The dataset contains color (RGB) images collected under different weather conditions and at different time periods, with a resolution of 1280×1024. The images cover various lychee varieties, such as Nuomi, Feizi Xiao, Heiye, and Huaizhi. The dataset includes three distinct ripening stages: immature, semi-ripe, and mature, comprising a total of 11,414 images. These include 878 original RGB images, 8,780 enhanced RGB images, and 1,756 depth images. The images are organized into five folders based on collection date, such as Image_YoloLabel_250605_Outdoor_sunny. Each folder includes descriptions of the weather and indoor/outdoor scenes. Each image and its labeled file is numbered, and includes the data augmentation method, number of categories, and similarity score (e.g., ID-0001_cc_unripe-0_semi-ripe-0_ripe-2_sim-0.0016.jpg). These images are labeled with 9,658 pairs of tags for lychee detection and ripeness classification during robotic harvesting. To improve the consistency of annotation, three researchers independently annotated the data, and then a fourth reviewer summarized and verified their results.

The dataset contains color (RGB) images collected under different weather conditions and at different time periods, with a resolution of 1280×1024. The images cover various lychee varieties, such as Nuomi, Feizi Xiao, Heiye, and Huaizhi. The dataset includes three distinct ripening stages: immature, semi-ripe, and mature, comprising a total of 11,414 images. These include 878 original RGB images, 8,780 enhanced RGB images, and 1,756 depth images. The images are organized into five folders based on collection date, such as Image_YoloLabel_250605_Outdoor_sunny. Each folder includes descriptions of the weather and indoor/outdoor scenes. Each image and its labeled file is numbered, and includes the data augmentation method, number of categories, and similarity score (e.g., ID-0001_cc_unripe-0_semi-ripe-0_ripe-2_sim-0.0016.jpg). These images are labeled with 9,658 pairs of tags for lychee detection and ripeness classification during robotic harvesting. To improve the consistency of annotation, three researchers independently annotated the data, and then a fourth reviewer summarized and verified their results.

Lychee is a high-value subtropical fruit. The adoption of vision-based harvesting robots can significantly improve productivity while reduce reliance on labor. High-quality data are essential for developing such harvesting robots. However, there are currently no consistently and comprehensively annotated open-source lychee datasets featuring fruits in natural growing environments. To address this, we constructed a dataset to facilitate lychee detection and maturity classification. Color (RGB) images were acquired under diverse weather conditions, and at different times of the day, across multiple lychee varieties, such as Nuomici, Feizixiao, Heiye, and Huaizhi. The dataset encompasses three different ripeness stages and contains 11,414 images, consisting of 878 raw RGB images, 8,780 augmented RGB images, and 1,756 depth images. The images are annotated with 9,658 pairs of lables for lychee detection and maturity classification. To improve annotation consistency, three individuals independently labeled the data, and their results were then aggregated and verified by a fourth reviewer. Detailed statistical analyses were done to examine the dataset. Finally, we performed experiments using three representative deep learning models to evaluate the dataset. It is publicly available for academic use.