaraj8143/agrisense.git
   cd agrisense

2. Set up Virtual Environment

   python -m venv venv
   # Windows
   .\venv\Scripts\activate
   # Mac/Linux
   source venv/bin/activate

3. Install Dependencies

   pip install -r backend/requirements.txt

4. Configure Environment Create a .env file in the root directory:

   DEEPGRAM_API_KEY=your_deepgram_key_here
   OPENROUTER_API_KEY=your_openrouter_key_here
   FLASK_DEBUG=True

5. Run the Application

   python backend/app.py

   Access the app at http://localhost:5000

🌍 Deployment

The project is configured for easy deployment on Render.

1. Push code to GitHub.
2. Create a new Web Service on Render linked to your repo.
3. Build Command: pip install -r backend/requirements.txt
4. Start Command: gunicorn --chdir backend app:app
5. Add your API keys in the environment variables settings.

🤝 Contributing

We welcome contributions!

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/AmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/AmazingFeature)
5. Open a Pull Request

📄 License

Distributed under the MIT License. See LICENSE for more information.

---

Empowering Farmers, One Click at a Time. 🌾