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Deep Neural Networks for Image-Based Dietary Assessment
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Citrus dataset for image classification.

Mei-Ling Huang1, You-An Chen1

  • 1Department of Industrial Engineering & Management, National Chin-Yi University of Technology, Taichung, Taiwan.

Data in Brief
|October 23, 2023
PubMed
Summary
This summary is machine-generated.

This study developed a citrus fruit image database and used Convolutional Neural Network models to accurately classify four common Taiwanese citrus varieties, achieving over 95% accuracy. This aids in identifying these economically important subtropical fruits.

Keywords:
AugmentationCitrusImage classification

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Area of Science:

  • Agricultural Science
  • Computer Science
  • Food Science

Background:

  • Citrus fruits are vital subtropical crops in Taiwan, rich in nutrients and antioxidants.
  • Several citrus species exhibit similar appearances, posing identification challenges.
  • Accurate classification is crucial for Taiwan's economically significant citrus industry.

Purpose of the Study:

  • To create a comprehensive image database of commonly marketed citrus varieties in Taiwan.
  • To evaluate the performance of Convolutional Neural Network (CNN) models in classifying citrus species.
  • To support the accurate identification of economically important citrus fruits.

Main Methods:

  • Construction of an image database with 1379 original images of four common citrus varieties.
  • Data augmentation techniques expanded the dataset to 7584 images.
  • Three distinct Convolutional Neural Network (CNN) models were trained and evaluated for classification accuracy.

Main Results:

  • The developed image database contains 7584 augmented images across four citrus varieties.
  • All three selected CNN models demonstrated classification accuracy exceeding 95%.
  • The models effectively distinguished between visually similar citrus species.

Conclusions:

  • High-accuracy CNN models can reliably classify common Taiwanese citrus varieties.
  • The image database and classification models offer valuable tools for the citrus industry.
  • This research contributes to the technological advancement of agricultural product identification.