AgriTech Web Platform

Overview

This project is an AgriTech Web Platform designed to assist farmers and agricultural stakeholders with crucial information and predictions. The platform provides three main features:

• Crop Prediction: Suggests the most suitable crops to plant based on environmental factors.
• Crop Yield Prediction: Estimates the potential yield of crops to help in planning and resource management.
• Disease Analysis: Identifies crop diseases from images and suggests possible treatments.

Table of Contents

• Features
• Installation
• Usage
• Technologies
• Contributing
• Important Points

Features

1. Crop Prediction

Utilizes environmental data such as soil type, weather conditions, and other parameters. Suggests the most suitable crops for planting.

2. Crop Yield Prediction

Predicts the potential yield of selected crops. Helps in planning and resource management.

3. Disease Analysis

Analyzes images of crops to detect diseases. Provides treatment suggestions and preventive measures.

Installation

1. Clone the repository:

   git clone https://github.com/yourusername/agriculture-web-platform.git
   cd agriculture-web-platform

2. Install dependencies:

   pip install -r requirements.txt

3. Set up environment variables (if any).
4. Run the application:

   python app.py

Usage

1. Navigate to the homepage of the platform.
2. Use the Crop Prediction feature by inputting environmental data.
3. Use the Crop Yield Prediction feature by selecting a crop and inputting necessary parameters.
4. Use the Disease Analysis feature by uploading an image of the crop.

Technologies

• Frontend: HTML, CSS, JavaScript
• Backend: Flask / Django (or any framework you're using)
• Machine Learning: Scikit-learn, TensorFlow, Keras, etc.
• Database: MySQL, PostgreSQL, SQLite (or any database you're using)

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch:

   git checkout -b feature-branch-name

3. Make your changes.
4. Commit your changes:

   git commit -m 'Add some feature'

5. Push to the branch:

   git push origin feature-branch-name

6. Submit a pull request.

Important Points

This project is does not contain the dataset of Disease prediction download it from Kaggle file for details.