Apple Disease Dataset

This dataset contains images of Manalagi apple varieties from Indonesia. The data represent both healthy and diseased fruit. All data were collected from apple orchards owned by farmers. The dataset consists of four categories: one healthy fruit class and three disease classes: Anthracnose, Black Pox, and Powdery Mildew. Images were captured using a digital camera and a smartphone. To increase the quantity and variety of data, image augmentation was performed. The augmentation techniques used included rotation (45°, 90°, 180°, 225°, and 270°), flipping (vertical and horizontal), noise addition (Gaussian noise, speckle noise, and salt-and-pepper noise), and image transformations such as vertical and horizontal shifts, saturation changes, and brightness adjustments. This process aims to enrich the data variety so that the model can learn more robustly to various field conditions.

Dataset Summary:
- Total original data: 579 images
- Total data after augmentation: 8,396 images
- Image resolution: 1024 × 1024 pixels

Data Distribution:
- Healthy: 102 → 1,530 images
- Anthracnose: 163 → 2,445 images
- Black Pox: 166 → 2,201 images
- Powdery Mildew: 148 → 2,220 images

Dataset Description:
- This dataset focuses on fruit diseases (fruit-based dataset), which is still rare compared to public datasets that generally focus on leaves.
- The data is collected directly from orchards under natural conditions, reflecting real world variations such as lighting, background, and disease manifestations.
- Augmentation techniques help improve the performance of deep learning models by expanding the data variety and reducing the risk of overfitting.
- This dataset can be used as a benchmark for training, validating, and evaluating apple disease classification models.
- This dataset also supports the development of automated disease detection systems, smart farming, and AI based applications in agriculture.