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ORIGINAL RESEARCH article

Front. Plant Sci.
Sec. Technical Advances in Plant Science
Volume 15 - 2024 | doi: 10.3389/fpls.2024.1508449
This article is part of the Research Topic Machine Vision and Machine Learning for Plant Phenotyping and Precision Agriculture, Volume II View all articles

Segmentation of Tobacco Shred Point Cloud and 3-D Measurement Based on Improved PointNet++ Network with DTC algorithm

Provisionally accepted
Yihang Wang Yihang Wang 1haiwei Zheng haiwei Zheng 2Jie Yang Jie Yang 2Yan Wang Yan Wang 3Li Wang Li Wang 1Qunfeng Niu Qunfeng Niu 1*
  • 1 Henan University of Technology, Zhengzhou, China
  • 2 Guangxi China Tobacco Industry Corporation Limited, Guang xi, China
  • 3 Henan Centreline Electronic Technology Co., zhengzhou, China

The final, formatted version of the article will be published soon.

    The three dimensions of the tobacco silk components (cut stem, tobacco silk, reconstituted tobacco shred, and expanded tobacco silk) of cigarettes directly affect cigarette combustibility; by accurately measuring the dimensions of different tobacco silks in cigarettes, it is possible to optimize combustibility and reduce the production of harmful substances. Identifying the components of tobacco shred in cigarettes is a prerequisite for three-dimensional measurement. The two-dimensional image method can identify the tobacco shred and measure its two-dimensional characteristics but cannot determine its thickness. This study therefore focuses on the identification of the tobacco shred and measuring it in three dimensions. The point cloud data of the upper and lower surfaces of tobacco shred are segmented using the improved three-dimensional point cloud segmentation model based on the PointNet++ network. This model combines the weighted cross-entropy loss function to enhance the classification effect, the cosine annealing algorithm to optimize the training process, and the improved k-nearest neighbors multi-scale grouping method to enhance the model's ability to segment the point cloud with complex morphology. Meanwhile, this study also proposes a dimension transformation calculation method for calculating the three dimensions of tobacco shred. The experimental results show that the precision and recall of the improved segmentation model increased from 84.27% and 83.63% to 95.13% and 97.68%, respectively; the relative errors of the length and width of tobacco shred were less than 5% and 7%, and the relative error of the standard gauge block thickness measurement reached 1.12%. This study also provides a new idea for implementing threedimensional measurements of other flexible materials.

    Keywords: Blended tobacco shred, PointNet++, Semantic segmentation, Non-contact measurement, DTC

    Received: 09 Oct 2024; Accepted: 13 Dec 2024.

    Copyright: © 2024 Wang, Zheng, Yang, Wang, Wang and Niu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Qunfeng Niu, Henan University of Technology, Zhengzhou, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.