An AI-powered mobile application designed to provide Indian farmers with actionable, step-by-step guidance for farming decisions. The platform integrates weather data, soil health, market prices, government schemes, and agricultural best practices to deliver personalized recommendations through voice and regional languages.

• Offline-First Architecture: Core functionality works without internet connectivity
• AI-Powered Recommendations: Crop, fertilizer, and seed recommendations using AWS Bedrock
• Weather Intelligence: 7-day forecasts with farming advice and severe weather alerts
• Market Intelligence: Real-time mandi prices and selling guidance
• Government Schemes Navigator: Discover and apply for eligible schemes
• Soil Health Insights: Interpret soil test results and get improvement recommendations
• Training & Learning: Short practical lessons in regional languages
• Voice Interface: Complete voice navigation for low-literacy users
• Multilingual Support: 10+ Indian regional languages
• Smart Alerts: Timely reminders for farming activities

• Mobile: React Native 0.84.1 with TypeScript
• Backend: AWS Lambda (Node.js/TypeScript)
• Database: DynamoDB (cloud) + SQLite (local)
• AI/ML: AWS Bedrock
• Storage: Amazon S3
• Testing: Jest + fast-check (property-based testing)
• Code Quality: ESLint + Prettier

• Node.js >= 22.11.0
• npm or yarn
• React Native development environment
• Android Studio (for Android development)
• Xcode (for iOS development, macOS only)

1. Clone the repository:

git clone <repository-url>
cd MadhavAI

2. Set up environment variables:

# Copy example file
cp .env.example .env

# Edit .env and add your values (see docs/secrets/SECRETS_SETUP_GUIDE.md)

3. Install dependencies:

npm install

4. Generate Android keystore (for release builds):

cd android
./generate-keystore.sh
cd ..

5. Install iOS dependencies (macOS only):

cd ios && pod install && cd ..

For detailed setup instructions, see Quick Start Guide

npm start

npm run android

npm run ios

npm test

npm run test:watch

npm run test:coverage

Property-based tests use fast-check library and are included in the test suite. They validate correctness properties across randomized inputs.

npm run lint

npm run lint:fix

npm run format

npm run format:check

npm run type-check

MadhavAI/
├── src/
│   ├── components/      # Reusable UI components
│   ├── screens/         # Screen-level components
│   ├── services/        # Business logic and API services
│   ├── hooks/           # Custom React hooks
│   ├── store/           # State management
│   ├── config/          # Configuration files
│   ├── types/           # TypeScript type definitions
│   └── utils/           # Utility functions
├── android/             # Android native code
├── ios/                 # iOS native code
├── __tests__/           # Test files
├── docs/                # Project documentation
└── .kiro/specs/         # Specification documents

Comprehensive documentation is available in the docs/ folder:

• Quick Start Guide - Get up and running in 10 minutes
• What to Do Next - Post-MVP deployment guide
• Deployment Checklist - Complete deployment guide

• Secrets Setup Guide - Quick secrets setup
• Environment Setup - Complete environment variables guide
• Secret Variables Reference - All variables explained

• Build Optimization - APK optimization guide
• Rollout Strategy - Staged deployment strategy
• Direct Distribution - Distribute without Play Store

• Checkpoint 5 Validation
• Dashboard Implementation
• Document Picker Setup
• Multilanguage Implementation
• Soil Health Upload Guide
• Voice Interface Implementation
• Project Structure
• Test Results - 718 tests, 100% passing ✅
• Voice Interface Guide
• Translation Service Guide
• Update Mechanisms - OTA updates & A/B testing

For complete documentation index, see docs/README.md

This project follows a spec-driven development approach. Implementation tasks are defined in .kiro/specs/farmer-decision-support-platform/tasks.md.

✅ Task 1: Project setup and infrastructure foundation - COMPLETE
✅ Task 2: Authentication module implementation
✅ Task 3: User profile module implementation
✅ Task 4: Offline sync module implementation
✅ Task 5: Checkpoint - Core infrastructure validation
✅ Task 6: Weather intelligence module implementation
✅ Task 7: Market intelligence module implementation
✅ Task 8: oil health module implementation
✅ Task 9: Recommendation engine implementation
✅ Task 10: Checkpoint - Core recommendation engine validation
✅ Task 11: Government schemes navigator implementation
✅ Task 12: Training and learning module implementation
✅ Task 13: Alert and reminder system implementation
✅ Task 14: Voice interface module implementation
✅ Task 15: Multilanguage support implementation
✅ Task 16: Dashboard module implementation
✅ Task 17: Checkpoint - Core features integration validation

• Hindi (हिन्दी)
• Tamil (தமிழ்)
• Telugu (తెలుగు)
• Kannada (ಕನ್ನಡ)
• Marathi (मराठी)
• Bengali (বাংলা)
• Gujarati (ગુજરાતી)
• Punjabi (ਪੰਜਾਬੀ)
• Malayalam (മലയാളം)
• Odia (ଓଡ଼ିଆ)
• English

1. Offline-First: All core features work without internet
2. Modular: Clean separation of concerns
3. Type-Safe: Strict TypeScript configuration
4. Testable: Property-based testing for correctness
5. Scalable: Designed for 10M+ users
6. Accessible: Voice interface and regional language support

• AES-256 encryption for local data
• TLS 1.3 for network communications
• OTP-based authentication
• Secure session management
• Data privacy compliance

• Dashboard load: < 2 seconds (offline)
• API response: < 3 seconds
• Recommendations: < 5 seconds
• App size: < 50 MB
• Battery usage: < 5% per hour active use
• Minimum device: Android 8.0, 2 GB RAM

1. Follow the coding standards (ESLint + Prettier)
2. Write tests for new features
3. Maintain 80%+ code coverage
4. Update documentation
5. Follow the spec-driven development workflow

This project is licensed under the MIT License. See the LICENSE file for more information.

Payal Kumari
Team Leader | Full Stack Developer | AI Integration
LinkedIn: https://www.linkedin.com/in/payalkumari10/

Akashdeep
Full Stack Developer | React Native Development
LinkedIn: https://www.linkedin.com/in/akashdeep023/

Prem Rathod
Full Stack Developer | Backend & Cloud Support
LinkedIn: https://www.linkedin.com/in/prem-arun-rathod/

If you have questions, suggestions, or would like to collaborate, feel free to reach out.

Payal Kumari
Team Leader – Team Madhav
📧 Email: kumaripayal7488.com
🔗 LinkedIn: https://www.linkedin.com/in/payalkumari10/

For technical queries, you can also connect with any of the Team Madhav members listed above.

Built with ❤️ to empower Indian farmers with AI-powered decision support.