Classifies leaves in 2 classes (healthy and rotten).
Followed Steps:
- Image is transformed to HSV (improve the RGB model).
- Image segmentation to identify rotten parts of the leaf.
- Feature extraction is performed for every sample that is available for training.
- Training of the SVM classifier performs, generating two classes (healthy leaves 1 class, rotten leaves 2 class).
- Datasets consists of apple laf images.
- Training Images: 2000 images (1000 healthy leaf images) and (1000 rotten leaf images).
- Testing Images: 40 images for testing (20 healthy leaf images) and (20 rotten leaf images).
This project is licensed under the MIT License - see the LICENSE file for details.