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

Shuangze Cai; Wenbo Gou; Weiliang Wen; Xianju Lu; Jiangchuan Fan; Xinyu Guo

doi:10.3390/plants12030483

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

Plants (Basel). 2023 Jan 20;12(3):483. doi: 10.3390/plants12030483.

Authors

Shuangze Cai^{1

2}, Wenbo Gou², Weiliang Wen^{2

3}, Xianju Lu², Jiangchuan Fan^{2

4}, Xinyu Guo^{1

2

3}

Affiliations

¹ School of Agricultural Equipment Engineering, Jiangsu University, Zhenjiang 212013, China.
² Beijing Key Lab of Digital Plant, National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China.
³ Information Technology Research Center, Beijing Academy of Agriculture and Forestry Sciences, Beijing 100097, China.
⁴ Beijing PAIDE Science and Technology Development Co., Ltd., Beijing 100097, China.

Abstract

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.

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

Grants and funding

This research was funded by the National Key R&D Program (2022YFD2002305), Construction of Beijing Nova Program (Z211100002121065), Collaborative Innovation Center of Beijing Academy of Agricultural and Forestry Sciences (KJCX201917), and Science and Technology Innovation Special Construction Funded Program of Beijing Academy of Agriculture and Forestry Sciences (KJCX20210413).