ShetNiyojan

ShetNiyojan is a comprehensive agricultural management system designed to empower farmers with data-driven insights for optimizing crop cultivation, health monitoring, and supply chain management.

Project Overview

ShetNiyojan integrates modern frontend technologies with advanced machine learning capabilities to create a complete farm management solution. The platform offers tools for each phase of the agricultural cycle - from planning and preparation to harvesting and distribution.

Project Structure

The project follows a client-server architecture:

Frontend

• Built with React + TypeScript + Vite
• Uses Tailwind CSS for styling and Shadcn UI components
• Responsive design for mobile, tablet, and desktop devices
• Interactive dashboards and visualizations
• Located in the Frontend directory

Backend

• Built with Flask and Python
• MongoDB database for data persistence
• Machine learning models for crop recommendation, soil analysis, and disease detection
• AI-powered image analysis for plant health assessment
• RESTful API endpoints for frontend communication
• Located in the Backend directory

Key Features

Phase 1: Planning & Preparation

• AI-Based Crop Prediction: Get personalized crop recommendations based on soil composition, climate conditions, and historical yield data
• Lease Marketplace: Access equipment rental and field planning tools for efficient resource allocation

Phase 2: Growing & Monitoring

• Crop Health Monitoring:
  • Upload plant images for real-time disease detection
  • AI-powered diagnosis of plant health issues
  • Detailed treatment recommendations with dosage information
  • Voice input support for hands-free operation
• Smart Irrigation: Optimize water usage with intelligent scheduling based on weather forecasts and soil moisture levels

Phase 3: Harvest & Distribution

• Supply Chain Optimization:
  • Transport route optimization for maximum profit
  • Market price analysis across different locations
  • Interactive maps for visualizing distribution options
• Yield Management:
  • Comprehensive tracking of yields and performance metrics
  • Historical data analysis for continuous improvement

Additional Features

• User Authentication: Secure mobile number-based login and registration system
• Multilingual Support: Language selection for broader accessibility
• Responsive Design: Optimized for all devices, from smartphones to desktops
• Real-time Activity Tracking: Keep track of all farm activities in one place

Getting Started

Prerequisites

• Node.js 16+
• Python 3.8+
• MongoDB
• API keys for external services (if applicable)

Frontend Setup

1. Navigate to the Frontend directory:

   cd ShetNiyojan/Frontend

2. Install dependencies:

   npm install

3. Start the development server:

   npm run dev

4. The application will be available at http://localhost:5173

Backend Setup

1. Navigate to the Backend directory:

   cd ShetNiyojan/Backend

2. Create and activate a virtual environment:

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

3. Install dependencies:

   pip install -r requirements.txt

4. Configure environment variables:

   • Create a .env file based on the existing one
   • Add any required API keys for external services

5. Start the backend server:

   python app.py

6. The API will be available at http://localhost:5000

API Endpoints

Authentication

• POST /api/register: Register a new user
• POST /api/login: Authenticate user and get token
• GET /api/profile: Get current user profile

Yields Management

• GET /api/yields: Get all yields for current user
• POST /api/yields: Create a new yield
• GET /api/yields/:id: Get a specific yield
• PUT /api/yields/:id: Update a yield
• DELETE /api/yields/:id: Delete a yield

Crop Health

• POST /api/crop-health: Upload plant image for disease detection

Crop Recommendation

• POST /api/crop-prediction: Get crop recommendations based on soil and climate data

Supply Chain

• POST /api/supply-chain/optimize: Get transport route optimization

Technologies Used

Frontend

• React 18 with TypeScript
• Vite for build tooling
• Tailwind CSS for styling
• Shadcn UI component library
• Axios for API communication
• React Router for navigation
• Responsive design for all devices

Backend

• Flask for API development
• MongoDB for database storage
• Python for backend logic
• XGBoost and other ML models for predictions and recommendations
• AI-powered image analysis for disease detection
• RESTful API architecture

Future Enhancements

• Integration with IoT devices for real-time sensor data
• Weather forecast integration
• Mobile application development
• Marketplace for agricultural products
• Community features for knowledge sharing

Contributors

• Gopal Dose
• Yuvraj Sanghai
• Shreeshail Chavan
• Viraj Mane
• Ninad Suryawanshi

License

This project is licensed under the MIT License.