PotatoCare: Deep learning based potato disease dataset

The dataset consists of 10,117 images categorized into 10 classes, representing different potato diseases and healthy samples. The classes include Black Scurf (49 images), Blackleg (47), Blackspot Bruising (770), Brown Rot (105), Common Scab (60), Dry Rot (1,355), Healthy Potatoes (815), Miscellaneous (73), Pink Rot (57), and Soft Rot (560). The dataset was compiled from various sources and merged to create a diverse and representative collection of images. However, the distribution of images across classes is imbalanced, with some diseases like Dry Rot and Blackspot Bruising having significantly more samples than others like Blackleg and Pink Rot. This dataset is useful for training deep learning models for automated disease detection in potatoes, enabling early identification and reducing the risk of crop damage. The diverse nature of the dataset enhances model generalizability, making it suitable for real-world agricultural applications.