Crop Recommendation AI

A machine learning-powered application that provides intelligent crop recommendations based on environmental and soil conditions.

Table of Contents

• Features
• Project Structure
• Installation
• Usage
• Backend Setup
• Frontend Setup
• Model Information
• Technologies Used

Features

• 🌾 AI-powered crop recommendations
• 📊 Data-driven decision making
• 🎨 Interactive web interface
• 📱 Responsive design
• 🚀 REST API backend

Project Structure

crop/
├── app.py                          # Flask/Django backend application
├── generate_backend.py             # Backend generation script
├── run_eval.py                     # Model evaluation script
├── Crop_Recommendation_AI.ipynb    # Jupyter notebook with ML model
├── README.md                       # This file
├── frontend/                       # React/Vue frontend
│   ├── src/
│   │   ├── App.jsx
│   │   ├── main.jsx
│   │   ├── App.css
│   │   ├── index.css
│   │   └── assets/
│   ├── public/
│   ├── vite.config.js
│   ├── tailwind.config.js
│   ├── postcss.config.js
│   ├── eslint.config.js
│   ├── package.json
│   └── README.md
└── model/                          # Machine learning models

Installation

Prerequisites

• Python 3.8+
• Node.js 14+
• npm or yarn

Backend Setup

# Navigate to project root
cd crop

# Install Python dependencies
pip install -r requirements.txt

# Run the application
python app.py

The backend will be available at http://localhost:5000 (or configured port).

Frontend Setup

# Navigate to frontend directory
cd frontend

# Install dependencies
npm install

# Start development server
npm run dev

The frontend will be available at http://localhost:5173 (Vite default).

Usage

1. Start the Backend

   python app.py

2. Start the Frontend

   cd frontend
   npm run dev

3. Access the Application

   • Open your browser and navigate to http://localhost:5173
   • Input soil and environmental parameters
   • Receive crop recommendations from the AI model

Model Information

The crop recommendation model is built using machine learning techniques and is defined in the Jupyter notebook: Crop_Recommendation_AI.ipynb

To evaluate the model:

python run_eval.py

Technologies Used

Backend

• Python
• Flask or Django (see app.py)

Frontend

• React or Vue.js
• Vite (build tool)
• Tailwind CSS (styling)
• ESLint (code quality)

Machine Learning

• Jupyter Notebook for model development
• Python ML libraries (pandas, scikit-learn, etc.)

API Endpoints

Refer to the backend documentation or app.py for available API endpoints and request/response formats.

Contributing

Feel free to submit issues and enhancement requests!