Lychee13-3634: A new lychee image dataset and classification methodological evaluation - Shaoye Luo; Hanling Zheng; Ziyang Lin; Tingting Zeng; Miaomiao Huang; Yongyi Xiao; Antoni Grau; Jiayan Huang (PLoS ONE, 2025)
The rapid and accurate classification of lychee varieties is crucial for improving production efficiency and optimizing market supply. Especially for the main production areas of lychee, efficient lychee classification is more urgent. However, there is currently no publicly available comprehensive and diverse lychee benchmark dataset for precise training of classification models. To fill this gap, this work constructs a comprehensive lychee image dataset (Lychee13-3634), which covers 13 varieties and 3634 images. Different from the general fruit datasets, which show significant differences in features between their fruit images, Lychee13-3634 highlights minor inter-class differences among various lychee varieties. Based on this dataset, we applied 20 advanced deep learning-based classification models to validate its availability and effectiveness. Meanwhile, we comprehensively evaluated and provided meaningful insights about all models. Experimental results show that EfficientNetv2 has the best classification performance with an accuracy of up to 99.90%. Besides, we further comprehensively analyzed the balance of Lychee13-3634, and the corresponding experiments demonstrate that a more balanced dataset usually leads to better classification performance of the model. In summary, Lychee13-3634 provides benchmark training data for the lychee image classification task and demonstrates the effective application of existing deep learning classification models, providing reference and inspiration for other agricultural product image recognition research. Our Lychee13-3634 and all evaluation models are available at https://github.com/jyanhuang/Lychee13-3634 .