How iamsonubisht/plant-disease-detection works
The repository is a plant-leaf disease classifier with a Flask web app and a documented PyTorch model workflow. The main user flow is image upload, 39-class inference, and display of disease guidance and a related supplement; the app also provides a supplement market and informational pages. The sampled Flask source supports the upload, prediction, and CSV lookup wiring. Training details are documented, but notebook internals were not sampled.
Main entry points: app.py Flask routes · CNN.py CNN classifier · Plant Disease Detection Code.ipynb Model training materials · templates Web pages
Components 11
Web application
- Flask routes
Flask Deployed App/app.py - Image upload
Flask Deployed App/app.py - Web pages
Flask Deployed App/templates
Disease inference
- Image prediction
Flask Deployed App/app.py - CNN classifier
Flask Deployed App/CNN.py
Plant knowledge
- Disease catalogue
Flask Deployed App/disease_info.csv - Supplement catalogue
Flask Deployed App/supplement_info.csv - Diagnosis guidance
Flask Deployed App/templates/submit.html - Supplement market
Flask Deployed App/templates/market.html
Model development
- Model training materials
Model/Plant Disease Detection Code.ipynb
Other components
- Farmer / visitor
Connections 10
- Farmer / visitor → Flask routes: uses
- Flask routes → Web pages: renders
- Flask routes → Image upload: handles upload
- Image upload → Image prediction: saves image
- Image prediction → CNN classifier: classifies image
- Flask routes → Disease catalogue: loads / looks up
- Flask routes → Supplement catalogue: loads / looks up
- Flask routes → Diagnosis guidance: renders result
- Flask routes → Supplement market: renders market
- Model training materials → CNN classifier: documents model