🌱 AgriTech — Smart Farming Solutions

AgriTech Banner

📚 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

⚠️ 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!

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.

📌 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

🚀 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.

🖥️ Application Preview

🏗️ System Architecture

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

🌟 Core Features

🌾 Crop Recommendation
📉 Yield Prediction
🔬 Disease Detection
� AI Chatbot - Platform guidance & agriculture support
�🤝 Farmer Community
🛒 Shopkeeper Listings

🤖 AI Chatbot

AgriTech's AI-powered chatbot provides comprehensive support for farmers:

Features

Platform Guidance: Explains how to use all AgriTech features and tools
Agriculture Support: Answers farming questions, crop recommendations, pest control
Decision Making: Provides region-specific, season-based farming advice
Image Analysis: Upload plant photos for disease detection and diagnosis
24/7 Support: Always available for instant farming assistance

Technical Implementation

Dual Mode: AI-powered (Google Gemini) + Rule-based fallback
Smart Matching: Fuzzy search with keyword analysis for accurate responses
Offline Capability: Works without internet using JSON-based responses
Image Processing: Analyzes plant photos for disease identification

Usage

# Start the chatbot server
npm install
node server.js

# Access at: http://localhost:3000/chat

API Endpoints

POST /api/chat - Send messages and images for AI analysis

🛠️ 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

📂 Project Structure

AGRITECH/
├── app.py                      # 🐍 Flask Backend (Main entry point)
├── server.js                   # 🟢 Node.js Chatbot Server
├── package.json                # Node.js dependencies
├── requirements.txt            # Python dependencies
├── firebase.js                 # Firebase config fetching
├── 📁 chatbot/
│   ├── chat.html               # 🤖 Chatbot interface
│   ├── chat.js                 # Chatbot client logic
│   ├── chat.css                # Chatbot styling
│   ├── json-chatbot.js         # Rule-based chatbot engine
│   └── chatbot-responses.json  # Predefined responses
├── 📁 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

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)

🔐 Security & Reliability

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

⚙️ 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

🔐 Registration Validation Rules

To maintain clean user data and enforce business policies, AgriTech now applies strict validation whenever a new account is created (frontend and backend):

Full Name / Username: only alphabetic characters and spaces are allowed. The same rule is enforced in the database model, API routes, and client-side form.
Email: registrations are limited to @gmail.com addresses. Non‑Gmail domains are rejected both on the client and by API endpoints.

Error messages are shown immediately on the form and the backend returns a descriptive 400 response when validation fails. Automated tests cover both model‑level and endpoint behavior.

🛣️ Roadmap

Cloud Deployment
Mobile Application
Real-Time Weather API
~~AI Chatbot~~ ✅ COMPLETED
Multilingual Support

🤝 Contribution Flow

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

Read CONTRIBUTING.md for SWoC 2026 guidelines.

👥 Team

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

🤝 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!

✨ Contributors

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

Contributors

View full contribution graph

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

pip install gunicorn
gunicorn app:app

🔑 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).

🐞 Reporting Issues

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

📄 License

This project is licensed under the MIT License.

🛠️ Common Issues & Fixes

❌ ModuleNotFoundError 👉 Run pip install -r requirements.txt
❌ Firebase config error 👉 Ensure .env values match Firebase Console
❌ CORS issue 👉 Make sure backend runs before frontend
❌ Port already in use 👉 Change port in app.py or stop previous process

❓ 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,

AgriTech

Install / Use

README