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Lychee13-3634: A new lychee image dataset and classification methodological evaluation.

Shaoye Luo1,2, Hanling Zheng1, Ziyang Lin1

  • 1College of Computer and Data Science, Putian University, Putian, Fujian, China.

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|October 23, 2025
PubMed
Summary
This summary is machine-generated.

A new lychee image dataset (Lychee13-3634) with 13 varieties was created to address the lack of benchmark data. EfficientNetv2 achieved 99.90% accuracy in lychee classification using this dataset.

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Area of Science:

  • Agricultural Science
  • Computer Vision
  • Machine Learning

Background:

  • Accurate lychee variety classification is vital for production efficiency and market supply.
  • Existing datasets lack the diversity and comprehensiveness needed for precise lychee classification models.
  • Minor inter-class differences among lychee varieties pose a challenge for automated classification.

Purpose of the Study:

  • To construct a comprehensive and diverse lychee image dataset (Lychee13-3634) for benchmark training.
  • To evaluate the performance of deep learning models on lychee image classification.
  • To provide insights into dataset balance and its impact on classification accuracy.

Main Methods:

  • Construction of the Lychee13-3634 dataset, comprising 3634 images across 13 lychee varieties.
  • Application and evaluation of 20 advanced deep learning-based classification models.
  • Analysis of dataset balance and its correlation with model performance.

Main Results:

  • The Lychee13-3634 dataset effectively highlights subtle differences between lychee varieties.
  • EfficientNetv2 demonstrated superior performance, achieving 99.90% accuracy in lychee classification.
  • Dataset balance was found to positively influence model classification performance.

Conclusions:

  • The Lychee13-3634 dataset serves as a valuable benchmark for lychee image classification research.
  • Deep learning models, particularly EfficientNetv2, are effective for lychee variety identification.
  • The study provides a foundation for future agricultural product image recognition research.