Download Report IEEE DataPort : An Annotated Image Dataset for Small Apple Fruitlet Detection in Complex Orchard Environments - 2025
JPG, TXT, XML by Bo Wang
Information
Format: JPG, TXT, XML Publisher: IEEE DataPort Publication Date of the Electronic Edition: 11/16/2025
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ISBN: 10.21227/z74d-8t41
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Description
This study presents a small apple pre-thinning dataset designed to provide reliable data support for small apple detection and the development of intelligent thinning systems. The dataset comprises 2,517 RGB images (original size 3024×3024 pixels, uniformly resized to 500×500 pixels for standardization) systematically captured under real-world orchard conditions. The dataset encompasses natural variations in weather conditions (sunny/cloudy), lighting scenarios (direct sunlight/backlight), and fruit sizes (3-25mm diameter range) to ensure broad applicability. Each image was meticulously annotated using LabelImg software, with all small apple targets precisely labeled using both PASCAL VOC (XML) and YOLO (TXT) format bounding boxes, facilitating compatibility with various detection frameworks. Validation experiments demonstrate the dataset’s effectiveness across multiple state-of-the-art detection architectures (Faster R-CNN, Cascade R-CNN, Grid R-CNN, RetinaNet, YOLOv5, YOLOv8, YOLOv11, YOLOv12, RT-DETR, and DEIMv2), with all models achieving consistently high detection accuracy under various challenging conditions. This dataset serves as a valuable resource for developing intelligent thinning systems, with potential applications in promoting automation in the apple industry, enhancing thinning efficiency, and improving fruit quality.
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