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

Abstract

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.

Plant phenotyping relevance

イネ粒の充実・不充実という植物器官の状態を、構造化光3D画像と点群処理・深層学習で分類する手法を開発し、複数手法と比較検証しているため、植物フェノタイピング手法が中心である。

Code and data availability

The paper's rice grain 3D point cloud dataset and analysis code are not publicly deposited; the Data Availability Statement says raw datasets are available only upon request from the authors. No public URL for data or code is provided.

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