https://github.com/uditmahato/api_maize_plant_disease

An API that utilizes a Deep Learning model built with Keras (Tensorflow) to detect if a plant is healthy or suffering from Rust and Powder formation.
https://github.com/uditmahato/api_maize_plant_disease

api cnn deeplearning flaskapi machinelearning

Last synced: about 13 hours ago
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An API that utilizes a Deep Learning model built with Keras (Tensorflow) to detect if a plant is healthy or suffering from Rust and Powder formation.

• Host: GitHub
• URL: https://github.com/uditmahato/api_maize_plant_disease
• Owner: uditmahato
• License: apache-2.0
• Created: 2023-10-28T15:40:13.000Z (about 1 year ago)
• Default Branch: master
• Last Pushed: 2024-03-03T18:55:58.000Z (9 months ago)
• Last Synced: 2024-03-03T19:47:26.097Z (9 months ago)
• Topics: api, cnn, deeplearning, flaskapi, machinelearning
• Language: Python
• Homepage:
• Size: 89.1 MB
• Stars: 15
• Watchers: 1
• Forks: 2
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Plant Disease Detection API

An API that utilizes a Deep Learning model built with Keras (Tensorflow) to detect if a plant is healthy or suffering from Rust and Powder formation.

## Table of Contents

- [Introduction](#introduction)

- [API Endpoints](#api-endpoints)

  - [Running the API](#running-the-api)

  - [Prediction Endpoint](#prediction-endpoint)

- [Contributing](#contributing)

- [License](#license)

## Introduction

This API provides the capability to detect plant diseases using a pre-trained deep learning model. It allows you to upload an image of a plant, and it will predict whether the plant is healthy or suffering from Rust and Powder formation.

## API Endpoints

### Running the API

To run the API, follow these steps:

1. Fork the repository.

2. Create a new branch for your feature or bug fix.

3. Implement your changes.

4. Test your changes.

5. Create a pull request.

### Prediction Endpoint

#### `POST /predict`

- **Description**: The root function returns the prediction and confidence level of an image using a pre-trained model.

- **Parameters**:

  - `file` (UploadFile): The image to be predicted.

- **Returns**: A dictionary containing the prediction and confidence level.

## Contributing

Contributions to this project are welcome. If you'd like to contribute, please follow these steps:

1. **Fork the repository**.

2. **Create a new branch** for your feature or bug fix.

3. **Implement your changes**.

4. **Test your changes**.

5. **Create a pull request**.

## License

This project is licensed under the [Apache License 2.0](LICENSE).