https://github.com/md-nur/plantaid-client

PlantAId is a web application that uses TensorFlow CNN to analyze leaf photos and detect diseases in 14+ crops. Built with FastAPI (backend) and Next.js (frontend), it aims to help farmers identify crop diseases early and improve yield. Future plans include an offline mobile app for remote areas.
https://github.com/md-nur/plantaid-client

fastapi nextjs tensorflow

Last synced: about 2 months ago
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PlantAId is a web application that uses TensorFlow CNN to analyze leaf photos and detect diseases in 14+ crops. Built with FastAPI (backend) and Next.js (frontend), it aims to help farmers identify crop diseases early and improve yield. Future plans include an offline mobile app for remote areas.

• Host: GitHub
• URL: https://github.com/md-nur/plantaid-client
• Owner: Md-Nur
• Created: 2025-01-30T22:30:10.000Z (over 1 year ago)
• Default Branch: main
• Last Pushed: 2025-02-13T20:35:24.000Z (over 1 year ago)
• Last Synced: 2025-10-19T20:43:47.389Z (8 months ago)
• Topics: fastapi, nextjs, tensorflow
• Language: TypeScript
• Homepage: https://plant-aid-client.vercel.app
• Size: 31.9 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 2
• Metadata Files:
  • Readme: README.md

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README

          
# PlantAid: Crop Disease Detection using TensorFlow CNN

![GitHub](https://img.shields.io/badge/Language-Python-blue)

![GitHub](https://img.shields.io/badge/Framework-TensorFlow-orange)

![GitHub](https://img.shields.io/badge/Backend-FastAPI-green)

![GitHub](https://img.shields.io/badge/Frontend-Next.js-purple)

A web application that analyzes leaf photos of various crops to detect diseases using a **TensorFlow CNN model**. The application supports **14+ crops**, including potato, tomato, pepper, apple, blueberry, cherry, corn, grape, orange, peach, raspberry, soybean, squash, and strawberry.

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## Features

- **Disease Detection:** Upload a leaf photo, and the app will detect if there is any disease and identify it.

- **Multi-Crop Support:** Works for 14+ crops, with the flexibility to add more.

- **User-Friendly Interface:** Built with **Next.js** for a seamless frontend experience.

- **Scalable Backend:** Powered by **FastAPI** for efficient and fast predictions.

- **Future Plans:** Developing a mobile app that can run offline for use in remote areas.

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## Tech Stack

- **Deep Learning:** TensorFlow, Convolutional Neural Networks (CNN)

- **Backend:** FastAPI

- **Frontend:** Next.js

- **Deployment:** Docker, Hugging Face

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## Dataset

The model was trained on a [Plant Village](https://www.kaggle.com/datasets/arjuntejaswi/plant-village) & [New Plant Diseases](https://www.kaggle.com/datasets/vipoooool/new-plant-diseases-dataset) dataset of crop leaf images, categorized by crop type and disease. The dataset includes images for all supported crops.

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## Future Enhancements

- Add support for more crops.

- Develop a mobile app version that works offline.

- Improve model accuracy with more training data.

- Add multilingual support for farmers.

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## Acknowledgments

A special thanks to **Bipin Dada** for his invaluable help with model format conversion and deployment. Without his guidance, this project would not have been possible.

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## License

This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.

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## Contributing

Contributions are welcome! Please open an issue or submit a pull request for any improvements.