🌱 AgriTech — Smart Farming Solutions

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📚 Table of Contents

• Overview
• Quick Start
• Application Preview
• System Architecture
• Core Features
• Tech Stack
• Project Structure
• Backend vs Frontend
• Security & Reliability
• Environment Variables
• Roadmap
• Contribution Flow
• Team
• Contributing & Support
• Contributors
• Production Deployment
• API Keys Guide
• Reporting Issues
• License
• FAQ

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⚠️ Note: For production deployment, use a WSGI server like gunicorn instead of Flask's built-in server.

⚠️ Never commit your .env file or API keys to the repository. Always keep secrets private!

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AgriTech is an AI-powered smart agriculture platform designed to assist farmers with crop recommendations, yield prediction, plant disease detection, and community-driven collaboration—enabling sustainable and data-driven farming practices.

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📌 Overview

• AI-driven decision support for modern agriculture
• Early-stage plant disease detection
• Crop yield forecasting using machine learning models
• Collaborative ecosystem for farmers and stakeholders

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🚀 Quick Start

1️⃣ Clone the Repository

git clone https://github.com/omroy07/AgriTech.git
cd AgriTech

2️⃣ Run Backend (Primary – Flask)

pip install -r requirements.txt
python app.py

Backend URL:

http://localhost:5000

🔍 Backend Health Check

GET http://localhost:5000/health

Expected response:

{ "status": "ok" }

3️⃣ Run Frontend

cd src/frontend
python -m http.server 8000

Frontend URL:

http://localhost:8000

Note: Backend and frontend must be running simultaneously for proper functionality.

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🖥️ Application Preview

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🏗️ System Architecture

1. User Input: Soil data and plant images
2. Backend Processing: Flask APIs and model routing
3. ML Inference:
   • CNNs for disease detection
   • Random Forest / XGBoost for crop recommendation
   • Regression models for yield prediction
4. Output: Predictions with insights and actions

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🌟 Core Features

• 🌾 Crop Recommendation
• 📉 Yield Prediction
• 🔬 Disease Detection
• 🤝 Farmer Community
• 🛒 Shopkeeper Listings

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🛠️ Tech Stack

🎨 Frontend

• HTML5
• CSS3
• JavaScript (ES6)

⚙️ Backend

• Python (Flask)
• Node.js (Optional)

🤖 Machine Learning

• TensorFlow
• Scikit-learn
• OpenCV

🗄️ Database & DevOps

• MySQL
• MongoDB
• Firebase
• Docker
• GitHub Actions

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📂 Project Structure

AGRITECH/
├── app.py                      # 🐍 Flask Backend (Main entry point)
├── server.js                   # 🟢 Node.js Server (Optional)
├── requirements.txt            # Python dependencies
├── firebase.js                 # Firebase config fetching
├── 📁 src/
│   └── 📁 frontend/            # 🌐 Frontend UI (HTML, CSS, JS)
│       ├── 📁 pages/           # Individual page files
│       ├── 📁 css/             # Stylesheets
│       └── 📁 js/              # Client-side scripts
├── 📁 Crop Recommendation/   # 🌾 Crop recommendation module
├── 📁 Disease Prediction/     # 🔬 Disease detection module
├── 📁 Crop Yield Prediction/   # 📊 Yield forecasting module
├── 📁 Community/               # 💬 community/forum backend
├── 📁 images/                  # 📸 Screenshots and assets
├── 📄 README.md                # This file
└── 📄 CONTRIBUTING.md          # Contribution guidelines

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Backend vs Frontend

• Backend (app.py at root): Flask server handling APIs, Firebase config
• Frontend (src/frontend/): Static HTML/CSS/JS served via Python HTTP server
• Optional Node Server (server.js): Alternative chat backend (not required)

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🔐 Security & Reliability

• Image sanitization using OpenCV
• Secrets stored in .env files
• ML models evaluated using standard performance metrics (accuracy varies by model and dataset)

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⚙️ Environment Variables

GEMINI_API_KEY=your_api_key
FIREBASE_API_KEY=your_key
FIREBASE_AUTH_DOMAIN=your_project.firebaseapp.com
FIREBASE_PROJECT_ID=your_project_id
FIREBASE_STORAGE_BUCKET=your_bucket.appspot.com
FIREBASE_MESSAGING_SENDER_ID=your_sender_id
FIREBASE_APP_ID=your_app_id
FIREBASE_MEASUREMENT_ID=your_measurement_id

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🛣️ Roadmap

• Cloud Deployment
• Mobile Application
• Real-Time Weather API
• AI Chatbot
• Multilingual Support

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🤝 Contribution Flow

Fork → Clone → Branch → Commit → Push → Pull Request → Review → Merge

Read CONTRIBUTING.md for SWoC 2026 guidelines.

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👥 Team

Name	Role
Om Roy	Project Lead · Web · ML
Shubhangi Roy	ML · Backend

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🤝 Contributing & Support

We love contributions! Please read our CONTRIBUTING.md to get started with SWoC 2026 tasks. Whether it's fixing bugs, adding features, or improving documentation, your help is always welcome!

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✨ Contributors

Thanks to all the wonderful people contributing to this project! 💖

View full contribution graph

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For production use, run the backend with a WSGI server like gunicorn:

pip install gunicorn
gunicorn app:app

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🔑 API Keys Guide

• Gemini API Key: Google AI Studio se generate karein.
• Firebase Keys: Firebase Console se apne project ke liye keys lein.
• API keys ko .env file me store karein (kabhi bhi repo me commit na karein).

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🐞 Reporting Issues

Found a bug or want a new feature? Open an issue.

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📄 License

This project is licensed under the MIT License.

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❓ FAQ

Q: The project is not running. What should I do? A: Make sure all Python dependencies are installed, the .env file is properly configured, and the correct API keys are being used.

Q: How do I get a Gemini/Firebase API key? A: Refer to the “API Keys Guide” section above for step-by-step instructions.

Q: I am seeing the warning “This is a development server…” on the Flask server. What does it mean? A: This server is intended only for development. For production, use a WSGI server such as Gunicorn.

Q: I accidentally pushed my .env file. What should I do? A: Immediately rotate/regenerate your API keys, remove the .env file from Git tracking, add it to .gitignore, and clean the repository history if required.

Q: The /health endpoint is not responding. What could be wrong? A: Ensure the backend server is running correctly and that the /health route is properly implemented.

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Made with ❤️ by the AgriTech Community — SWoC 2026