This project is a leaf disease detection system that uses deep learning techniques, including transfer learning, to identify and classify 33 different types of leaf diseases. The model has been trained on a large dataset of images and is designed to help agricultural professionals and enthusiasts diagnose plant diseases in a fast and accurate manner.
To use the model for leaf disease detection, follow these steps:
- Make sure you have a Python environment set up with the necessary libraries installed. You can use the provided requirements.txt file to set up the required dependencies.
pip install -r requirements.txt
- Run main.py
streamlit run main.py
The leaf disease detection model is built using deep learning techniques, and it uses transfer learning to leverage the pre-trained knowledge of a base model. The model is trained on a dataset containing images of 33 different types of leaf diseases. For more information about the architecture, dataset, and training process, please refer to the code and documentation provided.
We would like to acknowledge the contributions of the open-source community and the creators of the base model that this project builds upon. Your work and support are greatly appreciated.