Strawberry Disease Detection Dataset with Hybrid Data Augmentation

This dataset provides annotated images for instance segmentation of strawberry diseases affecting both fruit and leaves. It extends two publicly available datasets:

[1] Afzaal, U., Bhattarai, B., Pandeya, Y.R., Lee, J., 2021. An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN. Sensors 21, 6565. https://doi.org/10.3390/s21196565.

[2] Pérez-Borrero, I., Marín-Santos, D., Gegúndez-Arias, M.E., Cortés-Ancos, E., 2020. A fast and accurate deep learning method for strawberry instance segmentation. Comput. Electron. Agric. 178, 105736. https://doi.org/10.1016/j.compag.2020.105736.

Both datasets are distributed under terms that allow reuse for academic and research purposes. This extended dataset (v1.0) applies systematic augmentation for class balance and increases the diversity of environmental conditions in the data.

The proposed dataset contains 5,610 annotated images across 8 classes, with approximately 700 images per class: Angular Leafspot, Anthracnose Fruit Rot, Blossom Blight, Gray Mold, Healthy Strawberry, Leaf Spot, Powdery Mildew Fruit, Powdery Mildew Leaf.

All images are provided in JPG format with a resolution of 640 × 640 pixels. Annotations are polygon-based instance masks provided in COCO segmentation format (.json).

The proposed dataset is organized into 80% training, 10% validation, and 10% testing, ensuring balanced representativeness of all classes.

Two directory structures are included at the root level:

/Strawberry-segmentation-hybrid-augmentation
/train
image_1.jpg
image_2.jpg
...
_annotations.coco.json
/valid
image_1.jpg
image_2.jpg
...
_annotations.coco.json
/test
image_1.jpg
image_2.jpg
...
_annotations.coco.json

/Strawberry-segmentation-traditional-augmentation
/train
image_1.jpg
image_2.jpg
...
_annotations.coco.json
/valid
image_1.jpg
image_2.jpg
...
_annotations.coco.json
/test
image_1.jpg
image_2.jpg
...
_annotations.coco.json

Traditional image augmentation includes geometric and photometric transformations, including random cropping, center cropping, rotation, horizontal flipping, vertical flipping, random brightness and contrast adjustments, and random hue and saturation value adjustments.

Hybrid image augmentation additionally incorporates synthetic images generated with a diffusion model (DALL·E 3). Synthetic images are included only in the training split.