The effects of sampling and instrument orientation on LiDAR data from crop plots

Frontiers in Plant Science

Plant Science

Azar Khorsandi¹  Scott D. Noble²  Karen Tanino³ 

[1] Department of Chemical and Biological Engineering, College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada;Department of Mechanical Engineering, College of Engineering, University of Saskatchewan, Saskatoon, SK, Canada;Department of Plant Sciences, College of Agriculture and Bioresources, University of Saskatchewan, Saskatoon, SK, Canada;

关键词: wheat;    LiDAR;    phenomics;    phenotyping;    spatial sampling;   

DOI  :  10.3389/fpls.2023.1087239

 received in 2022-11-02, accepted in 2023-02-14,  发布年份 2023

来源: Frontiers

【 摘 要 】

Wheat is one of the most widely consumed grains in the world and improving its yield, especially under severe climate conditions, is of great importance to world food security. Phenotyping methods can evaluate plants according to their different traits, such as yield and growth characteristics. Assessing the vertical stand structure of plants can provide valuable information about plant productivity and processes, mainly if this trait can be tracked throughout the plant’s growth. Light Detection And Ranging (LiDAR) is a method capable of gathering three-dimensional data from wheat field trials and is potentially suitable for providing non-destructive, high-throughput estimations of the vertical stand structure of plants. The current study considers LiDAR and focuses on investigating the effects of sub-sampling plot data and data collection parameters on the canopy vertical profile (CVP). The CVP is a normalized, ground-referenced histogram of LiDAR point cloud data representing a plot or other spatial domain. The effects of sub-sampling of plot data, the angular field of view (FOV) of the LiDAR and LiDAR scan line orientation on the CVP were investigated. Analysis of spatial sub-sampling effects on CVP showed that at least 144000 random points (600 scan lines) or an area equivalent to three plants along the row were adequate to characterize the overall CVP of the aggregate plot. A comparison of CVPs obtained from LiDAR data for different FOV showed that CVPs varied with the angular range of the LiDAR data, with narrow ranges having a larger proportion of returns in the upper canopy and a lower proportion of returns in the lower part of the canopy. These findings will be necessary to establish minimum plot and sample sizes and compare data from studies where scan direction or field of view differ. These advancements will aid in making comparisons and inform best practices for using close-range LiDAR in phenotypic studies in crop breeding and physiology research.

【 授权许可】

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