An RGB-D Image Dataset for Lychee Detection and Maturity Classification for Robotic Harvesting

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.荔枝是一种高价值亚热带水果。采用基于视觉的采摘机器人可显著提升生产效率，同时降低对人工劳动力的依赖。高质量数据是研发此类采摘机器人的核心前提。然而，当前尚无针对自然生长环境下荔枝果实、标注规范统一且全面的开源数据集。为此，本研究构建了一款面向荔枝检测与成熟度分类任务的数据集。本数据集采集了多个荔枝品种（如糯米糍、妃子笑、黑叶、怀枝）在不同天气条件、不同时段下的红-绿-蓝（RGB）彩色图像。该数据集涵盖3个成熟度等级，共包含11414张图像，其中原始RGB图像878张、增强RGB图像8780张以及深度图像1756张。针对荔枝检测与成熟度分类任务，该数据集共标注了9658组标签。为提升标注一致性，本研究由3名标注人员独立完成标注，随后由第四名审核人员对标注结果进行汇总与核验。研究团队对该数据集开展了详细的统计分析。最后，本研究选用3种典型深度学习模型开展实验，以对该数据集进行性能评估。本数据集可公开获取用于学术研究。