Initial Release of Crop Prediction AI System

Overview

This is the initial release of the Crop Prediction AI System, a comprehensive solution designed to predict optimal crop yields based on environmental and soil parameters. This project includes both front-end and back-end components, allowing users to interact with the prediction system via a user-friendly API.

Features

• Multi-model prediction using Decision Tree, Logistic Regression, Naive Bayes, and Random Forest algorithms.
• FastAPI-based backend for efficient API handling.
• User-friendly front-end interface for easy input and visualization of predictions.

Installation

To get started with the Crop Prediction AI System, follow these instructions:

Backend Setup

1. Clone the repository:

   git clone https://github.com/mosh3eb/Crop_predictor.git
   cd Crop_predictor/BackEnd

2. Install the required dependencies:

   pip install -r requirements.txt

3. Start the FastAPI server:

   uvicorn main:app --reload

FrontEnd Setup

1. Navigate to the front-end directory:

   cd FrontEnd

2. Install the front-end dependencies:

   npm install

Usage

1. Send a POST request to the /predict endpoint with the following JSON body:

   {
       "nitrogen": <value>,
       "phosphorus": <value>,
       "potassium": <value>,
       "temperature": <value>,
       "humidity": <value>,
       "ph": <value>,
       "rainfall": <value>
   }

2. The response will include the predicted crop:

   {
       "predicted_crop": "crop_name"
   }

Contribution

Contributions are welcome! If you have suggestions for improvements or new features, please fork the repository and submit a pull request.

License

This project is licensed under the MIT License. See the LICENSE file for details.