adityakmrmishra / kisan_prediction

Kisan Prediction

This project is an application for predicting crop types based on input environmental factors. The backend is implemented in Flask (Python), and the frontend is built using React.

Introduction

The Crop Prediction App uses a machine learning model to predict the most suitable crop based on environmental factors such as nitrogen, phosphorus, potassium, temperature, humidity, pH, and rainfall.

Features

• Predict crop type based on environmental factors.
• User-friendly frontend for input and result display.

Getting Started

Prerequisites

• Node.js
• Express.js
• Python
• Flask
• Vite + React + Tailwind CSS

Installation

1. Clone the repository:

git clone https://github.com/your-username/crop-prediction-app.git

2. Install backend dependencies:

cd crop-prediction-backend
npm i 

pip install -r requirements.txt

3. Install frontend dependencies:

cd crop-prediction-frontend
npm install

Project Structure

crop-prediction-backend: Backend implementation using Node, express and Flask .

crop-prediction-frontend: Frontend implementation using Vite + React + Tailwind CSS .

Backend

The backend uses a RandomForestClassifier machine learning model trained on crop data.

Run Backend Locally

cd crop-prediction-backend
npm run dev

cd crop-prediction-backend
python model_api.py

Frontend

The frontend is a React application providing a user interface for entering input values and displaying predictions.

Run Frontend Locally

cd crop-prediction-frontend
npm run dev

API Endpoints

/api/predict : Endpoint for predicting the crop type based on input environmental factors.

Usage

1. Start the backend server.
2. Start the frontend application.
3. Visit http://localhost:5173 in your web browser.
4. Enter the required input values.
5. Click the "Predict" button to get the crop prediction.

Contributing

Contributions are welcome! Please fork the repository and create a pull request.