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. 2023 Mar 14:14:1087239.
doi: 10.3389/fpls.2023.1087239. eCollection 2023.

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

Azar Khorsandi  1 , Karen Tanino  2 , Scott D Noble  3
Affiliations

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

Azar Khorsandi et al. Front Plant Sci. .

Erratum in

Abstract

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.

Keywords: LiDAR; phenomics; phenotyping; spatial sampling; wheat.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Data acquisition platform. Left column (A, C, E) shows the system used in 2019 experiment, right column (B, D, F) shows the 2020 system. Top row: photographs of the systems in use. Middle row: a top view diagram of data acquisition for the year. Bottom row: Front view showing the plane of the LiDAR scan.
Figure 2
Figure 2
The effect of the different number of contiguous scan lines for applying height correction pre-processing on ground peak elevation on CVP.
Figure 3
Figure 3
(A) the LiDAR images obtained from a typical plot on 15/08/2019, highlighting the scanned region covered by different angular FOV. (B) a representation of the FOV geometry viewed perpendicularly from the scanning plane.
Figure 4
Figure 4
Determination of ground elevation from LiDAR data on 15/08/2019. Histogram of distance from the LiDAR and CVP after distance correction and height correction pre-processing were applied for a typical plot (A) (Acadia) when the distance between LiDAR and ground was consistent and (B) (Stettler) when there was variation in payload height with respect to the ground.
Figure 5
Figure 5
The average of 10 CVPs obtained from 800×240 random points and the standard deviation of these CPV graphs- in different canopy heights.
Figure 6
Figure 6
The relationship between the number of random points used and RMSE compared to using the whole area points, including and excluding the ground-peak region.
Figure 7
Figure 7
The CVP obtained from LiDAR data provided with 48000, 144000 and whole plot data.
Figure 8
Figure 8
The correlation between areas containing the different number of plants per row and RMSE.
Figure 9
Figure 9
The CVP obtained from LiDAR data provided with three different numbers of plants per row.
Figure 10
Figure 10
CVPs for FOV of 12, 36 and 60° for wheat genotype Superb (A) and for the wheat genotype of Stettler (B) on 15/08/2019 and the difference in the proportion of areas for FOV 12 and 60° for height greater than 40 cm.
Figure 11
Figure 11
Variation of normalized CVP for two directions of travel (A) parallel to (or with) the rows and (B) perpendicular to (across) the rows.
Figure 12
Figure 12
Comparing the CVP obtained from (A) the same directions of travel and (B) the perpendicular directions of travel from one container on 09/09/2020.
Figure 13
Figure 13
The Repeatability of CVP graphs obtained in three consequence days. Comparing scans collected (A) from the same directions of travel and (B) from the perpendicular directions of travel during three days (9, 10 and 11 of September 2020) from the same container.
See this image and copyright information in PMC

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