A Novel Method for Filled/Unfilled Grain Classification Based on Structured Light Imaging and Improved PointNet plus

文献类型： 外文期刊

第一作者： Huang, Shihao

作者： Huang, Shihao;Lu, Zhihao;Shi, Yuxuan;Dong, Jiale;Hu, Lin;Huang, Chenglong;Huang, Shihao;Huang, Chenglong;Huang, Shihao;Huang, Chenglong;Yang, Wanneng

作者机构：

关键词： 3D structured light; point cloud segmentation; data enhancement; deep learning; normal vector; grain classification

期刊名称：SENSORS （ 影响因子：3.9； 五年影响因子：4.1 ）

ISSN：

年卷期： 2023 年 23 卷 14 期

页码：

收录情况： SCI

摘要： China is the largest producer and consumer of rice, and the classification of filled/unfilled rice grains is of great significance for rice breeding and genetic analysis. The traditional method for filled/unfilled rice grain identification was generally manual, which had the disadvantages of low efficiency, poor repeatability, and low precision. In this study, we have proposed a novel method for filled/unfilled grain classification based on structured light imaging and Improved PointNet++. Firstly, the 3D point cloud data of rice grains were obtained by structured light imaging. And then the specified processing algorithms were developed for the single grain segmentation, and data enhancement with normal vector. Finally, the PointNet++ network was improved by adding an additional Set Abstraction layer and combining the maximum pooling of normal vectors to realize filled/unfilled rice grain point cloud classification. To verify the model performance, the Improved PointNet++ was compared with six machine learning methods, PointNet and PointConv. The results showed that the optimal machine learning model is XGboost, with a classification accuracy of 91.99%, while the classification accuracy of Improved PointNet++ was 98.50% outperforming the PointNet 93.75% and PointConv 92.25%. In conclusion, this study has demonstrated a novel and effective method for filled/unfilled grain recognition.

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