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Citation: Saha, K.K.; Tsoulias, N.;
Weltzien, C.; Zude-Sasse, M.
Estimation of Vegetative Growth in
Strawberry Plants Using Mobile
LiDAR Laser Scanner. Horticulturae
2022,8, 90. https://doi.org/10.3390/
horticulturae8020090
Academic Editors: Riccardo Lo
Bianco, Antonino Pisciotta and
Luigi Manfrini
Received: 27 October 2021
Accepted: 17 January 2022
Published: 19 January 2022
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horticulturae
Article
Estimation of Vegetative Growth in Strawberry Plants Using
Mobile LiDAR Laser Scanner
Kowshik Kumar Saha 1,2 , Nikos Tsoulias 2, Cornelia Weltzien 1,2 and Manuela Zude-Sasse 2,*
1
Technische Universität Berlin, Straße des 17. Juni 135, 10623 Berlin, Germany; [email protected] (K.K.S.);
[email protected] (C.W.)
2Department of Horticultural Engineering, Leibniz Institute for Agricultural Engineering and
Bioeconomy (ATB), Max-Eyth-Allee 100, 14469 Potsdam, Germany; [email protected]
*Correspondence: [email protected]
Abstract:
Monitoring of plant vegetative growth can provide the basis for precise crop manage-
ment. In this study, a 2D light detection and ranging (LiDAR) laser scanner, mounted on a linear
conveyor, was used to acquire multi-temporal three-dimensional (3D) data from strawberry plants
(‘Honeoye’ and ‘Malling Centenary’) 14–77 days after planting (DAP). Canopy geometrical variables,
i.e., points per plant, height, ground projected area, and canopy volume profile, were extracted from
3D point cloud. The manually measured leaf area exhibited a linear relationship with LiDAR-derived
parameters (R
2
= 0.98, 0.90, 0.93, and 0.96 with number of points per plant, volume, height, and
projected canopy area, respectively). However, the measuring uncertainty was high in the dense
canopies. Particularly, the canopy volume estimation was adapted to the plant habitus to remove
gaps and empty spaces in the canopy point cloud. The parametric values for maximum point to point
distance (D
max
) = 0.15 cm and slice height (S) = 0.10 cm resulted in R
2
= 0.80 and RMSPE = 26.93% for
strawberry plant volume estimation considering actual volume measured by water displacement.
The vertical volume profiling provided growth data for cultivars ‘Honeoye’ and ‘Malling Centenary’
being 51.36 cm
3
at 77 DAP and 42.18 cm
3
at 70 DAP, respectively. The results contribute an approach
for estimating plant geometrical features and particularly strawberry canopy volume profile based
on LiDAR point cloud for tracking plant growth.
Keywords: canopy volume; non-destructive; point cloud; volume profile; Fragaria ×ananassa
1. Introduction
Strawberry (Fragaria
×
ananassa) is one of the worldwide grown berry crops with high
market value thanks to its perishable but unique appearance, aroma, and being rich in
secondary phytonutrients [
1
,
2
]. Strawberry is a perennial herbaceous plant that develops
fast, with fruit-bearing and maturation occurring within one season after planting [
3
].
Monitoring of the plant growth is essential to ensure high yield and profitable strawberry
production. In the concept of precise horticulture, spatio-temporally resolved data of
biomass at different phenological stages can support the optimization of drip irrigation,
spraying, and yield prediction [
4
]. For effective implementation of such precise, data-based
production management, timely and non-destructive recording of plant growth information
is a prerequisite.
Manual measurements of the leaf area and plant volume can be performed after
defoliation. However, after defoliation, the information on the canopy profile and, therefore,
light distribution in the canopy, is lost. Frequently analysed leaf area and light interception
are, however, not linearly correlated owing to varying distribution of leaf area in the
canopy and shading effects as shown in tree crops [
5
]. The non-destructive analysis of
vegetative plant structure and its distribution along the canopy axis (vertical volume profile)
would enable new research on the yield physiology of strawberry and other fruit bearing
Horticulturae 2022,8, 90. https://doi.org/10.3390/horticulturae8020090 https://www.mdpi.com/journal/horticulturae
Horticulturae 2022,8, 90 2 of 16
plants. So far, no approach was published to monitor the vertical canopy profile of herbal
strawberry plants.
Non-destructive data acquisition with high-throughput capacity became feasible ow-
ing to the recent advancements in remote sensing technologies [
6
]. Numerous remote
sensing applications in agriculture have been employed for plant geometric analysis, based
on two-dimensional images obtained with machine vision systems that use color, spectral,
and thermal imaging approaches to extract plant information. The latter techniques repre-
sent passive sensors, having no active light source, encountering limitations in field due to
varying lighting conditions, shadows, and coinciding surfaces of plant and other objects [
7
].
Recent advancement of three-dimensional (3D) remote sensing techniques enabled the
acquisition of high resolved plant information [
8
]. Several machine vision systems were
developed to acquire 3D plant information, such as binocular stereovision [
9
], time-of-flight
(TOF) RGB [
10
] including advanced RGB-depth camera [
11
], structured light [
12
], and light
detection and ranging (LiDAR) [
13
,
14
]. LiDAR sensors have recently been introduced in
horticulture to acquire digital representatives of plants with complex 3D geometry. LiDAR
sensors emit a monochromatic laser beam. The range of a targeted object is obtained when
the laser pulse emitted by a diode to specific direction, hits the object and returns to a
receiver in the same device. Taking into account the instruments response function, the
time difference between laser pulse emission and return of the backscattered laser beam to
gain time of flight (TOF) or phase shift determines the sensor distance to the object’s surface.
These systems are usually mounted on aerial or terrestrial platforms to acquire 3D point
cloud during the movement along the plants. Compared with air-borne systems, terrestrial
LiDAR are most feasible for studies at individual plant level regarding operating distances
with given divergence and distortion of commercially available LiDAR sensors [
15
,
16
].
Several studies in the last few years proved that mobile terrestrial LiDAR systems are
feasible to characterize tree crops [17,18].
Recent studies on strawberry reported the application of an RGB-depth camera to
quantify plant growth [
19
,
20
]. Guan et al. [
21
] mounted RGB and infrared cameras on
a tractor-driven platform, applying structure-from-motion technique to reconstruct the
3D point cloud of strawberries. Object-based image analysis technique was performed
by the work group to extract canopy parameters (e.g., plant height, canopy volume, and
canopy surface area) for estimating either biomass or leaf area from these canopy param-
eters. Other applications of 3D point cloud analysis in strawberry analysis capture fruit
detection
[22,23]
, analysis of fruit geometry [
24
], and mapping of genotypes based on 3D
shape of fruit
[25,26]
. However, no work was published on the description of strawberry
growth, and particularly the volume estimation, which is becoming more important in
plant protection (Directive 91/414/EEC [
27
]) and for approaching an improved method to
measure the light distribution in the dense canopy by means of the vertical profile of plant
canopy volume. The challenges of plant volume estimation are the non-uniform shape and
porosity in plant structure. The only reliable reference method existing for this purpose
is measuring the water displacement when dipping the plant under water. However, this
was never reported as a reference for remote sensing. Most studies relied on comparison of
methods, but without reference analysis. In tree phenotyping, Cheein et al. [
28
] applied
four approaches (convex hull, segmented convex hull, cylinder-based modeling, and 3D
occupancy grid) for 3D point cloud analysis to estimate the canopy volume. Convex hull
and cylinder-based modeling approaches achieved the most reasonable volume estimation.
Colaço et al. [
29
] applied convex-hull and alpha-shape to reproduce the shape of orange
tree crown and to obtain the canopy volume. Alpha-shape algorithm was found to be
suitable for individual trees, whereas convex-hull provided better estimates for transver-
sal section of orange tree row. Moreover, voxel-based canopy volume estimation was
approached for tree crops
[30–32].
A number of studies have been done on tree canopy
volume estimation by slicing 3D point cloud and estimating individual slice volumes using
different approaches applied [
33
] on 22 forest tree species, 5 tree species of coniferous and
broad-leaved [
34
], and 2 deciduous street tree species [
35
]. Slicing fruit tree point clouds
Horticulturae 2022,8, 90 3 of 16
into vertical prisms was shown to improve estimation of canopy volume [
36
]. However,
few LiDAR applications were presented for small fruit-bearing herbaceous plants.
This study was carried out to acquire multi-temporal 3D point cloud data of individual
strawberry canopy utilizing a 2D laser scanner mounted on a portable linear conveyor.
The objectives of the present study on strawberry plants were (i) to confirm the leaf area
estimation with LiDAR, (ii) to adapt the canopy volume estimation with LiDAR considering
the habitus of herbal plant, and (iii) to monitor the temporal juvenile growth by means of
vertical profiling of canopy volume.
2. Materials and Methods
2.1. Experimental Setup
The field experiments were conducted at the experimental station by Leibniz Institute
for Agricultural Engineering and Bioeconomy (ATB) located in Marquardt, Germany,
latitude 52
◦
28
0
00.4
00
N and longitude 12
◦
57
0
38.4
00
E, in the year 2020. Juvenile and mature
strawberry plants (Fragaria
×
ananassa ‘Malling Centenary’) were categorized into four
classes based on their age: (i) juvenile-1 (7 to 14 day after planting (DAP)), (ii) juvenile-2
(15 to 90 DAP), (iii) mature-1 (1 year old), and (iv) mature-2 (2 years old), and all plants
(n= 15) were measured once with a laser scanner.
For monitoring juvenile strawberry plants, ‘Malling Centenary’ (juvenile-1 and juvenile-
2, n= 20) and additionally the commercial cultivar ‘Honeoye’ (n= 20) were analysed from
April to July in 2020. Strawberry plants were purchased from the local market in Potsdam,
Germany. All juvenile samples were transplanted at 2–3 leaves stage (BBCH 13 [
37
]) in
a greenhouse equipped with drip-fertigation. ‘Honeoye’ plants were transplanted on
2 April 2020 and the late season cultivar ‘Malling Centenary’ plants were transplanted on
30 April 2020. Juvenile plants were grown in plastic 5 L planting pot with dimensions of
18 cm ×18 cm ×18 cm.
During the experiment, all plants were measured non-destructively. The plants of four
size classes were destructively measured after the experiments considering leaf area, fresh
mass, and dry mass. At the end of the monitoring experiments, additionally, the canopy
volume was measured destructively (n= 12).
2.2. Plant Reference Data
Non-destructive reference data were recorded after each LiDAR scan, capturing the
height, width, and number of leaves for each individual plant (n= 55) and repeatedly
5 times for the monitored juvenile plants.
In total, 15 plants of 4 size classes (4 juvenile-1, 4 juvenile-2, 4 mature-1, and 3 mature-
2) and 7 plants per cultivar for monitoring the juvenile plant growth were measured
destructively in the laboratory to determine fresh mass (FM), dry mass (DM), and leaf area
(LA). Plant canopies were cut at ground surface and leaves were separated from stems.
The area of all leaves was analysed with a desktop scanner (Scanjet 4850, HP, Palo Alto,
CA, USA), in groups of 5–10 leaves. The RGB-images were analysed, considering the sum
of pixels of each leaf, with Matlab (2017b, Mathworks, Natick, MA, USA) script [
38
]. An
area of 6241 pixels in the image equaled the area of 1 cm
2
. Finally, entire plants were cut
into smaller parts and oven-dried in a thin layer at 80
◦
C for 24 h until a stable weight was
reached [39].
Plant reference volume was analysed by the water displacement method [
40
]. For
this purpose, strawberry canopies (n= 12) were subdivided and submerged, avoiding air
bubbles, into a water container with a valve. The displaced water was collected in a beaker
and weighed. The weight of the displaced water was divided by temperature-corrected
density of water to obtain the canopy volume.
2.3. LiDAR Data Acquisition
A 2D LiDAR laser scanner (LMS511 pro, Sick AG, Düsseldorf, Germany), operating
based on the time of flight (TOF) measuring principle, was utilized to scan the potted plants
Horticulturae 2022,8, 90 4 of 16
at its growing location in field conditions. The sensor emitted photons at 905 nm, with a
scanning angle of 180
◦
, angular resolution of 0.1667
◦
, and frequency of 25 Hz. The laser
scanner was installed at a rigid frame and mounted on a linear tooth-belt conveyor system
(Module 115/42, IEF Werner, Germany) of 800 mm length, equipped with a servo posi-
tioning controller (LV-servoTEC S2, IEF Werner, Furtwangen, Germany) (Figure 1a,b). The
LiDAR sensor was connected via Ethernet to a laptop computer with software developed
in Visual Studio (version 16.1, Microsoft, Redmond, WA, USA) for acquisition of data as
described earlier [
14
]. In parallel, the positioning controller was connected to the same
computer for data acquisition with a RS-232 serial port, while a S2 Commander software
(version 4.1.4201.1.1, IEF Werner, Furtwangen, Germany) was used for configuration and
operation. The linear conveyor was configured at 10 mm s
−1
(
±
0.05 mm accuracy) forward
speed, with the sensor mounted at 0.6 m from ground level, being at the same height as the
strawberry plants.
Horticulturae 2022, 8, 90 4 of 16
2.3. LiDAR Data Acquisition
A 2D LiDAR laser scanner (LMS511 pro, Sick AG, Düsseldorf, Germany), operating
based on the time of flight (TOF) measuring principle, was utilized to scan the potted
plants at its growing location in field conditions. The sensor emitted photons at 905 nm,
with a scanning angle of 180°, angular resolution of 0.1667°, and frequency of 25 Hz. The
laser scanner was installed at a rigid frame and mounted on a linear tooth-belt conveyor
system (Module 115/42, IEF Werner, Germany) of 800 mm length, equipped with a servo
positioning controller (LV-servoTEC S2, IEF Werner, Furtwangen, Germany) (Figure
1a,b). The LiDAR sensor was connected via Ethernet to a laptop computer with software
developed in Visual Studio (version 16.1, Microsoft, Redmond, WA, USA) for acquisition
of data as described earlier [14]. In parallel, the positioning controller was connected to
the same computer for data acquisition with a RS-232 serial port, while a S2 Commander
software (version 4.1.4201.1.1, IEF Werner, Furtwangen, Germany) was used for configu-
ration and operation. The linear conveyor was configured at 10 mm s−1 (± 0.05 mm accu-
racy) forward speed, with the sensor mounted at 0.6 m from ground level, being at the
same height as the strawberry plants.
Each strawberry plant was scanned from a 1 m distance, presenting each plant from
two opposite sides to the laser scanner by turning the pots manually. Scanning the plants
at different size classes summed up to 30 scans (15 scans from each side). Juvenile plants
were measured in a 2-week interval during the growing season starting from 14 DAP to
77 DAP, summing up to 400 scans when measuring 40 plants from two sides on five dates.
Additional, 24 scans were carried out on 12 plants to finally analyse the plant volume
destructively.
(a) (b)
Figure 1. (a) Photograph of the LiDAR laser scanning system (LMS511 pro, Sick AG, Düsseldorf,
Germany) during strawberry plant measurement and (b) a schematic view of the LiDAR system
mounted on a linear conveyor measuring strawberry 3D point cloud. θ is the angular step and d is
the measured distance by the LiDAR laser scanner.
2.4. Reconstruction of 3D Plant Model
Raw LiDAR data were recorded in polar coordinates describing the angle (θ) from 0°
to 180° and the distance (d) of each laser hit, which converted to Cartesian coordinates (x,
y, z) with Python code (version 3.7, Python Software Foundation, Beaverton, OR, USA).
Specifically, x-y planes were defined considering d from sensor centre as origin of coordi-
nate system (Equations (1) and (2)). The displacement of LiDAR scanner in z direction was
calculated from the constant forward speed (v) and time difference (Δt) between each ver-
tical line of scan (Equations (3) and (4)).
𝑥 =𝑑 𝑐𝑜𝑠𝜃 (1)
Figure 1.
(
a
) Photograph of the LiDAR laser scanning system (LMS511 pro, Sick AG, Düsseldorf,
Germany) during strawberry plant measurement and (
b
) a schematic view of the LiDAR system
mounted on a linear conveyor measuring strawberry 3D point cloud.
θ
is the angular step and dis
the measured distance by the LiDAR laser scanner.
Each strawberry plant was scanned from a 1 m distance, presenting each plant from
two opposite sides to the laser scanner by turning the pots manually. Scanning the plants
at different size classes summed up to 30 scans (15 scans from each side). Juvenile plants
were measured in a 2-week interval during the growing season starting from 14 DAP to
77 DAP, summing up to 400 scans when measuring 40 plants from two sides on five dates.
Additional, 24 scans were carried out on 12 plants to finally analyse the plant volume
destructively.
2.4. Reconstruction of 3D Plant Model
Raw LiDAR data were recorded in polar coordinates describing the angle (
θ
) from 0
◦
to 180
◦
and the distance (d) of each laser hit, which converted to Cartesian coordinates (x,
y,z) with Python code (version 3.7, Python Software Foundation, Beaverton, OR, USA).
Specifically, x-yplanes were defined considering dfrom sensor centre as origin of coordinate
system (Equations (1) and (2)). The displacement of LiDAR scanner in zdirection was
calculated from the constant forward speed (v) and time difference (
∆
t) between each
vertical line of scan (Equations (3) and (4)).
xLiDAR =d cosθ(1)
yLiDAR =d sinθ(2)
Horticulturae 2022,8, 90 5 of 16
zLiDAR =z0+∆z(3)
∆z=v×∆t(4)
where dis the distance between a laser point and the sensor, x
LiDAR
and y
LiDAR
signify
the position of laser point in xand yaxes, z
LiDAR
is the LiDAR position along the linear
conveyor axis, z’ is the previous position of LiDAR sensor, vis the forward speed of LiDAR
sensor (0.01 ms−1), and ∆zis the displacement of the LiDAR sensor during time ∆t(s).
A distance filter was applied to segment laser points above 2 m distance and discard
points generated from background objects. In the pre-processing step, a statistical outlier
removal (SOR) filter was applied based on the maximum distance calculated by the sum of
mean distance and standard deviation of each point measured to its six neighboring points
to determine whether it is an outlier [
41
]. Subsequently, the point clouds from both sides of
strawberry plant were roughly aligned using open-source CloudCompare
®
(version 2.10)
software [
42
]. Fine registration was done by minimizing corresponding points’ distances
according to the iterative closest point algorithm [
43
], resulting in a complete point cloud
of each potted strawberry plant.
2.5. Estimation of Plant Parameters
The difference between maximum and minimum point of entire point cloud in the z
axis including the growing container was exploited to estimate the plant height. Subse-
quently, the height of the container was subtracted manually.
The number of canopy points was plotted in the xand yaxis. A line connected the
points on the boundaries, creating a polygon. Subsequently, the concave hull algorithm
was applied for estimating the projected canopy area of each plant [44].
Canopy volume was calculated by segmenting the canopy point cloud in horizontal
slices of equal height (S). Each slice of varying height (0.05–10 cm) was projected in the
xand yplane, while the Delaunay approach was performed for triangulation to create a
network of triangles having no points inside circumscribing circles. The line connecting
points was considered as an edge. The network of edges of the triangles was analyzed to
detect holes and concavities. To extract holes or voids, a maximum point to point distance
(D
max
) was set based on minimizing the root mean square percentage error (RMSPE) for
volume estimation. Edges greater than D
max
were removed from the network. In the next
step, the bounding polygon using the remaining edges was created. The edges present
in the triangulated network were analysed individually. Only those edges that belong
to a single triangle were selected [
45
], because only outer triangles can have edges not
belonging to more than one triangle. After connecting the selected edges, one or more
polygons were created. The area of the bounding polygon was recorded for each slice and
multiplied by its height [
46
]. The sum provided the volume of canopy (Equation (5)) as one
value (Figure 2).
Vplant =∑n
k=1(Ak×S)(5)
where V
plant
is total volume of strawberry canopy, kis number of slices, A
k
is area of the
k-th slice, and Sis the height of the slice.
The performance of this slicing and summing slice volume estimation approach was
compared with two widely-used volume estimation approaches: 3D convex hull [
28
,
29
]
and voxel-grid [
29
,
30
], with a grid size of 1 cm. The accuracy of these three approaches
was evaluated in terms of mean bias error (MBE), root mean square error (RMSE), RMSPE,
and coefficient of determination (R
2
) between reference and estimated strawberry canopy
volumes. Computational time was recorded and compared.
Applying the most accurate method, the volume of each slice was plotted against the
canopy height to obtain the canopy volume profiles during plant growth. Strawberry plant
growth rate was calculated from the changes in plant volume during the monitoring period
(14 to 77 DAP) [47].
Horticulturae 2022,8, 90 6 of 16
Horticulturae 2022, 8, 90 6 of 16
plant growth rate was calculated from the changes in plant volume during the monitoring
period (14 to 77 DAP) [47].
3. Results
3.1. Canopy Volume Extraction Capturing Four Size Classes
Reconstructed 3D point clouds were obtained from outdoor measurement of four
size classes of strawberry plants (Figure 2). Strawberry plants showed no consistent
growth direction, while the shape varied over the plant developmental stages. In the ju-
venile stage, strawberry canopy was less dense (average 60,660 points per plant) com-
pared with the matured stage (average 451,817 points per plant). In the juvenile stage,
gaps and empty spaces were observed underneath the leaves, while the stem and petioles
were still visible in the point clouds (Figure 2a,b). In the matured stage, stem and petioles
disappeared from the 3D point cloud owing to a dense canopy with an enhanced number
of leaves, but gaps and concavities were still present between the leaves (Figure 2c,d). The
results obtained with established 3D point cloud analysis are consistent with earlier stud-
ies (Table 1).
(a) (b)
(c) (d)
Figure 2. Point cloud of different size classes of strawberry plants: (a) juvenile-1, (b) juvenile-2, (c)
mature-1, and (d) mature-2. In these figures, 1 cm slice height is shown for visualization.
The approach of slicing and summing slice for volume estimation was tested on the
strawberry point cloud slice considering the combination of two parameters—maximum
point to point distance (D
max
) and slice height (S). D
max
has a key role in the proposed vol-
ume extraction approach to estimate an accurate canopy volume. The effect of D
max
on area
estimation is exemplarily visualized on point cloud slices of equal height considering
three D
max
values (0.15 cm, 1.0 cm, and 10.0 cm) in Figure 3. After removal of the boundary
lines larger than D
max
, the outer lines were applied to calculate the area of the polygon.
Considering all size class cases, using D
max
of 0.15 cm resulted in the lowest boundary and,
therefore, area. The use of 10.0 cm D
max
enhanced the estimated areas, whereas 0.15 cm
Figure 2.
Point cloud of different size classes of strawberry plants: (
a
) juvenile-1, (
b
) juvenile-2,
(c) mature-1, and (d) mature-2. In these figures, 1 cm slice height is shown for visualization.
3. Results
3.1. Canopy Volume Extraction Capturing Four Size Classes
Reconstructed 3D point clouds were obtained from outdoor measurement of four size
classes of strawberry plants (Figure 2). Strawberry plants showed no consistent growth
direction, while the shape varied over the plant developmental stages. In the juvenile stage,
strawberry canopy was less dense (average 60,660 points per plant) compared with the
matured stage (average 451,817 points per plant). In the juvenile stage, gaps and empty
spaces were observed underneath the leaves, while the stem and petioles were still visible
in the point clouds (Figure 2a,b). In the matured stage, stem and petioles disappeared from
the 3D point cloud owing to a dense canopy with an enhanced number of leaves, but gaps
and concavities were still present between the leaves (Figure 2c,d). The results obtained
with established 3D point cloud analysis are consistent with earlier studies (Table 1).
Table 1.
Descriptive statistics of estimated strawberry canopy volume using three approaches (slicing
and summing slice volume, voxel-grid, and 3D convex hull); errors are provided in terms of mean
bias error (MBE), root mean square error (RMSE), root mean square percent error (RMSPE), coefficient
of determination (R
2
) between estimated and reference volume (n= 12), and average computational
time per plant.
Approach Min
(cm3)
Max
(cm3)
SD
(cm3)
Mean
(cm3)
MBE
(cm3)
RMSE
(cm3)
RMSPE
(%) R2
Computational
Time
(s/plant)
Slicing and summing slices 43.9 97.2 13.7 71.0 −10.1 18.8 20.4 0.79 35.70
Voxel-grid 1692.0 2451.0 212.7 2134.3 2053.3 2062.3 2929.7 0.62 2534.00
3D convex hull 6064.2
17,341.9
2889.7 10,563.2 10,482.1 10,868.6 1,086,864 0.41 0.85
The approach of slicing and summing slice for volume estimation was tested on the
strawberry point cloud slice considering the combination of two parameters—maximum
point to point distance (D
max
) and slice height (S). D
max
has a key role in the proposed
Horticulturae 2022,8, 90 7 of 16
volume extraction approach to estimate an accurate canopy volume. The effect of D
max
on
area estimation is exemplarily visualized on point cloud slices of equal height considering
three D
max
values (0.15 cm, 1.0 cm, and 10.0 cm) in Figure 3. After removal of the boundary
lines larger than D
max
, the outer lines were applied to calculate the area of the polygon.
Considering all size class cases, using D
max
of 0.15 cm resulted in the lowest boundary and,
therefore, area. The use of 10.0 cm D
max
enhanced the estimated areas, whereas 0.15 cm
was visually appropriate for removing gaps and voids, but not working on randomly
appearing points.
Horticulturae 2022, 8, 90 7 of 16
was visually appropriate for removing gaps and voids, but not working on randomly ap-
pearing points.
(a) (b)
(c) (d)
Figure 3. Estimated areas from single point cloud slices with 0.1 cm height for different size classes
of strawberry plants using different D
max
(0.15 cm, 1.0 cm, and 10.0 cm): (a) juvenile-1, (b) juvenile-
2, (c) mature-1, and (d) mature-2.
The slicing and summing slice volume estimation approaches with D
max
values rang-
ing from 0.05 cm to 10.0 cm were applied on the point clouds of all size classes of straw-
berry plants, while S was still kept constant (0.1 cm) to demonstrate the effect of D
max
(Fig-
ure 4). An overestimation became apparent in the estimated canopy volume of all size
classes owing to the gradual increase of D
max
. For D
max
= 10.0 cm, the boundary line con-
nected all the outer points, including the inner spaces and gaps of the slice, resulting in
average canopy volumes of 2074 cm
3
, 3369 cm
3
, 50,768 cm
3
, and 105,360 cm
3
for juvenile-
1, juvenile-2, mature-1, and mature-2 size classes, respectively. With the smallest D
max
(0.05
cm), the lowest areas for each horizontal slices were obtained as 0.023 cm
3
, 0.025 cm
3
, 0.046
cm
3
, and 0.133 cm
3
considering average canopy volumes for juvenile-1, juvenile-2, mature-
Figure 3.
Estimated areas from single point cloud slices with 0.1 cm height for different size classes
of strawberry plants using different D
max
(0.15 cm, 1.0 cm, and 10.0 cm): (
a
) juvenile-1, (
b
) juvenile-2,
(c) mature-1, and (d) mature-2.
The slicing and summing slice volume estimation approaches with D
max
values rang-
ing from 0.05 cm to 10.0 cm were applied on the point clouds of all size classes of strawberry
plants, while Swas still kept constant (0.1 cm) to demonstrate the effect of D
max
(Figure 4).
An overestimation became apparent in the estimated canopy volume of all size classes
owing to the gradual increase of D
max
. For D
max
= 10.0 cm, the boundary line connected
Horticulturae 2022,8, 90 8 of 16
all the outer points, including the inner spaces and gaps of the slice, resulting in average
canopy volumes of 2074 cm
3
, 3369 cm
3
, 50,768 cm
3
, and 105,360 cm
3
for juvenile-1, juvenile-
2, mature-1, and mature-2 size classes, respectively. With the smallest D
max
(0.05 cm), the
lowest areas for each horizontal slices were obtained as 0.023 cm
3
, 0.025 cm
3
, 0.046 cm
3
,
and 0.133 cm
3
considering average canopy volumes for juvenile-1, juvenile-2, mature-1,
and mature-2 size classes, respectively. These volume results were certainly an underesti-
mation of the actual volume, pointing to the necessary optimisation of D
max
and Sbefore
application of the proposed volume approach.
Horticulturae 2022, 8, 90 8 of 16
1, and mature-2 size classes, respectively. These volume results were certainly an under-
estimation of the actual volume, pointing to the necessary optimisation of D
max
and S be-
fore application of the proposed volume approach.
Figure 4. Changes in strawberry canopy volume due to an increase in D
max
from 0.05 cm to 10.0 cm
when S is constant (0.1 cm) of different size classes of strawberry plant (n = 15). Logarithmic scale
was used for data visualization purposes.
To determine the optimum value of D
max
and S, reference plant (n = 12) volume data
obtained by water displacement technique were used. The proposed volume estimation
technique was applied for all reference strawberry plant point clouds using a combination
of D
max
ranging from 0.05 cm to 1.0 cm and S ranging from 0.05 to 1.0 cm applied itera-
tively. Root mean squared percent error (RMSPE) between reference volume and volume
obtained using the proposed approach showed RMSPE starting from 99.93 % at 0.05 cm
of D
max
and gradually decreasing to 26.93 % at 0.15 cm. However, RMSPE again increased
with a higher D
max
. D
max
of 1 cm produced the highest RMSPE of 1318 %. Similarly, the
RMSPE for varying S from 0.05 to 1.0 cm, with D
max
= 0.15 cm, is shown in Figure 5b.
Minimal RMSPE values were found for S ≤ 0.1 cm, whereas enhanced S resulted in higher
measuring uncertainty (Figure 5b). The computational time increased with enhanced D
max
and reduced S (Figure 5c,d).
Figure 4.
Changes in strawberry canopy volume due to an increase in D
max
from 0.05 cm to 10.0 cm
when Sis constant (0.1 cm) of different size classes of strawberry plant (n= 15). Logarithmic scale
was used for data visualization purposes.
To determine the optimum value of D
max
and S, reference plant (n= 12) volume data
obtained by water displacement technique were used. The proposed volume estimation
technique was applied for all reference strawberry plant point clouds using a combination
of D
max
ranging from 0.05 cm to 1.0 cm and Sranging from 0.05 to 1.0 cm applied iteratively.
Root mean squared percent error (RMSPE) between reference volume and volume obtained
using the proposed approach showed RMSPE starting from 99.93% at 0.05 cm of D
max
and gradually decreasing to 26.93% at 0.15 cm. However, RMSPE again increased with a
higher D
max
.D
max
of 1 cm produced the highest RMSPE of 1318%. Similarly, the RMSPE
for varying Sfrom 0.05 to 1.0 cm, with D
max
= 0.15 cm, is shown in Figure 5b. Minimal
RMSPE values were found for S
≤
0.1 cm, whereas enhanced Sresulted in higher measuring
uncertainty (Figure 5b). The computational time increased with enhanced D
max
and reduced
S(Figure 5c,d).
3.2. Comparative Analysis of Different Canopy Volume Estimation Approaches
The estimation of canopy volume of 12 reference strawberry plants was carried out
using three approaches of 3D convex hull and voxel-grid methods along with the slicing and
summing slice volume estimation approach. The comparison with manual measurement,
which was done by the water displacement technique, confirms the feasibility of the
proposed volume estimation approach with optimum parameters (D
max
= 0.15 cm and
S= 0.1 cm
), as shown in Table 1. Voxel-grid returned slightly enhanced R
2
(0.62) compared
with the 3D convex hull approach (R
2
= 0.41), because the voxel-grid approach was able to
remove the gaps and holes within the canopy. However, estimation using the voxel-grid
approach depends on the voxel-grid size and the approach requires a high computational
time. The 3D convex hull approach does not require any optimization of parameters
Horticulturae 2022,8, 90 9 of 16
and was the fastest (0.85 s/plant) among these three approaches. However, it could not
eliminate any gaps or holes within the point cloud, resulting in very high overestimation.
Horticulturae 2022, 8, 90 8 of 16
1, and mature-2 size classes, respectively. These volume results were certainly an under-
estimation of the actual volume, pointing to the necessary optimisation of D
max
and S be-
fore application of the proposed volume approach.
Figure 4. Changes in strawberry canopy volume due to an increase in D
max
from 0.05 cm to 10.0 cm
when S is constant (0.1 cm) of different size classes of strawberry plant (n = 15). Logarithmic scale
was used for data visualization purposes.
To determine the optimum value of D
max
and S, reference plant (n = 12) volume data
obtained by water displacement technique were used. The proposed volume estimation
technique was applied for all reference strawberry plant point clouds using a combination
of D
max
ranging from 0.05 cm to 1.0 cm and S ranging from 0.05 to 1.0 cm applied itera-
tively. Root mean squared percent error (RMSPE) between reference volume and volume
obtained using the proposed approach showed RMSPE starting from 99.93 % at 0.05 cm
of D
max
and gradually decreasing to 26.93 % at 0.15 cm. However, RMSPE again increased
with a higher D
max
. D
max
of 1 cm produced the highest RMSPE of 1318 %. Similarly, the
RMSPE for varying S from 0.05 to 1.0 cm, with D
max
= 0.15 cm, is shown in Figure 5b.
Minimal RMSPE values were found for S ≤ 0.1 cm, whereas enhanced S resulted in higher
measuring uncertainty (Figure 5b). The computational time increased with enhanced D
max
and reduced S (Figure 5c,d).
Horticulturae 2022, 8, 90 9 of 16
Figure 5. Changes in root mean squared percentage error (RMSPE) between reference strawberry
plant (n = 12) volume determined by water displacement technique and 3D point cloud estimated
volume using slicing and summing slice volume approach considering varying D
max
for S = 0.1 cm
(a) and varying S for D
max
= 0.15 cm (b). Computational time plotted for corresponding cases (c) and
(d). For enhancing readability, the x-axis is shown on log scale in the first two figures.
3.2. Comparative Analysis of Different Canopy Volume Estimation Approaches
The estimation of canopy volume of 12 reference strawberry plants was carried out
using three approaches of 3D convex hull and voxel-grid methods along with the slicing
and summing slice volume estimation approach. The comparison with manual measure-
ment, which was done by the water displacement technique, confirms the feasibility of
the proposed volume estimation approach with optimum parameters (D
max
= 0.15 cm and
S = 0.1 cm), as shown in Table 1. Voxel-grid returned slightly enhanced R
2
(0.62) compared
with the 3D convex hull approach (R
2
= 0.41), because the voxel-grid approach was able
to remove the gaps and holes within the canopy. However, estimation using the voxel-
grid approach depends on the voxel-grid size and the approach requires a high computa-
tional time. The 3D convex hull approach does not require any optimization of parameters
and was the fastest (0.85 s/plant) among these three approaches. However, it could not
eliminate any gaps or holes within the point cloud, resulting in very high overestimation.
Table 1. Descriptive statistics of estimated strawberry canopy volume using three approaches (slic-
ing and summing slice volume, voxel-grid, and 3D convex hull); errors are provided in terms of
mean bias error (MBE), root mean square error (RMSE), root mean square percent error (RMSPE),
coefficient of determination (R
2
) between estimated and reference volume (n = 12), and average com-
putational time per plant.
Approach Min
(cm
3
)
Max
(cm
3
)
SD
(cm
3
)
Mean
(cm
3
)
MBE
(cm
3
)
RMSE
(cm
3
)
RMSPE
(%) R
2
Computat-
Ional Time
(s/plant)
Slicing and
summing slices 43.9 97.2 13.7 71.0 −10.1 18.8 20.4 0.79 35.70
Voxel-grid 1692.0 2451.0 212.7 2134.3 2053.3 2062.3 2929.7 0.62 2534.00
3D convex hull 6064.2 17,341.9 2889.7 10,563.2 10,482.1 10,868.6 1,086,864 0.41 0.85
3.3. Summary Statistics and Correlations of Reference Plant Variables for Four Size Classes
The number of points per plant, canopy height, ground projected canopy area, and
manually measured data (Table 2) appeared normally distributed. All parameters in-
creased with enhanced plant size.
Figure 5.
Changes in root mean squared percentage error (RMSPE) between reference strawberry
plant (n= 12) volume determined by water displacement technique and 3D point cloud estimated
volume using slicing and summing slice volume approach considering varying D
max
for S= 0.1 cm
(
a
) and varying Sfor D
max
= 0.15 cm (
b
). Computational time plotted for corresponding cases (
c
) and
(d). For enhancing readability, the x-axis is shown on log scale in the first two figures.
3.3. Summary Statistics and Correlations of Reference Plant Variables for Four Size Classes
The number of points per plant, canopy height, ground projected canopy area, and
manually measured data (Table 2) appeared normally distributed. All parameters increased
with enhanced plant size.
Fresh mass, dry mass, and leaf area of plants measured in the laboratory appeared to
be correlated with LiDAR-estimated variables (Table 3). The number of points per plant 3D
point cloud showed the highest coefficient of determination (R
2
) of 0.99, 0.99, and 0.98 with
FM, DM, and leaf area, respectively, followed by other LiDAR-derived variables. However,
the leaf area estimation appears to have high measuring uncertainty owing to occlusions in
the dense canopies of mature plants.
Horticulturae 2022,8, 90 10 of 16
Table 2.
Descriptive statistics of LiDAR-estimated parameters (points per plant (PPP), height (h), and
ground projected canopy area (canopy area)) and manually measured variables (fresh mass (FM), dry
mass (DM), and leaf area (LA)) of strawberry canopies capturing four size classes (n= 15).
Descriptive
Statistics
LiDAR Estimated Variables Manually Measured Variables
PPP h
(cm)
Canopy Area
(cm2)
FM
(g)
DM
(g)
LA
(cm2)
Min 34,459 8.73 311.59 11.15 3.86 409.92
Max 658,840 52.06 4555.31 382.09 131.92 19,336.00
Mean 243,200 27.38 1905.97 127.84 44.53 6522.55
Median 84,333 17.24 768.62 37.79 11.97 1369.32
Standard deviation 226,807 15.54 1659.92 130.66 45.40 7223.58
Skewness 0.80 0.39 0.48 0.91 0.80 0.87
Kurtosis −0.82 −1.64 −1.54 −0.55 −0.78 −0.72
Table 3.
Performance of derived model from dry mass, fresh mass, and leaf area of strawberry plants
of four size classes (n= 15) with their LiDAR-estimated plant variables in terms of mean bias error
(MBE), root mean square error (RMSE), root mean square percent error (RMSPE), and coefficient of
determination (R2).
Model LiDAR-Estimated
Variables MBE RMSE RMSPE (%) R2
Fresh mass (g)
No. of points per plant −0.0006 3.37 5.44 0.99
Volume (cm3)−0.0174 10.56 37.38 0.91
Height (cm) 0.0073 8.67 30.72 0.93
Projected canopy area (cm2)0.0020 7.21 11.97 0.95
Dry mass (g)
No. of points per plant −0.0003 1.00 4.45 0.99
Volume (cm3)−0.0010 3.36 37.09 0.92
Height (cm) 0.0011 2.50 29.91 0.95
Projected canopy area (cm2)0.0002 2.09 10.87 0.97
Leaf area (cm2)
No. of points per plant 0.0010 258.03 12.84 0.98
Volume (cm3)35.1019 604.49 64.78 0.90
Height (cm) −1.3308 509.56 50.61 0.93
Projected canopy area (cm2)0.4246 370.43 22.66 0.96
3.4. Temporal Monitoring of Strawberry Canopy
3.4.1. Estimation of Leaf Area with LiDAR-Derived Canopy Variables of Juvenile Plants
Average size of leaf area found for ‘Honeoye’ was 87.69, 61.49, 93.13, 95.48, and
98.45 cm
2
at 14, 28, 50, 63, and 77 DAP, respectively. Similarly, for ‘M. Centenary’, the
average sizes of leaves recorded were 68.32, 92.31, 88.97, 94.24, and 98.93 cm
2
at 14, 28, 42,
56, and 70 DAP, respectively. The leaf size increased during plant development. Older
leaves that accumulated high biomass and leaf area were expanded, resulting in enhanced
leaf density in mature leaves compared with young leaves. To obtain the relationships
between manually measured leaf area of strawberry plants and LiDAR-derived variables,
linear regression analysis was performed. The leaf area was highly correlated to the
number of points in 3D point clouds (R
2
= 0.78). Furthermore, LiDAR-estimated canopy
volume revealed a high correlation with the leaf area. However, a reduced correlation was
observed between the LiDAR-estimated plant height and ground projected canopy area
with leaf area in juvenile plants (R
2
= 0.20 and 0.25). However, these relationships were
more pronounced when all size classes were considered (R
2
= 0.93 and 0.96), outweighing
the effect of straight upright growth of young leaves in juvenile plants and measuring
uncertainty due to dense canopies.
Horticulturae 2022,8, 90 11 of 16
3.4.2. Canopy Volume
The juvenile development of two strawberry cultivars was measured in situ with
LiDAR scanner in outdoor conditions throughout the growing season. The canopy volume
was estimated from 3D LiDAR point cloud data using the proposed volume estimation
method with D
max
0.15 cm and S= 0.10 cm according to the findings on reference plant
volume obtained by the water displacement method. Both cultivars showed a gradual
change in volume during the measurement period (Figure 6a). However, cultivar ‘Malling
Centenary’ showed a rapid increase from 14 to 28 DAP compared with cultivar ‘Honeoye’.
In the following observations, a similar progressive increase in canopy volume was found
for both cultivars. The first LiDAR scan was performed at 14 DAP, when leaves
(n= 5–7
)
were immature and standing straight upwards. Subsequently, more leaves grew and
young leaves were expanded, and the old leaves were laid down, losing their upright
habitus owing to increase in mass and loss of turgor. Consistently, the shape of plants
changed during the observation period. The average volume was increased from 35.5 cm
3
to 51.36 cm
3
for ‘Honeoye’ cultivar, whereas for the ‘Malling Centenary’, the average
volume was increased from 8.92 cm3to 42.18 cm3.
Figure 6.
(
a
) Increase in strawberry canopy volume estimated from LiDAR point cloud and (
b
) abso-
lute growth rate (cm
3
day
−1
) of two strawberry cultivars in term of plant volume over time in days
after planting (DAP).
Absolute growth rates of the strawberry plants of both cultivars were determined from
the change in plant volume (Figure 6b). It was found that, at 28 DAP, the absolute growth
rate was higher for the ‘Malling Centenary’ cultivar (1.55 cm
3
day
−1
) compared with
‘Honeoye’ (0.15 cm
3
day
−1
). However, the growth rate of ‘Malling Centenary’ declined
sharply in next 14 days. Conversely, ‘Honeoye’ cultivar showed a gradual increase in the
absolute growth rate and displayed 0.47 cm3day−1at DAP.
Time series of change in vertical volume profile were obtained when plotting the
histogram of points per plant. Data are shown for a typical sample plant from 14 to
77 DAP (Figure 7). Optimum D
max
(0.15 cm) and S(0.10 cm) found in this study (Figure 5)
provided the most realistic LiDAR-estimated volume. From the time series of vertical
volume profiles, it can be observed that the plants showed changes in volume and shape.
At 14 DAP (Figure 7a), when the plants showed 3–5 leaves, the existing leaves had long
petiole standing upwards, which was accountable for almost similar canopy height. At
28 DAP, new leaves were developing and the shape of profile changed with the upper part
producing higher volumes. Subsequently, leaf area and total canopy volume increased
until 77 DAP (Figure 7e).
Horticulturae 2022,8, 90 12 of 16
Horticulturae 2022, 8, 90 12 of 16
14 DAP (Figure 7a), when the plants showed 3–5 leaves, the existing leaves had long pet-
iole standing upwards, which was accountable for almost similar canopy height. At 28
DAP, new leaves were developing and the shape of profile changed with the upper part
producing higher volumes. Subsequently, leaf area and total canopy volume increased
until 77 DAP (Figure 7e).
Figure 7. Vertical canopy volume profiles obtained using D
max
= 0.15 cm and slight height S = 0.1 cm
considering a typical strawberry plant from time series LiDAR data acquired on 14, 28, 50, 63 and
77 days after planting (DAP) shown in (a–e), respectively.
4. Discussion
In this study, a canopy growth estimation by means of 3D LiDAR point cloud analy-
sis was proposed and evaluated on four different size classes of strawberry canopy rang-
ing from young to mature. Vegetative growth of juvenile strawberry plants was assessed
over the season in terms of adapted canopy volume estimated by LiDAR laser scanner
introducing vertical canopy volume profiling in strawberry plants.
Yamamoto and co-workers [20] used a depth camera system placed in a stationary
position while strawberry plants were placed on a movable planting bench underneath.
In the present experiment, a 2D LiDAR laser scanner was mounted on a linear, electric
conveyor to scan each individual strawberry plant with growing container from two sides
in the fruit production environment. Because of the electric engine, LiDAR movement
during scanning strawberry plants was reduced considering perturbation due to 3D rota-
tion (roll, pitch, and yaw). However, measurements were carried out in outdoor condi-
tions. While the light is supposed to be a marginal influencing factor in LiDAR measure-
ments, the wind has an impact on the data, because strawberry plants have no stiff woody
structure. Therefore, wind can cause considerable height reduction and shape defor-
mation visible in the point cloud. To overcome this challenge, extreme weather conditions
were avoided, but an influence can still be assumed.
Slicing the point cloud and estimation of individual slice volume method proposed
in this study was applied for strawberry canopy volume estimation in different plant
sizes. Xu et al. [33] proposed a tree crown volume estimation approach by slicing the point
cloud, but applying a different slice volume estimation method. As the strawberry plants
show an irregular shape and structure, the present volume estimation approach targeted
the removal of gaps. An effective approach was shown by Yan et al. [35] for estimation of
point cloud slice to calculate the canopy volume of individual trees and found the smallest
volume compared with other existing canopy volume estimation methods. In this study,
two parameters were introduced to improve the volume estimation accuracy: slicing and
adapted D
max
and S, which were calibrated by measuring the canopy volume by means of
the water displacement technique. Different S (0.05–10 cm) were tested along with varying
D
max
values ranging from 0.05 to 10 cm. The lowest RMSPE was found for 0.1 cm and 0.15
cm of S and D
max
, respectively. The resulting RMSPE was still rather high with > 20 %, but
comparatively smaller than the RMSPE found for different combinations of S and D
max
.
The computational complexity was observed in terms of average time required per plant.
Figure 7.
Vertical canopy volume profiles obtained using D
max
= 0.15 cm and slight height S= 0.1 cm
considering a typical strawberry plant from time series LiDAR data acquired on 14, 28, 50, 63 and
77 days after planting (DAP) shown in (a–e), respectively.
4. Discussion
In this study, a canopy growth estimation by means of 3D LiDAR point cloud analysis
was proposed and evaluated on four different size classes of strawberry canopy ranging
from young to mature. Vegetative growth of juvenile strawberry plants was assessed
over the season in terms of adapted canopy volume estimated by LiDAR laser scanner
introducing vertical canopy volume profiling in strawberry plants.
Yamamoto and co-workers [
20
] used a depth camera system placed in a stationary
position while strawberry plants were placed on a movable planting bench underneath.
In the present experiment, a 2D LiDAR laser scanner was mounted on a linear, electric
conveyor to scan each individual strawberry plant with growing container from two sides in
the fruit production environment. Because of the electric engine, LiDAR movement during
scanning strawberry plants was reduced considering perturbation due to 3D rotation (roll,
pitch, and yaw). However, measurements were carried out in outdoor conditions. While
the light is supposed to be a marginal influencing factor in LiDAR measurements, the
wind has an impact on the data, because strawberry plants have no stiff woody structure.
Therefore, wind can cause considerable height reduction and shape deformation visible in
the point cloud. To overcome this challenge, extreme weather conditions were avoided, but
an influence can still be assumed.
Slicing the point cloud and estimation of individual slice volume method proposed in
this study was applied for strawberry canopy volume estimation in different plant sizes.
Xu et al. [
33
] proposed a tree crown volume estimation approach by slicing the point cloud,
but applying a different slice volume estimation method. As the strawberry plants show
an irregular shape and structure, the present volume estimation approach targeted the
removal of gaps. An effective approach was shown by Yan et al. [
35
] for estimation of
point cloud slice to calculate the canopy volume of individual trees and found the smallest
volume compared with other existing canopy volume estimation methods. In this study,
two parameters were introduced to improve the volume estimation accuracy: slicing and
adapted D
max
and S, which were calibrated by measuring the canopy volume by means of
the water displacement technique. Different S(0.05–10 cm) were tested along with varying
D
max
values ranging from 0.05 to 10 cm. The lowest RMSPE was found for 0.1 cm and
0.15 cm of Sand D
max
, respectively. The resulting RMSPE was still rather high with >20%,
but comparatively smaller than the RMSPE found for different combinations of Sand D
max
.
The computational complexity was observed in terms of average time required per plant.
The computational time varied with slice height as it determined the number of iterations.
The optimum combination of parameters (0.1 cm Sand 0.15 cm D
max
) resulted in 35.70 s,
whereas computation time decreased gradually to 13.12 s (S= 0.5 cm and D
max
= 0.15 cm)
and 10.42 s (S= 1.0 cm and D
max
= 0.15 cm). The optimum combination of parameters
yielded a low RMSPE (26.93%). This sets the benchmark, because so far, hardly any studies
Horticulturae 2022,8, 90 13 of 16
exist using the actual volume of the canopy instead of comparing methods without the
actual ground truth.
Subsequently, after finding the optimum parameters of the proposed volume estima-
tion approach, two other existing tree canopy volume estimation approaches (3D convex
hull and voxel-grid) were compared. Effective removing of gaps and holes within the
canopy was important, and reducing the computational time was in consideration. A 3D
convex hull was widely used volume estimation from 3D point cloud data owing to its
faster computational time. However, it could not meet the acceptable accuracy for the
irregular-shaped 3D point cloud such as tree canopy when compared with accurate refer-
ence volume as shown in this study. Because its bounding geometrical approach included
all the holes and cavities presented in the point cloud [
34
], the voxel-grid approach resulted
in higher accuracy than the 3D convex hull approach for the strawberry point cloud. How-
ever, voxelization of 3D point cloud data required a high computational time and power,
which mostly depended on the size of the voxel grid. The smaller voxel size is able to
estimate volumes with enhanced accuracy, but it also has a high computational time cost.
Therefore, optimization of voxel size can be considered as a limitation of this approach [
48
].
Both of these approaches were applied previously for estimation of tree crown volume
in orchard or forestry where reference volume was determined by different geometrical
shape approaches. In this study, methods were compared to precise reference volume
determined by water displacement technique. In comparison, slicing and summing slice
volume estimation approach was found more accurate and required less computational
time than voxel-grid in this study.
Hosoi et al. [
30
] demonstrated the method for analyzing the vertical distribution of
volume for individual trees estimated by voxel-based volume estimation. In the present
study, time series of LiDAR 3D point cloud analysis revealed the growth patterns of ju-
venile strawberry plants based on total canopy volume of the two strawberry cultivars.
Furthermore, the time series of canopy volume profiles additionally revealed the pattern
of change in volume distribution along the vertical canopy axis (Figure 7). At first mea-
surement at 14 DAP of plantation, an average of 5–7 leaves was found, and most of them
were standing upward. Both cultivars have shown similar growth patterns considering the
straight upright leaves and petioles after planting and subsequently more mature leaves
with smaller and shorter petioles. More physiological explanation was provided by Taka-
hashi et al. [
19
]. The authors applied an RGB-depth camera, which is affected by varying
lighting conditions and, therefore, is difficult to use in a production environment. However,
the effect of temperature and amount of light received by the leaves on the strawberry plant
height was shown. With the present benchmark of volume estimation, a methodology is
available for further physiological analysis and support of variable rate plant protection
considering the volume profile of the canopies (Figure 7).
LiDAR point cloud derived plant geometrical parameters were correlated to other
destructively measured variables. Strong linear relationships were observed for the number
of points per plant with dry mass (R
2
= 0.99) and fresh mass (R
2
= 0.99) of plants. LiDAR
derived plant volume also demonstrated good linear relationships for dry mass (R
2
= 0.92)
and fresh mass (R
2
= 0.90). Walter el al. [
49
] found high correlations between above ground
biomass and LiDAR estimated volume up to r= 0.86 for wheat. Greaves et al. [
50
] also
revealed a strong correlation (R
2
= 0.92) between harvested biomass and LiDAR-estimated
volume for arctic shrubs using terrestrial laser scanning in a close range (2 m). However,
plant height and ground projected canopy area exhibited weaker linear relationships with
dry mass and fresh mass of plants. Guan et al. [
21
] tested the linear relationship of similar
parameters, although he used an RGB image to reconstruct point cloud using a structure
from motion algorithm. With this approach, R
2
= 0.77 was found for canopy volume and
dry mass. For LA observation, Guan et al. [
21
] found R
2
= 0.76 between canopy volume and
LA, whereas the current results showed R
2
= 0.97 when all size classes of strawberry plants
were considered. As a result, LiDAR point cloud derived strawberry plant parameters
could be utilized to model the strawberry plant growth. The introduction of the method in
Horticulturae 2022,8, 90 14 of 16
physiological studies or applications in variable rate management should be investigated
in future studies.
5. Conclusions
In this study, the 2D LiDAR laser scanner was mounted on a mobile linear conveyor to
scan commercial strawberry plants. This type of linear conveyor mounted 2D LiDAR laser
scanner can be further applied in greenhouse or greenhouse crop monitoring, providing
3D dataset of the plants.
A volume estimation technique was proposed based on slicing and summing slice
volume from 3D strawberry point cloud. The approach enabled the removal of holes and
gaps, resulting in R
2
= 0.80 with a low computational time. Moreover, this approach was
capable of extracting volumes of different horizontal layers of canopy, which can generate
the vertical canopy volume profile of strawberry plant. Analysis of time series of 3D
point cloud data revealed the typical growing pattern of strawberry plants in the vertical
canopy profiles.
The relationship between various LiDAR point cloud derived parameters with destruc-
tively measured biomass and geometry was confirmed in this study. In conclusion, LiDAR
laser scanners mounted on near-ground linear conveyor are a feasible tool for monitoring
strawberry plant growth.
Author Contributions:
Conceptualization: K.K.S. and M.Z.-S.; experimental plan: K.K.S. and M.Z.-S.;
methodology: K.K.S. and M.Z.-S.; data collection: K.K.S.; software and program coding: K.K.S. and
N.T.; data analysis: K.K.S.; writing—original draft: K.K.S. and M.Z.-S.; supervision: M.Z.-S. and C.W.;
writing—review and editing: K.K.S., N.T., C.W. and M.Z.-S. All authors have read and agreed to the
published version of the manuscript.
Funding:
The publication of this article was funded by the Open Access Fund of the Leibniz Association.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
The data of this study are available from the authors upon reason-
able request.
Acknowledgments:
This work was a part of Ph.D. research of the first author, financially supported
by the Bangladesh Agriculture Research Council, Ministry of Agriculture, Bangladesh (PIU BARC,
NATP Phase-II Project). We would like to express our gratitude to ATB authority for providing
laboratory facilities and technical manpower to conduct experiments.
Conflicts of Interest:
The authors declare that they have no conflict of interest regarding this research
work or its publication.
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