农业机械学报2026，Vol.57Issue(1)：41-50,10.DOI:10.6041/j.issn.1000-1298.2026.01.004
基于UGV表型平台的作物三维表型获取方法与性能对比研究
Comparison of Crop Three-dimensional Phenotyping Methods and Performance Based on UGV Phenotyping Platform
摘要
Abstract
High-throughput 3D crop phenotyping is one of the core methodologies in modern crop phenomics research,providing crucial data support for holistic morphological structure analysis,precise evaluation of plant architectural traits,and genotype-phenotype association analysis.Aiming to address the challenges of low efficiency and limited data accuracy inherent in traditional manual measurements,a high-throughput 3D crop phenotyping data acquisition platform was developed based on an unmanned ground vehicle(UGV).The performance of four mainstream sensors(FLIR visible light camera,Kinect DK,Velodyne VLP-16,and Livox Avia)and their corresponding 3D reconstruction algorithms for crop phenotyping were systematically investigated.Specifically,it was compared the 3D reconstruction from visible light images based on structure-from-motion(SfM)and multi-view stereo(MVS),3D reconstruction from RGB-depth images based on iterative closest point(ICP),point cloud reconstruction from solid-state LiDAR leveraging LiDAR-inertial odometry(LIO)and point cloud stitching from mechanical rotating LiDAR by using uniform velocity frame superposition.Experiments were conducted on potted lettuce plants in a greenhouse,where point cloud data acquired by the four methods underwent standardized processing.An automated processing pipeline was developed,enabling precise extraction and analysis of key phenotypic parameters,such as plant height and maximum canopy width.This research thoroughly explored and analyzed the characteristics,advantages,and disadvantages of each method.Their applicability was comprehensively evaluated based on point cloud quality,reconstruction efficiency,phenotypic trait accuracy and system cost.The findings can not only provide experimental basis for sensor selection and algorithm development of 3D phenotyping UGVs but also can offer valuable references for breeders and agronomists in selecting efficient and accurate phenotyping data acquisition approaches.关键词
作物表型/无人车/传感器/高通量/三维重建Key words
crop phenotyping/unmanned ground vehicle/sensor/high-throughput/3D reconstruction分类
信息技术与安全科学引用本文复制引用
YANG Si,GUO Xinyu,CAI Shuangze,GOU Wenbo,LU Xianju,QIU Guangjie..基于UGV表型平台的作物三维表型获取方法与性能对比研究[J].农业机械学报,2026,57(1):41-50,10.基金项目
北京市农林科学院改革与发展项目(GCFZ20240102)、国家重点研发计划项目(2022YFD2002300)、北京市乡村振兴项目(NY2401040025)、北京市农林科学院博士后基金项目和中国博士后科学基金项目(2025MM772488) （GCFZ20240102）
