Smart Agriculture Crop Recommendation System

A Flask-based API and Machine Learning model to recommend the best crop for a given area or land based on environmental and soil parameters.

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Table of Contents

• About the Project
• Features
• Tech Stack
• Installation
• Usage
• Contact

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About the Project

The Smart Agriculture Crop Recommendation System predicts the best crop for an area based on the following input parameters:

• Nitrogen (float): Level of nitrogen in the soil.
• Phosphorus (float): Level of phosphorus in the soil.
• Potassium (float): Level of potassium in the soil.
• Temperature (float): Temperature of the area.
• Humidity (float): Humidity percentage.
• pH (float): Soil pH value.
• Rainfall (float): Rainfall in mm.

This project:

1. Trains a Machine Learning model using environmental and soil data.
2. Provides a Flask API for predictions.
3. Deploys the Flask API on Render for live usage.

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Features

• Model Training: Trained a Machine Learning algorithm to predict the most suitable crop for specific soil and environmental conditions.
• Flask API: Developed an API to accept input parameters and return crop recommendations.
• Deployment: The API is deployed on Render, making it accessible for practical usage.

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Tech Stack

• Backend: Flask, Python
• Machine Learning: Scikit-learn
• Deployment: Render

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Installation

1. Clone the repository:

   git clone https://github.com/Harsh772005/smart-agriculture-crop-recommendation.git

2. Navigate to the project directory:

   cd smart-agriculture-crop-recommendation

3. Install required dependencies:

   pip install -r requirements.txt

Usage

1. Start the Flask API:

   python app.py

Contact

• If you have any questions or suggestions, feel free to contact me:

Email: [email protected] GitHub: Harsh772005