Added a ML model to analyze plant leaves using CNN

[pranawk]

Pull Request for PyVerse 💡

Requesting to submit a pull request to the PyVerse repository.

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Issue Title

Plant Leaf disease analyzer using machine learning
with CNN and tensorFlow

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Info about the Related Issue

What's the goal of the project?

To develop an AI-powered tool that accurately detects plant diseases from leaf images, providing a fast, accessible, and scalable solution for early disease diagnosis. This tool aims to support agricultural productivity by enabling farmers and researchers to identify diseases quickly, ultimately reducing crop losses and enhancing sustainable farming practices.

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Name

Pranaw Kumar

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GitHub ID

pranawk

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Email ID

pranawk3@outlook.com

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Identify Yourself

GSSOC-extd and HacktoberFest contributor

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Closes

Enter the issue number that will be closed through this PR.
Closes: #936

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Describe the Add-ons or Changes You've Made

This project uses CNNs with TensorFlow to detect plant diseases from leaf images. With Python libraries for processing and Flask for a web interface, users can upload images for quick, accurate diagnoses. This tool supports efficient, real-time disease detection to aid in crop health management.

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Type of Change

Select the type of change:

• New feature (non-breaking change which adds functionality)

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How Has This Been Tested?

The model has been tested through a combination of training-validation splits, cross-validation, and testing on a separate dataset of unseen images to assess the model's performance. We evaluated the model's accuracy, precision, recall, and F1-score to ensure it effectively distinguishes between healthy and diseased plants. Additionally, we used real-time testing via the Flask interface to verify the tool's responsiveness and accuracy in practical scenarios, ensuring consistent results across a variety of leaf images.

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Checklist

Please confirm the following:

• My code follows the guidelines of this project.
• I have performed a self-review of my own code.
• I have commented my code, particularly wherever it was hard to understand.
• I have made corresponding changes to the documentation.
• My changes generate no new warnings.
• I have added things that prove my fix is effective or that my feature works.
• Any dependent changes have been merged and published in downstream modules.

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