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

Abstract

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

Plant phenotyping relevance

ライチ果実の成熟段階という植物器官の状態をRGB-D画像から分類するデータセットを構築し、アノテーション検証と深層学習モデル評価を行っており、表現型取得・評価手法が中心である。

Code and data availability

The authors publicly release the paper's lychee RGB-D image dataset (raw/augmented RGB images, depth maps, detection and maturity annotations) and the Python scripts for data augmentation, image similarity comparison, and annotation in the same GitHub repository.