JEEVSARTHI - Livestock Health Management System

A comprehensive livestock health management platform that leverages AI, blockchain, and real-time monitoring to ensure antimicrobial use (AMU) compliance, Maximum Residue Limit (MRL) tracking, and complete traceability in the dairy and livestock industry.

🚀 Project Overview

JEEVSARTHI is a full-stack solution designed to help farmers, veterinarians, regulators, and other stakeholders manage livestock health records, track treatments, monitor compliance, and ensure food safety through advanced technology integration.

✨ Key Features

• AI-Powered Predictions: Smart predictions for AMU and MRL compliance using advanced machine learning algorithms
• Blockchain Traceability: Immutable records ensuring complete transparency and data integrity
• Real-time Alerts: Smart notifications for withdrawal periods and compliance violations
• QR Code Verification: Quick verification of animal health records and treatment history
• Multi-language Support: UI available in Hindi, Marathi, Gujarati, and English
• Video Consultations: Real-time WebRTC video consultations between farmers and veterinarians with peer-to-peer communication
• Face Recognition: Secure authentication using facial recognition and liveness detection
• Comprehensive Dashboards: Role-based dashboards for farmers, vets, regulators, and administrators

📁 Project Structure

jeevsarthi/
├── server/          # Backend API (Node.js/Express)
├── client/          # Web Client (React/Vite)
├── mobile/          # Mobile App (React Native/Expo)
├── model/           # ML Model API (Python/FastAPI) - Withdrawal Prediction
├── milk_model/      # Milk Quality ML Model API (Python/FastAPI)
├── python-face-service/  # Face Recognition Service (Python/FastAPI)
└── README.md        # This file

🛠️ Technology Stack

Backend

• Runtime: Node.js
• Framework: Express.js
• Database: MongoDB
• Authentication: JWT, Firebase Auth, DID Auth
• Real-time: Socket.io
• Video: WebRTC, Agora, VideoSDK

Frontend (Web)

• Framework: React 18
• Build Tool: Vite
• Styling: Tailwind CSS
• State Management: React Context API
• Routing: React Router v6
• Charts: Recharts

Mobile

• Framework: React Native
• Platform: Expo
• Navigation: React Navigation
• Real-time: Socket.io Client

ML Services

• Framework: FastAPI
• ML Libraries: CatBoost, XGBoost, Scikit-learn
• Face Recognition: OpenCV, ONNX

🚦 Getting Started

Prerequisites

• Node.js (v18+)
• MongoDB
• Python 3.11+
• Java 17 (for Android builds)
• Expo CLI (for mobile development)

Installation

1. Clone the repository

git clone <repository-url>
cd jeevsarthi

2. Install Backend Dependencies

cd server
npm install
npm run dev

3. Install Web Client Dependencies

cd client
npm install
npm run dev

4. Install Mobile App Dependencies

cd mobile
npm install
npm start

5. Setup ML Services

# Milk Model Service
cd milk_model
pip install -r requirements.txt

# Withdrawal Prediction Model
cd ../model
pip install -r requirements.txt

# Face Recognition Service
cd ../python-face-service
pip install -r requirements.txt

Environment Setup

Create .env files in each module with appropriate configuration. See individual module README files for detailed environment variable requirements.

📚 Module Documentation

• Server/Backend Documentation
• Web Client Documentation
• Mobile App Documentation
• ML Model APIs Documentation
• Milk Model Documentation
• Face Recognition Service

🎯 User Roles

1. Farmer: Manage animals, treatments, view alerts, consult vets
2. Veterinarian: Prescribe treatments, order lab tests, conduct consultations
3. Lab Technician: Process lab tests and upload results
4. Chemist/Retailer: Manage drug inventory and sales
5. Regulator: Monitor compliance across regions
6. Admin: System administration and analytics

🔐 Security Features

• JWT-based authentication
• Role-based access control (RBAC)
• Blockchain-based data integrity
• Face recognition with liveness detection
• Encrypted data transmission
• Rate limiting and security headers

📱 Deployment

• Backend: Deployed on Render/Railway
• Frontend: Deployed on Vercel
• ML Services: Deployed on Render
• Mobile: Available on Android/iOS via Expo

🤝 Contributing

This project was developed by Team Cattle-Coders for SIH 2025.

📄 License

MIT License - see LICENSE file for details

📞 Support

For issues and queries, please refer to the individual module documentation or contact the development team.

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Note: Ensure all environment variables are properly configured before running any module. Refer to individual README files for specific setup instructions.