venkat-2811 commented 3 weeks ago

Related Issue

This PR addresses #489 & Closes #489

Description

Plant-disease-detection

About the Project

An application that for farmers to detect the type of plant or crops, detect any kind of diseases in them. The app sends the image of the plant to the server where it is analysed using CNN classifier model. Once detected, the disease and its solutions are displayed to the user

Model

Trained to identify 5 classes for Disease Detection and 24 classes for Disease Classification. Dataset can be downloaded form kaggle

       - Disease Classification Classes

                   - Apple___Apple_scab
                   - Apple___Black_rot
           - Apple___Cedar_apple_rust
           - Apple___healthy
           - Blueberry___healthy
           - Cherry___healthy
           - Cherry___Powdery_mildew
           - Grape___Black_rot
           - Grape___Esca_Black_Measles
           - Grape___healthy
           - Grape___Leaf_blight_Isariopsis_Leaf_Spot
           - Orange___Haunglongbing
           - Peach___Bacterial_spot
           - Peach___healthy
           - Pepper,_bell___Bacterial_spot
           - Pepper,_bell___healthy
           - Potato___Early_blight
           - Potato___healthy
           - Raspberry___healthy
           - Soybean___healthy
           - Squash___Powdery_mildew
           - Strawberry___healthy
           - Strawberry___Leaf_scorch

        - Disease Detection Classes

           - Cherry___healthy
           - Cherry___Powdery_mildew
           - Grape___Black_rot
           - Grape___Esca_Black_Measles
           - Grape___healthy
           - Grape___Leaf_blight_Isariopsis_Leaf_Spot

Type of PR

[ ] Bug fix
[ ] Feature enhancement
[ ] Documentation update
[X] Other (specify): New Model for Plant Disease Detection

Screenshots / videos (if applicable)

Demo

About
Disease Predection
- Image Upload
- Image Detected
Disease Classification
- Image Upload
- Image Classified
Treatement Page

Checklist:

[X] I have performed a self-review of my code
[X] I have read and followed the Contribution Guidelines.
[X] I have tested the changes thoroughly before submitting this pull request.
[X] I have provided relevant issue numbers, screenshots, and videos after making the changes.
[X] I have commented my code, particularly in hard-to-understand areas.

Additional context:

Customization

You can customize the detection thresholds and other parameters within the script to suit your specific requirements. Detailed comments within the code will guide you through making these adjustments.

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Name	Status	Preview	Comments	Updated (UTC)
ml-nexus	❌ Failed (Inspect)			Nov 3, 2024 5:57am

github-actions[bot] commented 3 weeks ago

👋 Thank you for opening this pull request! We appreciate your contribution to improving this project. Your PR is under review, and we'll get back to you shortly. Don't forget to mention the issue you solved!.

To help move the process along, please tag @UppuluriKalyani, @Neilblaze, and @SaiNivedh26 for a faster review!