Overview

The Crop Prediction Web Application is a tool designed to predict the best crop to grow based on various environmental factors. The application is built using a React front-end and a Flask back-end, leveraging a machine learning model for the prediction.

Visit the live website : https://next-crop.onrender.com

Features

• User-friendly Interface: Intuitive and easy-to-use interface built with React.
• Machine Learning Predictions: Utilizes a trained machine learning model to predict the optimal crop.
• RESTful API: Backend API developed with Flask to handle predictions and data processing.
• Cross-Origin Resource Sharing (CORS): Enabled to allow the front-end and back-end to communicate seamlessly.

Technologies Used

• Front-end: React,JavaScript
• Back-end: Flask, Python
• Machine Learning: Pandas, Scikit-learn, Pickle , xgboost
• Other: Flask-CORS for handling cross-origin requests

Getting Started

Prerequisites

• Node.js
• Python 3.x
• pip (Python package installer)

Installation

Backend Setup

1. Clone the repository:

   git clone https://github.com/yourusername/crop-prediction.git
   cd crop-prediction/backend

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Run the Flask server:

   python app.py

Frontend Setup

1. Navigate to the frontend directory:

   cd ../client

2. Install dependencies:

   npm install

3. Start the React development server:

   npm run dev

Usage

1. Ensure the Flask server is running on port 5000.
2. Ensure the React development server is running on port 3000.
3. Open your browser and navigate to http://localhost:3000.
4. Enter the required environmental parameters (Nitrogen, Phosphorus, Potassium, Temperature, Humidity, pH Value, Rainfall).
5. Click on the "Predict" button to get the crop prediction.

API Endpoints

GET /

• Description: Check if the API is running.
• Response: {"message": "API is Running"}

POST /predict

• Description: Predict the optimal crop based on input parameters.
• Request Body: JSON object containing the following fields:
  • Nitrogen (int)
  • Phosphorus (int)
  • Potassium (int)
  • Temperature (float)
  • Humidity (float)
  • pH_Value (float)
  • Rainfall (float)
• Response: JSON object containing the prediction:
  • {"prediction": ["CropName"]}

Example

Request:

{
  "Nitrogen": 90,
  "Phosphorus": 42,
  "Potassium": 43,
  "Temperature": 20.8,
  "Humidity": 82,
  "pH_Value": 6.5,
  "Rainfall": 202
}