🌱 Smart Crop Advisory System (SIH Hackathon 2025)

A data-driven crop advisory platform designed for small and marginal farmers in India, providing personalized crop recommendations, real-time weather insights, soil health management, and market intelligence.
Built as part of Smart India Hackathon (SIH) 2025.

---

🚀 Features

• Farmer Profiles – Land records, resources, and farm details
• Smart Crop Recommendations – AI/ML-based suggestions with yield prediction
• Weather Advisory – Real-time forecasts, irrigation schedules & alerts
• Soil Health Management – Nutrient analysis & fertilizer recommendations
• Market Intelligence – Price trends, demand forecasting, selling opportunities
• Pest & Disease Detection – Image-based detection & treatment advice
• Agricultural Calendar – Sowing, harvesting & crop care schedules
• Financial Advisory – Subsidy, insurance & government scheme alerts
• Voice & Multilingual Support – Hindi, English & regional languages
• Offline-first Architecture – Works in low-connectivity areas

---

🛠️ Tech Stack

Frontend: React.js (TypeScript), Tailwind CSS, Material-UI
Backend: Node.js (Express.js), MongoDB, PostgreSQL
Machine Learning: Python (Flask/FastAPI) – crop advisory, yield prediction, disease detection
APIs: IMD, OpenWeatherMap, ISRO Bhuvan, eNAM, APMC
Cloud & Deployment: AWS / Azure, Docker, CI/CD

---

📊 System Architecture

Farmer Input → Data Validation → ML Models → Recommendation Engine → Advisory Dashboard → Feedback Loop

---

📂 Project Structure

frontend/        # React + TypeScript app
backend/         # Node.js + Express API
ml\_service/      # Python ML microservices
docs/            # Documentation & design assets

---

🎯 Impact

• Focused on small & marginal farmers (<5 acres)
• Data-backed farming decisions → improved yield & reduced crop loss
• Increased farmer income with real-time market insights
• Promotes sustainable agriculture

---

📦 Installation & Setup

Prerequisites

• Node.js & npm
• Python 3.x
• MongoDB & PostgreSQL
• Docker (optional for containerized deployment)

Steps

# Clone the repository
git clone https://github.com/your-username/smart-crop-advisory.git
cd smart-crop-advisory

# Install backend dependencies
cd backend
npm install

# Install frontend dependencies
cd ../frontend
npm install

# Run ML service
cd ../ml_service
pip install -r requirements.txt
python flask_app.py

---

📜 License

This project is licensed under the MIT License.

---