Leaf Buster AI - README

Leaf Buster AI is a machine learning project developed during the HackMerced VIII hackathon.

The goal of the project is to help farmers identify and classify diseases in their crops using computer vision and artificial intelligence.

Through the utilization of image recognition techniques with machine learning, we were able to identify leaf blotch. To accomplish this, we employed a dataset obtained from Kaggle, consisting of images depicting both healthy leaves and leaves affected by leaf blotch. Our approach involved using machine learning algorithms to train an AI model to recognize the visual patterns associated with leaf blotch. The model was taught to differentiate between healthy leaves and leaves affected by leaf blotch based on features such as the color, shape of the spots, size, distribution of the lesions, and other characteristics. Upon completion of the training process, the trained model can be used to automatically detect leaf blotch. This can be achieved via a computer vision system that examines images of plant leaves captured by a camera and delivers a diagnosis of leaf blotch based on its analysis.

Get started

To use the application, simply clone the repository and run the following commands in your terminal:

Instal dependencies

npm i

This will install all the necessary dependencies and start the development server

Run App

npm start

You can then access the application by navigating to http://localhost:3000 in your web browser.

Once you have the application running, simply upload an image of a plant leaf and the machine learning model will attempt to classify it. The results will be displayed on the screen along with a confidence score.

Note that the machine learning model is not perfect and may make mistakes. It is intended to be used as a tool to assist farmers in identifying potential issues with their crops, but should not be relied on as the sole source of information.

We hope you find this project useful and informative. If you have any questions or feedback, please feel free to contact us.

Contributors

• Mulero Alamou
• Princess Thomas
• Joseph Sayda
• Emi Rueth

License

This project is licensed under the MIT License.