Plant Disease Prediction model

Description

This repository contains files related to an arable farming project, potentially involving AI, plant analysis, and web page design. The project includes various files such as HTML, CSS, JavaScript, and Python notebooks.

File Structure

Data

Dataset

• Data_set.md: A markdown file containing information about the dataset used in the project. It includes details about the data source

Presentation

• poster and ppt: Contains poster and PowerPoint presentation files.
• patent: Contains files related to patents.

Images

• extra image: Contains additional images for the project.

Notebook

• AI Modal.ipynb: Python notebook for an AI model.
• boka.ipynb: Machine learning code for plant disease prediction.
• Plant.ipynb: Python notebook for plant analysis.

Webpage

• finance.html: HTML file for finance-related content.
• financepop.html: HTML file for a finance popup.
• home.html: HTML file for the home page.
• learnpopup.html: HTML file for a Basic information popup.
• aimodal.html: HTML file for the AI modal.
• login.html: HTML file for the login page.
• weather.html: HTML file for weather information.

Media_for

• MainMedia.html: HTML file for main media content.
• mediafor tomatobilght.html: HTML file for media related to tomato blight.
• mediaforapplesacb.html: HTML file for media related to apple scab.
• mediaforcornsmut.html: HTML file for media related to corn smut.
• mediaforriceblast.html: HTML file for media related to rice blast.
• mediaforwheatrust.html: HTML file for media related to wheat rust.
• mediapotatoblight.html: HTML file for media related to potato blight.

Script

• weather.js: JavaScript file for weather information.
• aimodal.js: JavaScript file for the AI modal.
• aa.py: Python script.

Style

• aimodal.css: CSS file for the AI modal.

• login.css: CSS file for the login page.

• weather.css: CSS file for weather information.

• README.md: This file, providing an overview of the project.

Usage

To use this repository:

1. Clone the repository to your local machine.
2. Open the HTML files in a web browser to view the web pages.
3. Run the Python notebooks (.ipynb files) using Jupyter Notebook or a similar environment.

License

This project is open source and available under the MIT License.

Contact

For questions or feedback, please contact us.