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Research on bamboo strip density control technology based on deep learning.

Ziyi Liu1,2, Wenfu Zhang3, Ying Zhao2

  • 1College of Chemical and Materials Engineering, Zhejiang Agriculture and Forestry University, Hangzhou, 311300, China.

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|December 2, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces automated bamboo strip density detection using deep learning. The ConvNeXt model achieved 99% accuracy in classifying vascular bundle density, improving quality control.

Keywords:
Bamboo densityConvNeXtDeep learningVascular bundles

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Area of Science:

  • Materials Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Traditional bamboo strip quality control relies on manual inspection, which is time-consuming and subjective.
  • Bamboo density is a critical quality parameter, influenced by vascular bundle distribution.
  • Automated methods are needed to improve the efficiency and accuracy of bamboo density assessment.

Purpose of the Study:

  • To develop a deep learning-based method for automated bamboo strip density detection.
  • To quantify bamboo density by analyzing vascular bundle distribution in cross-sectional images.
  • To compare the performance of various deep learning models for this task.

Main Methods:

  • A dataset of bamboo strip cross-sectional images was compiled.
  • Eleven mainstream deep learning models, including Convolutional Neural Networks (CNNs) and Transformers, were trained and evaluated.
  • Vascular bundle density classification was performed using these models.

Main Results:

  • The ConvNeXt model demonstrated superior performance in vascular bundle density classification.
  • The ConvNeXt model achieved a classification accuracy of 99%.
  • Deep learning models effectively analyzed vascular bundle distribution for density quantification.

Conclusions:

  • Deep learning offers an effective and automated solution for bamboo strip density control.
  • The ConvNeXt model shows significant potential for precise bamboo quality assessment.
  • This research highlights the applicability of AI in enhancing material quality inspection processes.