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

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.

Plant phenotyping relevance

LiDAR搭載UGVによる植物3D形質取得プラットフォームを開発し、植物体高・最大冠幅の抽出を手測定と比較検証しており、フェノタイピング手法が研究の中心である。

Code and data availability

The paper describes a UGV-LiDAR phenotyping platform and pipeline for 54 potted lettuce plants, but provides no public dataset, image, code, or model deposit. The Data Availability Statement reads 'Not applicable.' The only URL present (Phenospex PlantEye F500) is a commercial product page for a cited comparison sensor

No evidence-backed public reproduction asset is currently recorded.