Apple Leaf Diseases is a high-quality apple leaf image dataset specifically designed for target detection tasks in precision agriculture. It can be directly used for training computer vision models such as YOLOv8 and YOLOv11, agricultural disease identification research, and the development of smart agriculture applications. This dataset contains 3,444 images of apple leaves, covering four categories: healthy apple leaves and three common diseases: black rot, cedar rust, and scab. The dataset has been preprocessed to standard YOLO format and divided into training and validation sets. Each image has a corresponding .txt file containing bounding box coordinates and a class ID.

Data composition:

• Apple_BlackRot: 621 images, 621 bounding boxes
• Apple_CedarRust: 364 images, 454 bounding boxes
• Apple_Healthy: 1,736 images, 1,892 bounding boxes
• Apple_Scab (brown spot disease): 723 images, 801 bounding boxes