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Design and Development of a Low-Cost UGV 3D Phenotyping Platform with Integrated LiDAR and Electric Slide Rail

文献类型： 外文期刊

作者： Cai, Shuangze 1 ; Gou, Wenbo 2 ; Wen, Weiliang 2 ; Lu, Xianju 2 ; Fan, Jiangchuan 2 ; Guo, Xinyu 1 ;

作者机构： 1.Jiangsu Univ, Sch Agr Equipment Engn, Zhenjiang 212013, Peoples R China

2.Natl Engn Res Ctr Informat Technol Agr, Beijing Key Lab Digital Plant, Beijing 100097, Peoples R China

3.Beijing Acad Agr & Forestry Sci, Informat Technol Res Ctr, Beijing 100097, Peoples R China

4.Beijing PAIDE Sci & Technol Dev Co Ltd, Beijing 100097, Peoples R China

关键词： 3D phenotyping platform; electric slide rail; LiDAR; low-cost UGV; point cloud processing

期刊名称：PLANTS-BASEL （ 影响因子：4.5； 五年影响因子：4.8 ）

ISSN：

年卷期： 2023 年 12 卷 3 期

页码：

收录情况： SCI

摘要： Unmanned ground vehicles (UGV) have attracted much attention in crop phenotype monitoring due to their lightweight and flexibility. This paper describes a new UGV equipped with an electric slide rail and point cloud high-throughput acquisition and phenotype extraction system. The designed UGV is equipped with an autopilot system, a small electric slide rail, and Light Detection and Ranging (LiDAR) to achieve high-throughput, high-precision automatic crop point cloud acquisition and map building. The phenotype analysis system realized single plant segmentation and pipeline extraction of plant height and maximum crown width of the crop point cloud using the Random sampling consistency (RANSAC), Euclidean clustering, and k-means clustering algorithm. This phenotyping system was used to collect point cloud data and extract plant height and maximum crown width for 54 greenhouse-potted lettuce plants. The results showed that the correlation coefficient (R2) between the collected data and manual measurements were 0.97996 and 0.90975, respectively, while the root mean square error (RMSE) was 1.51 cm and 4.99 cm, respectively. At less than a tenth of the cost of the PlantEye F500, UGV achieves phenotypic data acquisition with less error and detects morphological trait differences between lettuce types. Thus, it could be suitable for actual 3D phenotypic measurements of greenhouse crops.

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