This project aims to detect diseases in grapevine plants from leaf images using MATLAB. The MATLAB script analyzes grapevine leaf images to identify symptoms of diseases such as leaf blight, black rot, etc. The detection process involves image preprocessing, feature extraction, and classification using machine learning techniques.
- Deep Network Designer: Utilize MATLAB's Deep Network Designer tool to design, visualize, and train custom CNN architectures for grapevine disease detection.
- Preprocessing: Apply image preprocessing techniques to enhance the quality and diversity of the grapevine leaf image dataset.
- Training and Fine-Tuning: Train the CNN models using transfer learning or fine-tuning approaches with pre-trained networks for improved disease classification performance.
- Testing and Evaluation: Evaluate the trained CNN models using testing datasets to measure their performance for grapevine disease detection.
- MATLAB
- Deep Learning Toolbox
- Image Processing Toolbox
- Clone or download the repository to your local machine.
- Open MATLAB and navigate to the project directory.
- Open the
model.mscript to start the grapevine disease detection process. - Use the Deep Network Designer to design and train CNN models for grapevine disease detection.
- Follow the instructions provided in the script or the user interface to input grapevine leaf images and view the detection results.
You can use your own dataset of grapevine leaf images or explore publicly available datasets for training and testing your CNN models. Additionally, a sample dataset of grapevine leaf images is included in this repository under the dataset directory. This dataset can be used to get started with training and testing your CNN models right away.
This project is licensed under the MIT License - see the LICENSE file for details.