Evidence map›Paper›PMID 41602520›Full record

ArticleFrontiers in plant science2025

A 3D stem diameter measurement method for field maize at jointing stage: combining RLRSA-PointNet++ and structural feature fitting.

Jing Zhou, Yijia Tang, Mingren Cui, Wenlong Zou, Yudi Gao, Yushan Wu, Min Wu, Bowen Jiang, Zhenghong Zhong, Yujie Zou and 2 more

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Article in Frontiers in plant science, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 citing paper in PubMed.

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5 · Who and what money

Authors and funding

12 authors.

Jing ZhouCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Yijia TangCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Mingren CuiCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Wenlong ZouCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Yudi GaoCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Yushan WuCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Min WuCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Bowen JiangCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Zhenghong ZhongCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Yujie ZouCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Lixin HouCollege of Information Technology, Jilin Agricultural University, Changchun, China.
Haijuan TianA Jilin Province Key Laboratory of Grain and Oil Processing, Jilin Business and Technology College, Changchun, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: In precision agriculture, accurate measurement of maize stem diameter during the jointing stage is crucial for lodging resistance assessment and yield prediction. However, existing methods have certain limitations: manual measurement is time-consuming and highly subjective, while two-dimensional image recognition can only capture local features and fails to reconstruct the true three-dimensional structure of the stem. Therefore, there is a critical need for an accurate and automated three-dimensional stem diameter measurement approach. Methods: This study proposes a three-dimensional stem diameter measurement method that integrates an improved PointNet++ segmentation network with structural feature fitting, focusing on the position of the second above-ground internode of maize plants. Specifically, multi-view image reconstruction is employed to generate three-dimensional point clouds of maize stems, and Relative Position Encoding, the Local Group Rearrangement Module, and the Local Region Self-Attention mechanism are incorporated into the PointNet++ network to achieve precise segmentation of stems from the ground. On this basis, a structural feature fitting strategy is applied, where principal axis analysis and ellipse fitting are utilized to extract cross-sectional features, thereby obtaining the major axis and minor axis parameters for stem diameter estimation. Results: Experimental results demonstrate that the proposed method maintains high accuracy under complex field conditions, achieving a mean absolute error (MAE) of 1.27 mm (R² = 0.87) for major-axis stem diameter and 1.38 mm (R² = 0.82) for minor-axis stem diameter. Discussion: The proposed method effectively overcomes the limitations of traditional manual and two-dimensional measurement techniques. It provides a robust and accurate solution for maize stem diameter measurement during the jointing stage. This approach offers technical support for intelligent maize growth monitoring, lodging resistance analysis, and three-dimensional phenotypic trait extraction.

Indexed as

maize stem diameterPointNet++semantic segmentationstructural feature fittingthree-dimensional point cloud

Identifiers

PMID41602520
PMCPMC12833335

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