Carambola Leaf & Fruit Dataset for Disease Detection and Classification

This dataset is designed for the detection and classification of diseases affecting carambola (starfruit) leaves and fruits using image-based analysis. Captured using a smartphone, it consists of high-quality images covering both healthy and diseased samples. The dataset can be utilized for training deep learning models in plant disease detection, classification, and agricultural monitoring applications.

Original Dataset:
- Number of images: 2,618
- Data format: .jpg

Processed Dataset:
- Number of images: 2,618
- Data format: .jpg

Augmented Dataset:
- Number of images: 15,000
- Data format: .jpg

Augmentation Techniques:
1.Rotation, 2. Flipping, 3. Brightening, 4. Contrast Adjustment, 5. Blurring, 6. Shearing, 7. Zooming, 8. Adding Noise

Applications:
- This dataset can be used for plant disease detection and classification, enabling agricultural monitoring using AI-based models.
- It supports image-based plant health assessment and can be applied in precision farming and crop management.
- It can be used to train deep learning models for improved disease detection and classification accuracy.