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:

高通量作物原位三维重建技术是现代作物表型组学研究的核心方法之一，为作物形态结构解析、株型特征精准评估及表型与基因型关联分析提供了关键数据支持。针对传统人工测量效率低下且易出错的问题，本研究构建了一套基于无人地面车辆（Unmanned ground vehicle, UGV）的高通量作物三维表型数据原位获取平台，并系统探究了4种主流传感器（FLIR可见光相机、Kinect DK、Velodyne VLP-16、Livox Avia）的三维重建算法的性能。对比了基于运动恢复结构与多视角立体视觉（Structure-from-motion and multi-view stereo, SfM-MVS）的可见光图像三维重建、基于迭代最近点（Iterative closest point, ICP）的RGB-Depth图像三维重建、基于激光惯性里程计（LiDAR-inertial odometry, LIO）的固态激光雷达三维重建，以及基于匀速叠加帧的机械式激光雷达点云拼接重建的4种方法。以温室盆栽生菜为例，对4种方法获取的点云数据进行标准化处理，通过开发的自动化处理管道实现了株高和最大冠幅等关键表型参数的精确提取与分析。本研究深入探究并分析了上述方法的优缺点，从点云质量、重建效率、表型性状解析精度与系统成本等方面，对上述方法的适用性进行了全面评估。研究结果不仅可为UGV表型平台的传感器选型和算法开发提供实验依据，也可为育种家和农学家选取高效、精准的表型信息获取方式提供参考。

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.