Organ-level phenotyping extraction of potted wheat key growth period based on point cloud

Authors

  • Xu Zhang 1. College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, Shandong, China;
  • Weiting Pan 1. College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, Shandong, China;
  • Deran Cheng 1. College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, Shandong, China;
  • Chunying Wang 1. College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, Shandong, China; 2. Shandong Engineering Research Center of Agricultural Equipment Intelligentization, Shandong Agricultural University,Tai’an 271018, Shandong, China;
  • Haixia Yu 3. State Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai’an 271018, Shandong, China
  • Ping Liu 1. College of Mechanical and Electronic Engineering, Shandong Agricultural University, Tai’an 271018, Shandong, China; 2. Shandong Engineering Research Center of Agricultural Equipment Intelligentization, Shandong Agricultural University,Tai’an 271018, Shandong, China;
  • Xiang Li 3. State Key Laboratory of Wheat Improvement, Shandong Agricultural University, Tai’an 271018, Shandong, China

Abstract

Genotyping and phenotyping are critical for wheat breeding, with accurate phenotypic acquisition from potted wheat using 3D point cloud technology essential to overcoming bottlenecks from slow, inefficient processes. This study focuses on developing methods for accurate organ-level phenotypic data extraction from potted wheat plants using 3D point cloud techniques. The study exploited a multi-view acquisition system to construct point cloud datasets for wheat key growth stages and trained a network. Semantic (organ classification) and instance (organ segmentation) segmentation were performed on potted wheat to extract organ-level wheat point clouds. However, the imbalance in point cloud proportions among different wheat organs caused low semantic segmentation accuracy, and the interference from awns caused significant distortion in ear point clouds after instance segmentation. To address these issues, a class-related sampling strategy based on RandLA-Net was proposed, which balances various organ point clouds through a class-related sampling strategy. In addition, a geometric symmetry-based point cloud completion method was introduced to replenish the distorted wheat ear point cloud. Based on the segmented organ point clouds, phenotypic parameters were obtained using minimum bounding box and quadratic surface fitting methods. The results showed that semantic segmentation accuracy improved by 13.6% through class point balancing, and the determination coefficient for the extracted ear volume increased by 10.2% after point cloud completion. The obtained phenotypic parameters showed a strong correlation with manual measurements (determination coefficients ranging from 0.7737 to 0.9552). These phenotype acquisition methods provide accurate and practical phenotypic acquisition, supporting wheat optimization and superior gene screening.      

Keywords wheat, key growth period, 3D point cloud, organ segmentation, phenotypic analysis

DOI: 10.25165/j.ijabe.20261903.10466

Citation: Zhang X, Pan W T, Cheng D R, Wang C Y, Yu H X, Liu P, et al. Organ-level phenotyping extraction of potted wheat key growth period based on point cloud. Int J Agric & Biol Eng, 2026; 19(3): 243–255.

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Published

2026-07-14

How to Cite

(1)
Zhang, X.; Pan, W.; Cheng, D.; Wang, C.; Yu, H.; Liu, P.; Li, X. Organ-Level Phenotyping Extraction of Potted Wheat Key Growth Period Based on Point Cloud. Int J Agric & Biol Eng 2026, 19, 243–255.

Issue

Vol. 19 No. 3 (2026): IJABE

Section

Information Technology, Sensors and Control Systems

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