Carambola Leaf & Fruit Dataset for Disease Detection and Classification
Description
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.