A 3D Stem Diameter Measurement Method for Field Maize at Jointing Stage: Combining RLRSA-PointNet++ and Structural Feature Fitting

Jing Zhou¹Yijia Tang¹Mingren Cui¹Wenlong Zou¹Yudi Gao¹Yushan Wu¹Min Wu¹Bowen Jiang¹Zhenghong Zhong¹Yujie Zou¹Lixin Hou^1*Haijuan Tian^2*

• ¹College of Information Technology, Jilin Agricultural University, Changchun, China
• ²A Jilin Province Key Laboratory of Grain and Oil Processing, Jilin Business and Technology College, Changchun, China

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. To address these issues, 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. 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. This method optimizes traditional manual measurement and provides technical support for intelligent maize monitoring, lodging resistance studies, as well as three-dimensional phenotypic measurement and genetic improvement of crops.