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Automated Phenotypic Analysis of Mature Soybean Using Multi-View Stereo 3D Reconstruction and Point Cloud Segmentation

Author

Listed:
  • Daohan Cui

    (College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China)

  • Pengfei Liu

    (College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China)

  • Yunong Liu

    (College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China)

  • Zhenqing Zhao

    (College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China
    National Key Laboratory of Smart Farm Technologies and Systems, Northeast Agricultural University, Harbin 150030, China)

  • Jiang Feng

    (College of Electrical and Information, Northeast Agricultural University, Harbin 150030, China)

Abstract

Phenotypic analysis of mature soybeans is a critical aspect of soybean breeding. However, manually obtaining phenotypic parameters not only is time-consuming and labor intensive but also lacks objectivity. Therefore, there is an urgent need for a rapid, accurate, and efficient method to collect the phenotypic parameters of soybeans. This study develops a novel pipeline for acquiring the phenotypic traits of mature soybeans based on three-dimensional (3D) point clouds. First, soybean point clouds are obtained using a multi-view stereo 3D reconstruction method, followed by preprocessing to construct a dataset. Second, a deep learning-based network, PVSegNet (Point Voxel Segmentation Network), is proposed specifically for segmenting soybean pods and stems. This network enhances feature extraction capabilities through the integration of point cloud and voxel convolution, as well as an orientation-encoding (OE) module. Finally, phenotypic parameters such as stem diameter, pod length, and pod width are extracted and validated against manual measurements. Experimental results demonstrate that the average Intersection over Union (IoU) for semantic segmentation is 92.10%, with a precision of 96.38%, recall of 95.41%, and F1-score of 95.87%. For instance segmentation, the network achieves an average precision (AP@50) of 83.47% and an average recall (AR@50) of 87.07%. These results indicate the feasibility of the network for the instance segmentation of pods and stems. In the extraction of plant parameters, the predicted values of pod width, pod length, and stem diameter obtained through the phenotypic extraction method exhibit coefficients of determination ( R 2 ) of 0.9489, 0.9182, and 0.9209, respectively, with manual measurements. This demonstrates that our method can significantly improve efficiency and accuracy, contributing to the application of automated 3D point cloud analysis technology in soybean breeding.

Suggested Citation

  • Daohan Cui & Pengfei Liu & Yunong Liu & Zhenqing Zhao & Jiang Feng, 2025. "Automated Phenotypic Analysis of Mature Soybean Using Multi-View Stereo 3D Reconstruction and Point Cloud Segmentation," Agriculture, MDPI, vol. 15(2), pages 1-22, January.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:2:p:175-:d:1566987
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    References listed on IDEAS

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    1. Yongzhe Sun & Zhixin Zhang & Kai Sun & Shuai Li & Jianglin Yu & Linxiao Miao & Zhanguo Zhang & Yang Li & Hongjie Zhao & Zhenbang Hu & Dawei Xin & Qingshan Chen & Rongsheng Zhu, 2023. "Soybean-MVS: Annotated Three-Dimensional Model Dataset of Whole Growth Period Soybeans for 3D Plant Organ Segmentation," Agriculture, MDPI, vol. 13(7), pages 1-19, June.
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