Images dataset of hairy peach fruits at different ripeness for machine vision in smart orchards

With the development of smart agriculture, precise fruit detection and ripeness classification have become one of the key technologies in intelligent orchard management. In this paper, a high-quality dataset for ripeness classification of hairy peach to enhance the application effectiveness of object detection models for machine vision in smart orchard. The dataset is based on real orchard scenes, using smartphones to record images of hairy peach fruits at different ripeness, covered to three categories: immature, semimature and mature. To ensure data diversity, various factors such as different shooting distances, lighting conditions, and fruit growth states were considered during the collection process. Through excluding images that are too dark, blurry, defocused, heavily occluded, and highly repetitive, those high-quality and representative samples are manually selected according to fruit clarity, composition integrity, and balanced ripeness, a total of 1245 high-resolution images (original dataset to 8.23 GB, augmented dataset to 15.56 GB) were obtained, and 3493 fruit bounding boxes were manually annotated. The dataset provides annotation files in both Pascal VOC and YOLO formats. This dataset performs well with YOLO v8, RT-DETR, and Mamba-YOLO, and provides fundamental data support for the development of visual intelligent system for robots in smart orchard.