remote sensing
ArticleA UAV-Based Single-Lens Stereoscopic Photography Method for
Phenotyping the Architecture Traits of Orchard Trees
Wenli Zhang 1 , Xinyu Peng 1 , Tingting Bai 1 , Haozhou Wang 2 , Daisuke Takata 3 and Wei Guo 2, *
1

2

3

*

Citation: Zhang, W.; Peng, X.; Bai, T.;
Wang, H.; Takata, D.; Guo, W.
A UAV-Based Single-Lens
Stereoscopic Photography Method for
Phenotyping the Architecture Traits of
Orchard Trees. Remote Sens. 2024, 16,
1570. https://doi.org/10.3390/

Information Department, Beijing University of Technology, Beijing 100022, China;zhangwenli@bjut.edu.cn (W.Z.); pengxy@emails.bjut.edu.cn (X.P.); baitingting@emails.bjut.edu.cn (T.B.)
Graduate School of Agricultural and Life Sciences, The University of Tokyo, Tokyo 188-0002, Japan;
haozhou-wang@g.ecc.u-tokyo.ac.jp
Faculty of Food and Agricultural Sciences, Fukushima University, Fukushima 960-1296, Japan;
dtakata@agri.fukushima-u.ac.jp
Correspondence: guowei@g.ecc.u-tokyo.ac.jp

Abstract: This article addresses the challenges of measuring the 3D architecture traits, such asheight and volume, of fruit tree canopies, constituting information that is essential for assessing
tree growth and informing orchard management. The traditional methods are time-consuming,
prompting the need for efficient alternatives. Recent advancements in unmanned aerial vehicle(UAV) technology, particularly using Light Detection and Ranging (LiDAR) and RGB cameras, haveemerged as promising solutions. LiDAR offers precise 3D data but is costly and computationallyintensive. RGB and photogrammetry techniques like Structure from Motion and Multi-View Stereo(SfM-MVS) can be a cost-effective alternative to LiDAR, but the computational demands still exist.
This paper introduces an innovative approach using UAV-based single-lens stereoscopic photography
to overcome these limitations. This method utilizes color variations in canopies and a dual-imageinput network to generate a detailed canopy height map (CHM). Additionally, a block structure
similarity method is presented to enhance height estimation accuracy in single-lens UAV photography.
As a result, the average rates of growth in canopy height (CH), canopy volume (CV), canopy width(CW), and canopy project area (CPA) were 3.296%, 9.067%, 2.772%, and 5.541%, respectively. The
r2 values of CH, CV, CW, and CPA were 0.9039, 0.9081, 0.9228, and 0.9303, respectively. In addition,compared to the commonly used SFM-MVS approach, the proposed method reduces the time cost of
canopy reconstruction by 95.2% and of the cost of images needed for canopy reconstruction by 88.2%.
This approach allows growers and researchers to utilize UAV-based approaches in actual orchard
environments without incurring high computation costs.

rs16091570
Academic Editors: Andrea Garzelli,

Keywords: aerial photogrammetry; fruit tree canopy structure; cost-efficient 3D trait extraction

Jian Yao, Wei Zhang, Li Li and
Claudia Zoppetti
Received: 23 February 2024

1. Introduction

Revised: 18 April 2024

The canopy architecture traits of fruit trees, such as height, shape, arrangement, andvolume, offer critical insights into a tree’s growth status and potential yield [1–3] andplay a pivotal role in guiding management decisions. These decisions span a spectrumof tasks, including pruning, irrigation, fertilization strategies [4], and pesticide application plans [5]. However, the task of measuring such tree canopy architecture traits canbe laborious and time-consuming, particularly considering the expansion of production
areas in recent decades. Although simplified measurement methods have significantly
contributed to various aspects of fruit production, there have been instances where theiraccuracy falls short in practical scenarios. Consequently, there is an imperative need toadvance research into precision orchard management to obtain architecture traits about
fruit tree canopies swiftly, accurately, and cost-effectively. This approach aims to cater tothe unique characteristics of individual trees, ultimately enhancing production, ensuringhigh-quality yields, and promoting environmental conservation.

Accepted: 21 April 2024
Published: 28 April 2024

Copyright: © 2024 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms andconditions of the Creative Commons
Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

Remote Sens. 2024, 16, 1570. https://doi.org/10.3390/rs16091570

https://www.mdpi.com/journal/remotesensing

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With the recent development of low-altitude unmanned aerial vehicle (UAV) technology, the application of UAVs in the field of agriculture is becoming increasingly
widespread [6–8]. With the advantages of low-altitude flight and fast maneuverability,
UAVs can acquire higher-quality raw data in less time. This capability enables accurate
measurement of the canopy structures of fruit trees in large-area scenarios.
Mounted on UAV platforms, Light Detection and Ranging (LiDAR) sensors [9–12]
and RGB cameras [13–19] are utilized to acquire the architecture traits of fruit tree canopies.
Among the two, LiDAR scanning is the most direct and precise way of obtaining 3D point
cloud data of fruit tree canopies. Slavík et al. [9] used a UAV-mounted VUX-1UAV laser
scanner to ascertain the canopy structure of spruce trees and differentiate the survival or
non-survival of these spruce trees based on this canopy structure. Hyyppä et al. [10] useda UAV-LS-RiCOP UAV laser-scanning system to acquire point cloud data of birch forests,
based on which the canopy heights of birch trees were calculated. Chisholm et al. [11]used a UAV equipped with a UTM-30LX radar to acquire a point cloud of jatropha trees
and detected and measured the diameter at breast height (DBH) of the trees using a fitted
cylinder method. Ghanbari et al. [12] used an airborne Phoenix ALPHA LiDAR sensor
to acquire canopy data. Point clouds of eucalyptus, Ficus macrocarpa, and elm trees areused to calculate structural parameters, including canopy width and volume. However, thesubstantial cost of LiDAR devices and the computational workload involved in collecting
and analyzing canopy point cloud data pose significant challenges in measuring fruit tree
canopies, particularly in large-scale orchard settings.
To lower the device costs of acquiring 3D canopy architecture traits, numerous studies
have integrated photogrammetry techniques such as Structure from Motion and Multi-View
Stereo (SfM-MVS) [13–18]. Sun et al. [13] used a UAV to capture image sequences of apple
orchards and generate a 3D point cloud model of an orchard to establish a prediction model
linking the morphological characteristics of fruit tree canopies to apple yields. Similarly,
Mu et al. [14] generated a digital surface model (DSM) using photogrammetry for thedetection of peach tree canopy growth dynamics. Hao et al. [15] reconstructed canopy
point cloud models of Laurel and Ficus to calculate tree heights. Nasiri et al. [16] obtained
a point cloud model of a mountainous deciduous mixed forest during the leaf-opening
and leaf-declining seasons to calculate the canopy height and canopy diameter on the
point cloud model. Krause et al. [17] reconstructed a canopy height model of a Scottish
pine forest to measure the heights of individual trees. Hobart et al. [18] reconstructed apoint cloud model of the canopy part of an apple tree wall by using a low-flying UAV
designed to monitor its growth status. The abovementioned studies demonstrated thefeasibility of using photogrammetry for acquiring information on tree canopy structures.
While the photogrammetry approach reduces hardware costs compared to those incurred
when using LiDAR, it still necessitates handling a large number of images and involves
high computation costs for the 3D reconstruction process.
Along with the abovementioned SfM-MVS approach, binocular stereovision, in which
a simpler binocular parallax principle is utilized to calculate depth information, has been
considered as a solution for reducing the computational cost of acquiring 3D structural
data [19–21]. However, most of the tasks are performed in short-range photography
scenarios; in long-range photography scenarios, such as aerial imagery, a long baseline
binocular system is required, and such a system is not feasible for hardware design.
To overcome this limitation, a UAV-based single-lens stereoscopic photography method
has been designed: Matsuura et al. [22] simulated a long-baseline binocular photographysystem by using a UAV to take two overhead photographs with the help of the RTK-GNSS
positioning system. It can only obtain the height information of buckwheat plants, but
provides an idea for the detailed canopy measurement task in this study.
Therefore, we propose a fruit tree canopy architecture trait measurement method
based on a single UAV lens. It can comprehensively obtain information on fruit tree canopy
height, canopy volume, canopy width, and canopy projected area with low cost and highaccuracy for outdoor scenes. The main contributions of this study are as follows:Remote Sens. 2024, 16, x FOR PEER REVIEW

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canopy height, canopy volume, canopy width, and canopy projected area with low3costof 27
and high accuracy for outdoor scenes. The main contributions of this study are as follows:Firstly, to obtain fruit tree canopy heights using single-lens UAV photography, weleveraged
thetodistinct
in heights
fruit treeusing
canopies
resultingfromlighting condiFirstly,obtain color
fruit variations
tree canopy
single-lensUAVphotography,
wetions,
canopystructure,
texture,and
UAV
photography
angles.
We
introduce
novel dualleveraged the distinct color variations in fruit tree canopies resulting from alighting
conimage-input
canopyheighttexture,
calculationto account angles.
for theseWe
characteristics.
This
ditions, canopy
structure,and network
UAV photography
introduce a novel
networkharnesses
both
color
information
fromacanopyforeground
RGB(CFM-RGB)
dual-image-input canopy height calculation network to account for these characteristics.
mapand height
informationfrom informationa canopy-height-estimated(CHM-estimated)
map. By
This network
harnesses
both color
from a canopy foreground
RGB (CFM-RGB)
combiningthese information
inputs, this can
network
eﬀectively learn the height
distribution patterns
map and heightfroma canopy-height-estimated(CHM-estimated)
map. Byofrealcanopies,
leadingtothegenerationofarefinedcanopyheightmap(CHM).
In thiscombining these inputs, this can network effectively learn the height distribution patterns
context,
wedefine
the
CHM
as
a
map
that
accurately
represents
theforegroundheightsof real canopies, leading to the generation of a refined canopy height map (CHM). In thisofa canopy.
This paper
also asintroduces
a canopyheightdistribution
supervision
mechacontext,
we define
the CHMa map that
accurately
represents
the foregroundheights
of
nism,
which
serves
as
a
means
of
assessingtheappropriatenessofthecomputed
CHM’s
a canopy. This paper also introduces a canopy height distribution supervision mechanism,heightvalue distribution
to assessing
ensure that
final CHM is finely
andshowsheighta rawhich serves
as a means ofthetheappropriatenessof the detailed
computed
CHM’stionaldistribution.
Secondly,
improve
theof canopy
value height
distribution
to ensurethat thetofinal
CHM
is accuracy
finely detailed
andheight
showsestimationa rational
undersingle-lensphotography
conditions,
we introduce
a novelthat under
reliesheightUAV
distribution.
Secondly,
to improve
the accuracyof canopy
heightmethod
estimationonblock
structuresimilarity.
In
thisapproach,areasimilarity
is
used
as
a
metricfor
feaUAV single-lens photography conditions, we introduce a novel method that relies on blockture
matching,
resultinginanotablereduction
in
the
time
requiredforfeaturematching
structure similarity. In this approach, area similarity is used as a metric for feature matching,
betweencapturedby a single
UAVrequiredlens andforimproving
the accuracyof canopy
resultingimages
in a notablereductionin the timefeature matching
betweenimagesheightestimation.
Our
approach
represents
a valuable
contribution,
particularly
for scecaptured
by a single
UAV
lens and
improving
the accuracyof canopy
height estimation.
Our approach
representsUAV
a valuable
contribution,
particularly
for scenarios
where
singlenarios
where single-lensphotography
is a limiting
factor, andit promises
to enhancelensoverall
UAV photography
is a limiting
and itprocedures.
promises to enhance the overall quality ofthequality of canopy
heightfactor,
estimation
canopy height estimation procedures.
2. Materials and Methods
2. Materials and Methods
2.1. Experiment Field and Data Acquisition
2.1. Experiment Field and Data Acquisition
Twelve peach trees with a complex canopy structure in a peach orchard in Fukushima
Twelve peach trees with a complex canopy structure in a peach orchard in Fukushima
prefecture, Japan (37°50′15.0″N140°31′18.5″E), were selected in this experiment (Figure 1).
prefecture, Japan (37◦ 50′ 15.0′′ N 140◦ 31′ 18.5′′ E), were selected in this experiment (Figure 1).
During the fruit-ripening stage in August 2019, aerial image data were collected using aDuring the fruit-ripening stage in August 2019, aerial image data were collected using a
low-cost drone (DJI Phantom 4 RTK, DJI, Shenzhen, China) equipped with an airborne
low-cost drone (DJI Phantom 4 RTK, DJI, Shenzhen, China) equipped with an airborne
RGB camera. This camera has a lens field of view (FOV) of 84 degrees and a focal length
RGB camera. This camera has a lens field of view (FOV) of 84 degrees and a focal length ofof 8.8 mm. It is capable of capturing images with a maximum resolution of 5472 × 3648
8.8 mm. It is capable of capturing images with a maximum resolution of 5472 × 3648 pixels.
pixels. The flight height was set to 30 m, resulting in a ground resolution of 0.82 cm/pixel.
The flight height was set to 30 m, resulting in a ground resolution of 0.82 cm/pixel.

Figure 1.
1. The
The experimentexperiment field
field inin Fukushima
Fukushima prefecture.
prefecture.
FigureSimultaneously, we captured a 3D point cloud of the orchard using Topcon GLS-2000
laser scanner(TOPCON, Tokyo, Japan) on the same day of the flight. The geographic
information was also added by setting up control points (measured using RTK GPS) andRemote Sens. 2024, 16, x FOR PEER REVIEW

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Simultaneously, we captured a 3D point cloud of the orchard using Topcon GLS-2000
4 of27
laser scanner(TOPCON, Tokyo, Japan) on the same day of the flight. The geographic
information was also added by setting up control points (measured using RTK GPS) andthen placing TLSs on the tops of these control points during the scanning process. So, thethen placing
TLSs theon thetopsof thesecontrol points during the scanning process. So, thepointclouds from
TLSs
were
also georeferenced.
point clouds from the TLSs were also georeferenced.
2.2. Algorithm and Methodology
2.2. Algorithm and Methodology
This section mainly describes the method used in this study. Figure 2 illustrates theThis section mainly describes the method used in this study. Figure 2 illustrates thefunctional structure of the method, which consists of the following components.
functional structure of the method, which consists of the following components.

Figure 2.
2. General
Generalframework
frameworkof
ofthe
themethodology
methodologyof
ofthisthisstudy.
study. (a)(a) Canopy
Canopyforegroundforeground RGB
RGB map
map
Figure(CFM-RGB). (b)(b) Canopy-height-estimated
Canopy-height-estimated map
map (CHM-estimated).
(CHM-estimated). (c)(c) Canopy
Canopy heightheight map
map (CHM).
(CHM).
(CFM-RGB).

(1) Image-Preprocessing Module: In order to minimize the impact of ground back(1) Image-Preprocessing Module: In order to minimize the impact of ground background information on the estimation of canopy height, this module removes the background information on the estimation of canopy height, this module removes the background information in aerial images and generates a standard binocular canopy foregroundground information in aerial images and generates a standard binocular canopy foreRGB map (CFM-RGB) of the fruit tree target. This facilitates the subsequent process ofground RGB map (CFM-RGB) of the fruit tree target. This facilitates the subsequent procanopy height estimation. The details of this process are described in Appendix A.1.1.
cess of canopy height estimation. The details of this process are described in Appendix(2) Canopy Height Estimation Module. This module utilizes the canopy height
A.1.1.
estimation method proposed in this paper, which is based on block structural similarity. It(2) Canopy Height Estimation Module. This module utilizes the canopy height estiperforms matching of corresponding areas between binocular CFM-RGB maps using block
mation method proposed in this paper, which is based on block structural similarity. It
similarity metrics and calculates the binocular parallax to obtain a canopy-height-estimatedperforms matching of corresponding areas between binocular CFM-RGB maps usingmap (CHM-estimated) (corresponding to Section 2.2.1).
block(3)similarity
calculatesModule
the binocular
to obtain abetween
canopy-heightCanopymetrics
HeightandCalculation
based parallax
on the correlation
canopyestimated(CHM-estimated)(corresponding
Section
2.2.1).
color and mapheightdistribution: This
module uses tothecanopyheight calculation network(3) Canopy
HeightModuleonIt thecorrelation
betweencanopyproposed
to calculate
theCalculation
Canopy Height
Map based(CHM).
takes
CHM-estimatedand
CFMcolorandheightdistribution:This
moduleuses
thecanopyheightcalculation
network
RGB as inputs and uses the canopy foreground RGB information to guide the calculation
proposed
to calculate
CanopyHeightMap (CHM).
It takes CHM-estimated
CFMof CHM. The
modulethethenoutputs
the refined
CHM (Corresponding
to Sectionand2.2.2).
RGB asinputsanduses
thecanopyforeground
RGB
information
to
guide
the
calculation(4) Canopy structure reconstruction module: In the task of simulating long baselineofCHM. The
modulethen outputs
the refined
CHM(Corresponding
Section
2.2.2).
binocular
canopystructuremeasurements
using
a single
UAV lens, theto3D
structureof the(4)
Canopystructurereconstruction
module:
In
the
task
of
simulating
longbaseline
canopy is obtained from the refined binocular CHM via the canopy structure reconstruction
binocular
canopystructuremeasurementsa single UAV
module. This
moduleis describedin detailusing
in Appendix
A.1.2.lens, the 3D structure of the
canopyisobtainedfromtherefinedbinocular
CHM
via
the
canopyandstructurereconstrucThis paper focuses on the canopy height estimation module
the canopy
heighttion
module.
This
moduleisdescribed
indetailinAppendix
A.1.2.
calculation module within the overall structure of this methodology.

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This paper focuses on the canopy height estimation module and the canopy height
5 of 27
calculation module within the overall structure of this methodology.
2.2.1. Canopy Height Estimation Module
2.2.1.Since
CanopyEstimation
ModulethereHeightis a strong
correlation
between the calculability of canopy height and thethere
is ainput
strongcanopy
correlation
betweenthe calculabilityof canopy
heightandthetheimageSince
qualityof theheightestimation
module proposed
in Section
2.2.2,
image qualityofofthe
theinputproposed
canopy height
proposedin Section
2.2.2,and
theperformancemodel
in thisestimation
paper can module
be further
enhanced
if the speed
performanceof the modelin canthis bepaper
can be further
enhanced
if the speed
andaccuracyof canopy
heightproposed
estimation
improvedin the CHM
estimation
process.
accuracy of
canopy height
estimation
canestimation
be improvedin the CHM
estimation
However,conventional
binocular
depth
algorithms
typically
rely onprocess.
pixelHowever,conventional
binocular
depth estimation
algorithms
typically
relynumber
on pixel-point
point
matching[23,24]. Thesealgorithms
often require
generatinga largerof feamatchingalgorithms
oftenprocess,requireresulting
generatinglarger numberof infeatureture
points[23,24].
for the These
subsequent
matching
in areduced
eﬃciency
estipoints
forthesubsequent
matching
process,
resultingin
reducedefficiency
inestimating
mating canopy height. Additionally, these algorithms are highly sensitive to variations in
canopyandheight.
Additionally,
these
algorithms
highly number
sensitiveoftofalse
variationsin texture
texturelighting
conditions,leading
to an areincreased
matchesin theand lightingofconditions,leadingan increased
number of thisfalsenegatively
matches inimpacts
the foregroundforegroundcomplex fruit
treetocanopies.
Consequently,
the acof complex
fruit heighttree canopies.
Consequently, this negatively impacts the accuracy of
curacyof canopy
estimation.
canopyheightestimation.
In this paper, we observe that in binocular photographic images with long baselines,
In thispaper,
we canopy
observeshowsthat inhighbinocular
photographic
withbaselines,
the sameregionin thestructuralsimilarityimages
betweenthe longbinocular
imthe
same
regioninthecanopyshows
highstructuralsimilarity
betweenthebinocularages (as depicted in Figure 3). However, the canopy texture details of fruit trees are more
images (asdepictedin experimental
Figure 3). However,the canopy
texture details
areintricate.
Basedon thisphenomenon,
we hypothesizethat ofwefruitcan trees
use themore intricate. Basedon this experimental
phenomenon,
we hypothesizethat weuse
high-dimensional
information
similarity between
canopy structures
as a metric
forcanbinocthehigh-dimensional
information
similarity
betweencanopystructuresasametricular matching. Therefore, based on this concept, the canopy height estimation module forofbinocular
basedon this concept,
the canopy
heightestimation
modulethispaper matching.
provides aTherefore,
method for
estimating
canopy heightbasedon thestructural
simiof thisofpaper
methodestimating
canopy heightbased ontraits
the structural
larityblocks.provides
The corea idea
is toforextract
high-dimensionalarchitecturefrom thesimilarity
ofblocks.
The
core
idea
is
to
extract
high-dimensionalarchitecture
traitsfrom thebinocular canopy blocks using a down-sampling strategy. This allows for the comparison
binocular canopy blocks using a down-sampling strategy. This allows for the comparisonof structural similarity. The specifics of this approach are as follows:of structural similarity. The specifics of this approach are as follows:

Figure3.3.Higher
Highersimilarity
similaritybetween
betweenbinocularbinocularimages
imagesfor
forthethesamesameregion
regionofofthe
thecanopy
canopyandandlowerlower
Figuresimilarity
similaritybetween
betweendiﬀerent
differentregions
regionsinin thethe binocularbinocular images.
images.

In the process of canopy block matching, the accuracy of the similarity between blocks
directly affects the calculation of subsequent block height values. This method is designed
to optimize the utilization of the structural characteristics of canopy blocks in order todetermine the similarity between two blocks. To achieve this, the two block images areRemote Sens. 2024, 16, 1570

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initially down-sampled before the similarity of the blocks is computed [this process is
illustrated in Figure 4a and Algorithm 1 outlined in the pseudo-code]. Subsequently, thehigh-dimensional features of the two blocks are extracted, and their similarity is calculated
based on the high-dimensional feature maps. This approach enhances the significanceof the canopy’s structural features in determining the structural similarity of the blocks
while reducing the impact of canopy details on the structural similarity. Then, the distance
between the high-dimensional feature maps of the two blocks is calculated. (In this paper,
the cosine distance was chosen as the measure of similarity.) The smaller the cosine
distance between the two high-dimensional feature maps, the higher the similarity in termsof canopy structure between them. The formula for determining the cosine distance isshown in Equation (1):∑im=1 ∑nj=1 MLi,j MRi,jq∑i=1 ∑nj=1 ML2i,j ∑im=1 ∑nj=1 MR2i,jdcos ( ML, MR) = 1 − q m(1)where ML and MR are the high-dimensional feature maps of the left and right visual
canopy blocks whose similarity is to be calculated by Algorithm 1, and their pixel matrices
are MLm×n and MRm×n , respectively.
Algorithm 1. Similarity calculation process for binocular canopy block matching
Input: left eye block: BL, matched right eye block: BR
Output: similarity between canopy blocks: si
1: for i ← 1 to 5 do
2:BL ← convolutional_downsampling (BL, kernel size: 3×, step size: 1)
3:BR ← convolutional_downsampling (BR, kernel size: 3×3, step size: 1)
4: end for
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5: ML, MR ← high-dimensional feature map of BL, BR, respectively.
6: si = dcos (ML, MR), the cosine distance between ML and MR by Equation (1)7 of 29

Figure4.4.Architecture
Architecture ofof thethe canopy
based
onon
block
structuresimilarity.
Figurecanopy heightheightestimation
estimationmethod
methodbased
block
structuresimilarity.
(a) Process of calculating binocular block similarity. (b) Process of calculating canopy height h at a(a) Process of calculating binocular block similarity. (b) Process of calculating canopy height h atblock.
a block.

In order to obtain the left-eye canopy foreground height matrix M, traverse all of theleft-eye CFM-RGB blocks, use the above matching method to identify the best matchingregion, and calculate the height value. Next, eliminate any outliers from M, and note themaximum value, or hmax, in M. Finally, normalize the matrix M to a grayscale image with
0–255 levels, keeping only the canopy foreground region in the grayscale image [illus-

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Using the coronal block structure similarity measure mentioned above, divide thecoronal foreground region into blocks (referred to as blocks in this paper). Then, in theother binocular image, find the most similar block by using each block of the coronal
structure as a matching block (referred to as a matched block in this paper). Computethe centroid of the two matching blocks [as indicated by the coordinates (xb , yb ) and (xm ,ym ) shown in Figure 4] in the parallax between binoculars. Because the UAV used in thisstudy is integrated with an RTK module, assume that the flight altitude will not change
drastically during the acquisition of the left and right views (as shown in Figure 4, forexample). Then, based on the information from the photographic images, use the stereo
vision principle to calculate the height value at the block [as demonstrated in Figure 4b].
Because of the typical binocular form correction that the preceding binocular canopy
foreground-generating module already executed, only the region containing the upperand lower borders of the block area (as depicted in the bar area in Figure 4) can be used tosearch for the matching block.
In order to obtain the left-eye canopy foreground height matrix M, traverse all of theleft-eye CFM-RGB blocks, use the above matching method to identify the best matchingregion, and calculate the height value. Next, eliminate any outliers from M, and note themaximum value, or hmax , in M. Finally, normalize the matrix M to a grayscale image with
0–255 levels, keeping only the canopy foreground region in the grayscale image [illustrated
in Figure 5c]. To generate the right-eye CHM-estimated map, swap the left- and right-eye
CFM-RGB maps and apply the previously described canopy height estimation method
based on block structure similarity. Figure 5 displays the CFM-RGB map, the canopy
foreground block division image, and the CHM-estimated map. According to the CHMestimated map, the total height of the canopy in the area is represented by the height valueof each block in the picture. In addition to serving as the input object for the following
module that calculates canopy height, the CHM-estimated map also provides canopy heightvalue guidance for the network process used to calculate canopy height in the foreground.
This allows the network to incorporate RGB information from the foreground to calculate
the heights of the canopy at each location more precisely. In addition, the gray values in
the CHM-estimated map are records of the relative heights of each canopy segment. 8These
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records serve as the vertical mapping scale for the subsequent reconstruction of the canopy,
ensuring that the new canopy has a reasonable 3D scale.

Figure 5.
5. CFM-RGB
Figure
CFM-RGB (a),(a), block
block delineation
delineation image
image ofof canopy
canopy foregroundforeground area
area (b),(b), andand CHM-estimatedCHM-estimatedmap (c).
map (c).

2.2.2. Canopy Height Calculation Module
Fruit tree canopy height calculation requires not only the generation of a reasonablecanopy heightheight distribution
distributionbut
butalsoalsothethecertainty
certaintyofofthe
theinvarianceinvariancecontourshape
ofof
thethecontourshapeofof
canopyforeground
since
the3D
3Dstructure
structureofofthe
thecanopy
canopy needs
needs toto be reconstructed
thethecanopyforeground
since
the
from the computed fine CHM. In light of this, this paper suggests a network for calculating the foreground height of fruit trees using the correlation between canopy color andheight distribution. Figure 6 displays the training flowchart for this network model. The
network uses a generative adversarial network structure. Here, the “generator networkwith dual input images” is utilized to calculate canopy height. The generator takes two

Remote Sens. 2024, 16, 1570

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from the computed fine CHM. In light of this, this paper suggests a network for calculating
the foreground height of fruit trees using the correlation between canopy color and height
distribution. Figure 6 displays the training flowchart for this network model. The network
uses a generative adversarial network structure. Here, the “generator network with dual
input images” is utilized to calculate canopy height. The generator takes two inputs,
CFM-RGB and CHM-estimated, which supply the RGB color information of the canopy
foreground and the in-region predicted height values of the canopy, respectively. The
generator network combines the canopy foreground color from CFM-RGB and the canopy
height estimation information from CHM-estimated, from which it learns the deep mapping
relationship between the canopy RGB information and the estimated height information
for the real canopy height, generates a refined CHM image with reasonable canopy height
distribution characteristics (introduced in the Generator Network with Dual Input Images
Section), and outputs a refined CHM with the canopy heights. The loss function of canopy
height distribution proposed in this paper (introduced in the Loss Function for Canopy
Height Distribution Section) is used in the calculation process to accelerate the convergence
speed of the generator network and enhance the reasonableness of the height distributionof the generated CHM; meanwhile, the pixel-by-pixel loss function is used to ensure theinvariance of the two-dimensional contour structure of the computed CHM. In addition,the ‘discriminator network’ learns how to distinguish between the generated CHM andthe label CHM and improves the ability of the ‘generator network’ by using dual input
images to calculate the canopy height during the adversarial training of the generator
and discriminator. Such a network structure meets the needs of fruit tree canopy height
Remote Sens. 2024, 16, x FOR PEER REVIEW
9 of 29
calculation. To train the canopy height calculation network, the label CHM was generated
by projecting terrestrial-laser-scanner-acquired point cloud data.

Figure 6. Flowchart of training
training network
networkused
usedforforcalculating
calculatingforegroundforegroundheightheightofoffruit
fruittree
treecanopies.
canopies.

Generator Network with Dual Input Images

Generator
Network with
Dual
Input Images
The network’s
specific
structureis shown in Figure 7. The generator is based on
U-Net,
with
CFM-RGBandCHM-estimated
as in
dual
inputsand generator
the computed
fine on
CHM
The network’s specific structure is shown
Figure7. Theis basedUas
theoutput,
the
last
of
which
is
designedtoutilizethecanopyforeground
RGB
inforNet, with CFM-RGB and CHM-estimated as dual inputs and the computed fine CHM
asmationin CFM-RGB
and theispredicted
canopyinformationin CHM-estimatedtotheoutput,the last of which
designed toutilizeheightthe canopy
foreground
RGB information
guide
the
generator
network
towards
learning
the
label
CHM’s
canopyheightdistribution
in CFM-RGB and the predicted canopy height information in CHM-estimated to guidecharacteristics
to generate
a finelearningand reasonably
CHM.
thegenerator network
towards
the labeldistributed
CHM’s canopy
height distribution characteristics to generate a fine and reasonably distributed CHM.
In the U-Net down-sampling process (Figure 7), a continuous convolutional layer
module is used to extract the input CFM-RGB and CHM-estimated features at the sametime and fuse the RGB information of the canopy layer and the height information of the
canopy layer during the down-sampling process to obtain the high-dimensional features

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9 of 27

Figurecanopy heightheight calculation
calculationnetwork.
network.
Figure 7.
7. Generator structure of the canopy

the U-Net
down-sampling
process (Figure 7), a continuous convolutional layer
LossInFunctionfor Canopy
Height Distribution
module is used to extract the input CFM-RGB and CHM-estimated features at the same
The generator uses a pixel-by-pixel 𝐿 loss function to ensure the invariance of thetime and fuse the RGB information of the canopy layer and the height information of the
2D contour structure of the generated CHM, but in the actual training process, only using
canopy layer during the down-sampling process to obtain the high-dimensional features
this loss will render the network incapable of learning the characteristics of canopy heightthat are enriched with the color and height information of the canopy layer; the inverse
distribution, which will lead to problems such as slow convergence of the generator netconvolution module is used to reconstruct the image step-by-step in the up-sampling
work, unreasonable generation of canopy height distribution, and other problems. Thereprocess, and the skip-connection structure is used in this process to jump-connect the downfore, this paper proposes, for the first time, the canopy height distribution loss function
sampling feature layers of the same scale, rendering the image restoration process capable
lossQD (quantity and distribution loss of canopy height), which evaluates the similarity ofof fusing the feature information of the canopy layer’s color tone and outline at different
the distribution of height values between the generated CHM and the label CHM andscales, in addition to enhancing the quality of the reconstruction of the image.
makes the distribution of canopy heights generated by the generator network more reasonable.
This loss
consists ofcanopyheight value quantity distribution loss, i.e., lossquality,
Loss
Functionfor Canopy
HeightDistribution
and canopy height value location distribution loss, that is, lossdistribution, and it is defined as
Theingenerator
uses a pixel-by-pixel L1 loss function to ensure the invariance of theshown
Equation (2):
2D contour structure of the generated CHM, but in the actual training process, only using= 𝑙𝑜𝑠𝑠 of learning+ 𝑙𝑜𝑠𝑠the characteristics of canopy height𝑙𝑜𝑠𝑠 incapable
this loss will render the network(2)
distribution, which will lead to problems such as slow convergence of the generator
where lossquality is calculated by counting the number of height values in the canopy renetwork, unreasonable generation of canopy height distribution, and other problems.
gion, focusing on the part of the generated CHM and the label CHM in the foreground
Therefore, this paper proposes, for the first time, the canopy height distribution loss
region of the canopy only [shown in Figure 8a], and counting the number of height values
function lossQD (quantity and distribution loss of canopy height), which evaluates theof the canopy in the region [as shown in Figure 8b, the height value of the canopy is repsimilarity of the distribution of height values between the generated CHM and the label
resented by the grayscale values between the 0–255 threshold]. This is defined in Equation
CHM and makes the distribution of canopy heights generated by the generator network(3):
more reasonable. This loss consists of canopy height value quantity distribution loss, i.e.,
lossquality , and canopy height value location distribution
loss, that is, lossdistribution , and it∑ |𝑔(𝑖) − 𝑡(𝑖)|
(3)
𝑙𝑜𝑠𝑠
=
is defined as shown in Equation(2):
255
where g(i) is the number of pixels generating the ith level of height values in the forelossQD = lossquantity + lossdistribution(2)ground portion of the CHM, and t(i) is the number of pixels generating the ith level ofheightloss
valuesin the foreground portion of the true CHM, and the diﬀerence between thewherequality is calculated by counting the number of height values in the canopy region,
two
is
used
as
loss function
so that the
network
learnstheCHMvariability
the distrifocusing on thea partof the generated
CHM
and thelabel
in thebetween
foregroundregionbutionsofthenumberofheightvaluesinthecanopy.
of the canopy only [shown in Figure 8a], and counting the number of height values of the

canopy in the region [as shown in Figure 8b, the height value of the canopy is represented
by the grayscale values between the 0–255 threshold]. This is defined in Equation (3):lossquantity =∑255
i =1 | g (i ) − t (i )|
255

(3)

where g(i) is the number of pixels generating the ith level of height values in the foreground
portion of the CHM, and t(i) is the number of pixels generating the ith level of height values

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Remote Sens. 2024, 16, x FOR PEER in
REVIEW
11 usedof 29 asthe foreground portion of the true CHM, and the difference between the two isa loss function so that the network learns the variability between the distributions of thenumber of height values in the canopy.

Figure8.8.Canopy
Canopyforegroundforeground region
region ofandthethenumberof height
Figureof generated
generatedCHM
CHMvs.
vs.labellabelCHM
CHM
andnumberof height
values within the region statistics. (a) Generated CHM vs. label CHM in the foreground region. (b)values within the region statistics. (a) Generated CHM vs. label CHM in the foreground region.
Distribution of the number of height values within the canopy foreground region for generated(b) Distribution of the number of height values within the canopy foreground region for generated
CHM versus label CHM.
CHM versus label CHM.

lossdistribution is calculated by determining the distribution distance of each heightloss
is calculated by determining the distribution distance of each heightvalue indistribution
the canopy region from the center of the canopy, first determining the center 𝑂
value
in
the
canopythe centerof the canopy,
determiningof the smallest outerregion
circle infrom
the canopy
foreground[Figure first9a]. First,
the centerthe
𝑂 centerof the O
ofsmallest
the smallest
outercircleinthecanopyforeground[Figure9a].
First,
thecenterO of theouter circle in the foreground of the canopy is determined. Then, the distancesmallest
outercircleintheforegroundofthecanopyisdetermined.
Then,
the
distance, 𝑟 , between a height value ℎ (𝑖 ∈ [1, 255]) and the center O of𝑟, ,𝑟, ,𝑟, …𝑟, …𝑟,ri,1the, ri,2, ri,3 .in. .theri,j .canopy. . ri,m−1(where, ri,m between
heightofvalue
hi (valuesi ∈ [1, 255
andcanopy)
the center
O of]) theis decircle
m is the anumber
heightℎ inthecircleinthecanopy(wheremisthenumberofheightvalueshinthecanopy)i height value hi istermined. In this paper, we define the distribution distance of the canopy
determined.
paper,thewedistancedefine the
distributionof thecanopyofheightvalue hias 𝑟̅ , with In𝑟̅ thisdenotingfromthe main distance
distribution
location
this heightasvalueri , withri denoting
from ofthethemain
distribution
location
this heightvalue
in thecanopy tothe
thedistancecenter point
canopy[as shownby the ofradiusof the redindashed
the canopy
centercanopy[as shownby the radiusof the red dashedcircletoin theFigure9a],point
whichofisthedefinedas shown
in Equation(4):
circle in Figure 9a], which is defined as shown in Equation (4):∑ 𝑟,𝑟̅ = " m,# 𝑗 ∈ [1, 𝑚](4)∑ j𝑚=1 ri,jri =, j ∈ [1, m](4)m
The calculated CHM and the distribution
distance 𝑟̅ for each height value ℎ are
shown in Figure 9b.
Thecalculated
CHM andthe distribution
distanceri foreachinheightare shown
In orderto describe
the variability
betweenthe two
imagestermsvalueof thehidistribuintion
Figure9b. of canopy height values, this paper defines 𝑙𝑜𝑠𝑠
distance
as shown in EquationIn(5):order to describe the variability between the two images in terms of the distribution
distance of canopy height values, this paper
defines loss
as shown in Equation (5):∑ |𝑔(𝑟̅ ) − 𝑡(𝑟̅ )|distribution
(5)
𝑙𝑜𝑠𝑠
=
,
𝑖 ∈ [1, 𝑚]
255
255
g(ri )of−heightt(r i )| values in the foreground part of∑a |level
where g(𝑟̅ ) is the distribution distance of
i =1
loss𝑡(𝑟̅, i ∈ [1, m](5)
distribution
the generated CHM, and) is the=distribution
255distance of a level of height values in theforeground part of the label CHM. Using the diﬀerence between the two as a loss function

Remote Sens. 2024, 16, 1570

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12 of 29
where g(ri ) is the distribution distance of a level of height values in the foregroundpart of

Remote Sens. 2024,the16, xgenerated
FOR PEER REVIEW
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CHM, and t(r ) is the distribution distance of a level of height values in12theiforeground part of the label CHM. Using the difference between the two as a loss function
allows the network to learn about the variability between the distributions of the locations
allows the network to learn about the variability between the distributions of the locationsof the canopy height values.
network to learn about the variability between the distributions of the locationsof the canopy allows
heightthevalues.
of the canopy height values.

FigureSchematic representationof thedistancesof the heights
of a levelof theFigure9. 9.Schematic
representationof distribution
the distribution
distancesof theheights
ofcanopy
a level of thein the CHM (a) and statistics of the distribution distances of height values of the CHM in the forecanopy in the CHM
and statisticsof the distribution
distancesof height
CHM
incanopy
theFigure(a)9. Schematic
representationof the distribution
distancesof thevalues
heightsofof thea levelof theground region of the canopy (b).
in
the
CHM
(a)
and
statistics
ofthedistribution
distancesofheightvalues
of
the
CHM
in
the
foreforeground region of the canopy (b).
ground region of the canopy (b).
Before the fusion of lossquality and lossdistribution, we first normalized them to [0, 1] via
Before scaling
the fusionof loss
losslosses
first normalized
to [0, 1] via
quality
distribution
decimalto ensurethatboth andof theirare of, we
the samescale. Figure them
10 shows
Before the fusion of lossquality and lossdistribution, we first normalized them to [0, 1] via
decimalscaling to eﬀect
ensurethat andbothafterof theirlossesaretoofthethegenerator’ssame scale.
10forshows thelossFigure
functionthe comparison
beforeaddinglossQDdecimal scaling to ensure that both of their losses are of the same scale. Figure 10 shows
the same numberof training
rounds.
In contrast
toFigure10a,
Figure10b
illustrates
a for thecomparison
effectbeforeandafteraddingloss
tothegenerator’s
loss
functionforthe comparison eﬀect before and afterQD
adding lossQD to the generator’s loss function
distinct
variationinheightwithintheinnercanopy,
and
in
terms
of
canopydetails,thissame number theof training
rounds.
In contrast
to Figure
10a,toFigure
distinct asame numberof training
rounds.
In contrast
Figure10b10a,illustrates
Figure 10baillustrates
result isinmore
in line with theactual CHM.
As can beinobserved,
the networkdetails,
can signifivariation
heighttheterms ofthis
resultthis
distinctwithinvariation
in innerheight canopy,
within theandinner canopy,
andcanopy
in terms of canopy
details,
cantly increase the quality of CHM generation and speed up the training process after the
more
in lineCHM.
with theCHM.
As can beobserved,
thecannetwork
can signifiis more in lineresult
withistheactualAsactual
can beobserved,
thenetworksignificantly
addition of lossQD within the same training iteration rounds. The generator significantly
cantly ofincreasequality of andCHMspeed
generation
andspeed upthe training
after theincrease
the quality
CHMthegeneration
up thetraining
process
after process
the addition
increases CHM generation quality and quickens the network’s convergence process after
additionof losstraining
QD within the same training iteration rounds. The generator significantlyof loss
withinthesameiterationrounds.
The
generator
significantly
increases
lossQD
QD is added.
increases CHM generation quality and quickens the network’s convergence process after
CHM generation
quality and quickens the network’s convergence process after lossQD
lossQD is added.

is added.

Figure 10. Comparison of CHM images and details before and after adding lossQD to the generator’s
loss function for the same training rounds. (a) CHM generated without adding lossQD . (b) CHM
generated after adding lossQD . (c) Label CHM.

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12 of 27

Figure 10. Comparison of CHM images and details before and after adding lossQD to the generator’s
loss function
for the same
trainingrounds. (a) CHM generated without adding lossQD. (b) CHM
2.3. Canopy
Architecture
TraitsExtraction
generated after adding lossQD. (c) Label CHM.

The measurement of fruit tree canopy architecture traits is mainly concerned withthe relevant
parametersof a fruit
tree canopy in 3D space, of which the most important
2.3. Canopy
Architecture Traits
Extraction
structuralTheparameters
includeheight,architecture
canopy width,
projectedwith
area, canopy
measurementof fruit tree
tree canopy
traits iscanopy
mainly concerned
the relevant
parametersof aarchitectural
fruit tree canopy
in 3Ddirectly
space, ofreflect
which theimportantvolume,etc. [25–28].
These
traitsthemostgrowthstatus of fruit
includetree height,
width, canopyand
projected
area, canopy
trees structuraland playparameters
an important
guidingrole canopy
in the monitoring
managementof fruit trees.
volume, etc. [25–28]. These architectural traits directly reflect the growth status of fruit
Therefore, in this paper, the above four canopy structure parameters are used to evaluate
trees and play an important guiding role in the monitoring and management of fruit trees.
the performanceof canopy
measurement
methods,
andthe definitions
four canopy
Therefore, in thispaper, theabove four canopy
structureparameters
are usedof
to these
evaluatestructureparameters
are as follows:
the performanceof canopy
measurement methods, and the definitions of these four can1.
2.
3.
4.

opy
parameters
are as follows:
CHstructure(Canopy
Height)—thevertical distance from the highest part of the canopy to the1.
CH (Canopy
Height)—theground,as shown
in Figurevertical
11a; distance from the highest part of the canopy totheground,asshown
inFigure11a;
CW (Canopy Width)—the diameterof the smallest outer circle of the vertical projec2. CW (Canopy Width)—the diameter of the smallest outer circle of the vertical projectiontionof thecanopy,as
shown
in
Figure11b;of the canopy, as shown in Figure 11b;
CPA
(Canopy
Projected
Area)—theverticallyprojectedof the(empirical
canopy (empirical
3. CPA (Canopy Projected Area)—the vertically projectedarea ofarea
the canopy
2 as shown in Figure 11c;
value
used
in
this
paper:
𝐶𝑃𝐴
≈
0.65
∗
𝜋
∗
(CW/2)[15],
value used in this paper: CPA ≈ 0.65 ∗ π ∗ (CW/2) [15], as shown in Figure 11c;4.
(CanopyVolume)—the
Volume)—the volumeof the3D convex
envelopeof the canopy,
CV CV(Canopy
volumeof the3D convex
envelopeof theascanopy, as
shown in Figure 11d.

shown in Figure 11d.

Figure11. Schematic
diagram ofof canopy
parameters.
(a) Canopy
height, CH.
(b) Canopy
Figure11. Schematic
diagramcanopystructural
structuralparameters.
(a) Canopy
height,CH. (b) Canopy
width, CW. (c) Canopy projected area, CPA. (d) Canopy volume, CV.
width, CW. (c) Canopy projected area, CPA. (d) Canopy volume, CV.

2.4. Implementation
In the experiment, we employed the same computer, with 32 GB of RAM and anIntel i7-12700F CPU (Intel, Santa Clara, CA, USA) running at 2.10 GHz, to execute all
the methods. Additionally, this machine has an RTX4090 GPU (Nvidia, Santa Clara, CA,
USA) with 24 GB of VRAM. The operating system used for the experiment is Ubuntu
18.04. The proposed approach was implemented using Python 3.7. The canopy heightcalculation network and foreground segmentation network for this method were implemented using the Pytorch version 1.12.1 framework. Appropriate adjustments weremade to the training process’s epoch (epoch = 500), learning_rate (learning_rate = 0.001),
and batch_size (batch_size = 4). With a calculation amount amounting to 28.2 GFLOPS
and 5.45 M parameters, the training procedure took roughly 6 h, the more details canRemote Sens. 2024, 16, 1570

13 of 27

found in Supplementary Materials. The SFM+MVS method was executed using Pix4D
Mapper version 1.1.38 software. Demo software version 1.0 and data can be accessed athttps://github.com/I3-Laboratory/EasyCanopy (accessed on 22 February 2024).
3. Results
Currently, the method based on SFM+MVS reconstruction is the most commonlyused method in the field of UAV agricultural surveying and mapping [29–32]. In thismethod, first, feature points between image sequences are extracted; then, feature matchingis performed, and the spatial position relationships between photographic images are
estimated; finally, a three-dimensional point cloud of the object based on the estimatedcamera parameters is constructed. The existing fruit tree canopy architecture trait measurement methods are mostly based on this method. Therefore, we chose SFM+MVS as
a comparison method. Since UAV photogrammetry often requires using oblique photography technology to improve canopy structure reconstruction, the camera angle and the
UAV route need to be adjusted to ensure the overlap rate between the image sequences,which undoubtedly increases the cost of image data acquisition. In order to further simplify
the UAV aerial photography work and reduce the cost of data acquisition, the UAV aerial
photographs were taken using a vertical overhead angle in this experiment. This section
details comparative experiments conducted in terms of canopy structure traits (introducedin Section 3.1), canopy 3D structure (introduced in Section 3.2), and canopy reconstruction
efficiency (introduced in Section 3.2).
3.1. Validation via Canopy Structure Traits
In this section, we calculate the error rate and linear correlation between the measurement results and the true values. Figure 12 shows the results of a comparison between the
canopy structure parameters measured using the two methods and the true values for eachfruit tree. Table A1 shows the absolute values of the errors between the canopy structure parameters
Remote Sens. 2024, 16, x FOR PEER
REVIEW measured using the two methods and the true values of each fruit
15tree.
of 29 Figure 13
shows the linear correlation between the canopy structure parameters measured using thetwo methods and the true values for each fruit tree. Figure 14 shows the structural visualstructure
the canopy
reconstructedusing
has aandlarge
resulting in a
izationpointofcloudreconstructed
using
thethistwomethod
methods
itsloss,
terrestrial-laser-scanninglarge
gap forbetweenconvex
volume and the actual canopy volume.
point
cloud
eachthefruit
tree hullcanopy.

Figure 12. Comparison between the canopy structure parameters measured using the present andFigure12. Comparison between the canopy structure parameters measured using the present
SFM+MVS methods and the ground truth. (a) Comparison of CH. (b) Comparison of CV. (c) Comand parison
SFM+MVSand theground truth. (a) Comparison of CH. (b) Comparison of CV.
of CW.methods(d) Comparisonof CPA.
(c) Comparison of CW. (d) Comparison of CPA.

Meanwhile, as shown in Figure 13, the correlation coeﬃcient 𝑅 (which measuresthe linear correlation between the measured value and the true value) of the proposed
method for the CH is 0.9039, on the CV is 0.9081, on the CW is 0.9228, and on the CPA is0.9303. The linear correlation coeﬃcients 𝑅 of the structural parameters of the reconstructed canopy for the proposed method and the true value are all in the range of 0.9303.
The linear correlation coeﬃcients between the structural parameters of the reconstructedRemoteSens.
2024,
x FOR PEER REVIEW
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14 of 27

Figure13.
13.Comparison
Comparisonof
ofthe
the linear
linear correlation
correlation between
between the
Figurethe canopy
canopy structure
structureparametersparametersmeasured
measuredusing the proposed method and the SFM+MVS method and their true values. (a) Comparison ofusing the proposed method and the SFM+MVS method and their true values. (a) Comparison of CH.
CH. (b) Comparison of CV. (c) Comparison of CW. (d) Comparison of CPA.
(b) Comparison of CV. (c) Comparison of CW. (d) Comparison of CPA.

3.2. As
Validation
shown in Figure 12 and Table A1, the experimental error rate of the proposed
Asfor
shown
Figureby comparing
the parameterreconstructed
fruit tree
structuremethodeach infruit
tree 14,canopycharacteristic
is small,withcanopy
an average
error
withofthat
of SFM+MVS,
although
fruit
point
cloud
rate
3.296%
for the CH,
9.067% thefor reconstructed
the CV, 2.772%
fortreethe canopy
CW, and5.541%
forstructure
the CPA,in thispaper ismethod
diﬀerenthas
fromthat oferror
the terrestrial
lasercanopy
scannerstructural
point cloudin relationThetoandproposeda smallrate for eachparameter.
somedetails
(as
shown
in
the
blue
dashedarea
inFigure14b),
thereconstructed
fruit
tree
average error rate absolute value between the measurement results and the true value forcanopystructurehas greater
similarity
withusingthe 3D
structureground
cloud
in
each
canopystructureparameter
assessedthismethodofisthe
below
10%.point
On theothertermstheof theoverall convex
packetverifies
accuracyof theparameter,
measurehand,
SFM+MVS
methodhas amorphology.
higher error Thisrate foreachthecanopystructurement
the fruit value
trees inof theirerror
canopyparameters.
On
other
hand,
and
theofabsoluteof terms
the average
ratestructure
for the CVparameteris the
more
than
50%.
the measured
point cloudreconstructed
the SFM+MVS
methodis thecenteredon themethod
top of theThevaluesof the CVusingparameters
determinedusing
SFM+MVSarefruittree thancanopy,which is mostly
in theform
a slice. The
canopyallsmallerthe ground-truth
values.
It canbe ofspeculated
that reconstructed
the three-dimensional

Remote Sens. 2024, 16, 1570

structure is partially missing from the top-view perspective (as shown by the orange
dashed area in Figure 14c). The bumpy packet structure is also quite diﬀerent from thatin the terrestrial-laser-scanner-scanned canopy point cloud, which did not reconstruct the
3D structure of the canopy well, and this directly aﬀects the accuracy of the parameters
15 of 27
relating to the canopy structure of fruit trees. This also directly aﬀects the accuracy of the
canopy structure parameters. It can be seen that the method proposed in this paper canbetter reconstruct
the 3D
structure ofusing
a fruitthis
treemethod
canopyhas
anda obtain
more-accurate
structureof the canopy
reconstructedlarge loss,
resulting incanopy
a largestructureparameters
using
UAV
aerialimagescompared
withthe
SFM+MVS
method.
gap between the convex hull volume and the actual canopy volume.

Figure 14. Visualization
of ofthethecomparison
betweenthe canopy
structureof fruit
FigureVisualizationresults
results
comparison
betweenthe canopy
structure
oftrees
fruitrecontrees
structedusing
theproposedmethodand
SFM+MVS
methodanditsterrestrial
laser
scanner(TLS)reconstructed using the proposed method and SFM+MVS method and its terrestrial laser scanner
derived pointpoint
cloudcloud(in theof peachtreetreecanopyNo.
(TLS)-derived(in case
the caseof peach
canopyNo.BC4).
BC4).(a)(a)Structure
Structureofof terrestrial-laserterrestrial-laserscanner-scanned point cloud. (b) Canopy point cloud structure reconstructed using the proposed
scanner-scanned point cloud. (b) Canopy point cloud structure reconstructed using the proposed
method. (c) The canopy point cloud structure reconstructed using the SFM+MVS method.
method. (c) The canopy point cloud structure reconstructed using the SFM+MVS method.

3.3. Reconstruction
Meanwhile, asEﬃciency
shown inComparison
Figure 13, the correlation coefficient R2 (which measures the
addition, between
as shownthe
in measured
Table 1, invalue
termsandof thecost
for the fruit
treelinearIncorrelation
the reconstruction
true value) of the
proposedmethodcanopy,theisproposed
requirestwo
imagesto complete
theCPA
reconstruction
offor
the CH
0.9039, onmethod
the CV is
0.9081, only
on theCW
is 0.9228,
and on theis 0.9303. The
2 of thea single
fruit treecoefficients
canopy. ItRtakes
only
24 images
to complete
reconstructionof thelinearcorrelation
structuralparametersof thethereconstructed
canopyforcanopiesof all12 fruit
trees,
withvalue
an averageof the30 srange
required
for thereconstructionof athe
proposedmethodandthe trueare all inof 0.9303.
The
linear correlation
single fruit tree
canopy.
contrast,parameters
the SFM+MVS
methodused 203canopy
imagesandandthetook
02
coefficients
betweenthe Instructuralof thereconstructedtrue
valuesfor thisallreconstruction
above 0.9, whichshowsa strong
correlation
betweenh:06 m:04
s to methodcompleteare
theof all12 fruit
trees.linear
The proposed
methodrethemeasured
the true
values, while
linearcorrelation
coefficients
betweenduces
the timevalues
cost ofandcanopyreconstruction
bythe
95.2%
and
the cost ofimages neededforthestructuralparameters
the reconstructed
canopyandproposed
the true values
the reduce
indicators
canopyreconstruction
byof88.2%.
It can be seenthat themethodofcan
thefor
the acquisition
SFM+MVS cost
methodare treebelow
0.8, which
is a weakerlinearcorrelationcomparedimageof fruit
canopyreconstruction
andthe time
required
for fruit2 of the SFM+MVS method
with
that
of
the
proposedmethod.
FortheCVparameters,thertree canopy structure reconstruction more substantially.
is only 0.6778, which also shows that there is a large error in the measurement results ofTable
1. The comparison of the time and the number of images required to reconstruct the canopy
thismethod.
structures of 12 fruit trees between the proposed method and the SFM+MVS method(↓: The reduc-3.2.
tion Validation
ratio of the proposed method compared to the SFM+MVS method).
As shown in Figure 14, by comparing the reconstructed fruit tree canopy structure
MethodSFM+MVS
Our Methodwith that of SFM+MVS, although the reconstructed fruit tree canopy point cloud structure
Reconstruction time
02 h:06 m:04 s 6 m (↓95.2%)
in this paper is different from that of the terrestrial laser scanner point cloud in relation
Average reconstruction time/tree
630 s
30 s (↓95.2%)
to some details (as shown in the blue dashed area in Figure 14b), the reconstructed fruit
Numberimages needed
for reconstruction/picture
203 of the ground24 (↓88.2%)
tree
canopyofstructure
has greater
similarity with the 3D structure
point
cloud in terms of the overall convex packet morphology. This verifies the accuracy ofthe measurement of the fruit trees in terms of their canopy structure parameters. On theother hand, the point cloud reconstructed using the SFM+MVS method is centered on
the top of the fruit tree canopy, which is mostly in the form of a slice. The reconstructed
canopy structure is partially missing from the top-view perspective (as shown by the orange
dashed area in Figure 14c). The bumpy packet structure is also quite different from that

Remote Sens. 2024, 16, 1570

16 of 27in the terrestrial-laser-scanner-scanned canopy point cloud, which did not reconstruct the
3D structure of the canopy well, and this directly affects the accuracy of the parameters
relating to the canopy structure of fruit trees. This also directly affects the accuracy of the
canopy structure parameters. It can be seen that the method proposed in this paper canbetter reconstruct the 3D structure of a fruit tree canopy and obtain more-accurate canopy
structure parameters using UAV aerial images compared with the SFM+MVS method.
3.3. Reconstruction Efficiency Comparison
In addition, as shown in Table 1, in terms of the reconstruction cost for the fruit tree
canopy, the proposed method requires only two images to complete the reconstructionof a single fruit tree canopy. It takes only 24 images to complete the reconstruction of the
canopies of all 12 fruit trees, with an average of 30 s required for the reconstruction ofa single fruit tree canopy. In contrast, the SFM+MVS method used 203 images and took
02 h:06 m:04 s to complete the reconstruction of all 12 fruit trees. The proposed method
reduces the time cost of canopy reconstruction by 95.2% and the cost of images needed forcanopy reconstruction by 88.2%. It can be seen that the proposed method can reduce theimage acquisition cost of fruit tree canopy reconstruction and the time required for fruit
tree canopy structure reconstruction more substantially.
Table 1. The comparison of the time and the number of images required to reconstruct the canopy
structures of 12 fruit trees between the proposed method and the SFM+MVS method(↓: The reduction
ratio of the proposed method compared to the SFM+MVS method).
MethodSFM+MVS

Our Method

Reconstruction time
Average reconstruction time/tree
Number of images needed for reconstruction/picture

02 h:06 m:04 s
630 s
203

6 m (↓95.2%)
30 s (↓95.2%)
24 (↓88.2%)

Although the Pix4D software based on the SFM+MVS method utilizes the GPS positioning information of UAV aerial photography images to assist in the reconstruction of fruit
tree canopies, it still needs to satisfy the geometrical constraints of the camera viewpoint
based on the feature point matching of the images, so this method’s feature-point-matchingprocess with respect to images is inevitably affected by the ground background and thesurrounding fruit tree canopy. This affects the derivation of camera position in the SFM
process. At the same time, because the UAV aerial images were acquired using tilt photography, the MVS process of this method is not sufficient for employment in the dense
reconstruction of the viewing angle information, resulting in the dense reconstructionof the canopy 3D structure being inaccurate, a trait specifically reflected in the fact thatthe reconstruction of the canopy point cloud has different degrees of missing features,
which seriously affects the accuracy of the canopy structural parameters. It can be seenthat the image sequences of UAV aerial photography obtained based on the SFM+MVS
method could not yield a very good reconstruction of the 3D structure of the canopy, so,
temporarily, UAV aerial photography and the SFM+MVS method cannot be used to obtain
more accurate measurements of the canopy architecture traits of a single fruit tree. The
proposed method uses the canopy height calculation network to learn the distribution
characteristics of canopy height and obtain a fine canopy height image. On this basis, amore accurate 3D canopy structure can be obtained.
In addition, as shown in Table 1, regarding the cost required for fruit tree canopy
reconstruction, the SFM+MVS method requires a large number of photographic images
and a great deal of time for canopy reconstruction. On the other hand, since this method
cannot distinguish the front background of a canopy in the canopy reconstruction process,the redundant ground background information is involved in the camera position inferenceof SFM and the multi-view dense reconstruction process of MVS, resulting in additional
computational overhead and an increase the computational time required for canopy
reconstruction when using this method. In this paper, the proposed method requires onlyRemote Sens. 2024, 16, 1570

17 of 27

two images for the reconstruction of the same fruit tree canopy target. The foregroundgeneration of the same fruit tree canopy target in binocular vision is performed before
canopy height prediction, avoiding the influence of the ground background and otherfruit tree canopies on the reconstruction process. At the same time, the canopy height
prediction method based on the similarity of the block structures proposed also further
accelerates the speed of fruit tree canopy height prediction. It can be seen that comparedwith the SFM+MVS method, the proposed method has lower costs and a faster speed in
canopy reconstruction.
4. Discussion
For precise fruit tree management, canopy architecture traits are important, andwith the rapid development of UAV sensing techniques, phenotyping such traits canbe performed efficiently. However, there are still limitations of the current solutions,
including the hardware costs of Lidar-sensor-based solutions and the calculation costs of
RGB photogrammetry-based solutions. In this paper, we propose a novel UAV-based singlelens stereoscopic photography method designed to mitigate these challenges. The methods
were evaluated with respect to a peach orchard in the summer, and the results demonstrateefficiency and cost-effectiveness in canopy reconstruction, significantly reducing boththe time and computational demands traditionally associated with such tasks. We also
recognize that canopy architecture traits are needed in the winter as well, so, in addition, apreliminary experiment was also conducted using the image data of winter orchard trees
collected in another experiment field in Okayama prefecture (the experimental objects
included 12 peach trees, BF1-BF12), as shown in the Appendix. The proposed method
was also compared with the SFM+MVS method. The experimental results show that the
proposed method also shows effectiveness in the measurement of the canopy structuresof fruit tree branches in the winter (the experimental results are shown in Appendix A.3,
Figures A3–A6, and Tables A2 and A3).
Although the proposed method will produce more-accurate fruit tree canopy architecture trait measurement results in experiments, it is undeniable that measuring such
traits based on a single UAV lens is still a challenging task. First of all, in the experimental
results, it can be found that there are still some differences between the reconstructed
canopy and the real canopy in terms of the structural details of the proposed method,especially in relation to the winter fruit tree branches and the trunks of the object. The
proposed method also failed to capture more branches and trunks, resulting in errors in
the CW and CPA parameters of the fruit trees. This misrepresentation was caused by
the inaccurate segmentation of the foreground (canopy), making the CFM-RGB partiallymisrepresented, which affects the subsequent 3D structure reconstruction of the canopy.
It can be seen that the performance of the foreground segmentation network affects theaccuracy of canopy reconstruction, and a better foreground segmentation network can beselected in the future to enhance canopy reconstruction. Secondly, the current experiment
was only carried out on fruit trees in a certain period, and the actual state of the canopyof the fruit trees (the color of the canopy leaves, the degree of canopy leaf coverage, etc.)
changes dynamically with the change of season and time, which may pose a challengewith respect to the generalization ability of the canopy height calculation network and the
canopy foreground segmentation network. Aerial photography data of fruit trees captured
throughout the whole year can be collected to improve the learning ability of the networkwith respect to the canopy information of fruit trees in each period. If one needs to measurefruit trees in a new orchard [33–35], the model generated in this study can be applied or
finetuned, using new trees as a training dataset.
The proposed method can efficiently measure the phenotypic traits of fruit tree
canopies in large-area orchards; has guiding significance for fruit tree yield prediction, fruit
tree pruning, water and fertilizer application, pest and disease control, etc.; and promotes
the development of precision agriculture. The proposed method can be adapted to theedge devices carried by UAVs to realize online real-time phenotyping tasks. In addition,

Remote Sens. 2024, 16, 1570

18 of 27

other phenotypic traits such as canopy light interception rate and leaf area index can befurther measured based on the reconstructed fruit tree canopy structure. Finally, so far,the proposed method has been applied to fruit trees in orchards, but in future work, thisidea can be applied to the phenotypic measurement tasks of other crops, such as field
crops, low shrubs, and other scenarios, to broaden the scope of application of the proposed
method in agriculture.
5. Conclusions
In conclusion, in this study, we developed a high-precision, fast, and low-cost methodof measuring the canopy architecture traits of single fruit trees based on a single UAV lens.
This method can quickly obtain the 3D structure of a fruit tree canopy with higher accuracy
and calculate 3D characteristic parameter information regarding canopy structure, on thebasis of which we can reduce the cost of fruit tree canopy data collection and processing.
And compared to conventional methods, our method yields better results and has higher
efficiency. It can be adapted to the fruit tree canopy architecture trait measurement tasks indifferent seasons and is suitable for the high-throughput and accurate measurement of the
canopy structure of a single fruit tree in a wide range of orchard scenarios to assist in theanalysis of the growth states of fruit tree canopies.
Supplementary Materials: A video about the workflow of this study can be found at https://youtu.
be/A-MN9hlXiQ4 (accessed on 22 February 2024).
Author Contributions: Conceptualization, W.Z., X.P. and W.G.; methodology and software, W.Z., X.P.,
T.B. and W.G.; validation, W.G., T.B. and H.W.; data investigation and curation, D.T.;
writing—original draft preparation, W.Z. and X.P.; writing—review and editing, X.P., W.G., W.Z. and
H.W.; visualization, T.B. and H.W.; supervision, W.Z. and W.G. All authors have read and agreed tothe published version of the manuscript.
Funding: This study was partially supported by the National Natural Science Foundation of China(NSFC) Program Number 62276009 and the Japan Science and Technology Agency (JST) AIP Acceleration Research Number JPMJCR21U3.
Data Availability Statement: Demo software and demo data can be accessed at https://github.com/
I3-Laboratory/EasyCanopy (accessed on 22 February 2024).
Acknowledgments: The authors thank Yuichiro Fujii from the Okayama Prefectural Technology
Center for Agriculture, Forestry, and Fisheries for providing access to Okayama orchard and Futaba
Inc. for providing Lidar scanning data of Fukushima orchard.
Conflicts of Interest: The authors declare no conflicts of interest.

Appendix A
Appendix A.1 Methodologies of Image-Preprocessing Module and Canopy Structure
Reconstruction Module
Appendix A.1.1 Image-Preprocessing Module
Before estimating the height of the fruit tree canopy, we preprocessed the UAV image
sequence to generate a standard binocular CFM-RGB to facilitate subsequent canopy
height estimation work. The preprocessing process of this module is as follows (shown in
Figure A1):
1.
2.

Neighboring frame extraction: Extract neighboring frame images from UAV aerial
videos or image sequences.
Simulate binocular transformation: Since the UAV trajectory may not be parallel tothe camera lens CMOS sensor, it is necessary to convert the adjacent frames into astandard binocular image. This paper provides two transformations for simulating
standard binocular photography, and the details of the process are as follows:a.

Known camera lens attitude: The attitude of the lens while capturing UAV
photographs can be obtained from the UAV aerial photography log. AccordingRemote Sens. 2024, 16, 1570

19 of 27b.

to the rotation angle α of the horizontal direction of the camera lens, rotate theneighboring frames of the image and crop out the redundant boundaries; thus,
the form of the standard binocular image can be obtained.
Unknown camera lens attitude: If the attitude of the UAV camera lens is unknown, the rotation angle α can be calculated based on binocular feature point
matching, and the standard binocular image form can be obtained by rotating
the neighboring frame images according to the rotation angle α and croppingout the redundant boundary.

Target canopy matching: In this study, we used high-dimensional feature templatematching to match the uniform canopy object in the binocular image, and we wereable to determine the region of the uniform canopy object in the binocular map.
4.
Matching region segmentation: The matching region of the same fruit tree canopy inthe binocular image is segmented.
5.
Canopy foreground segmentation: In order to reduce the impact of the groundbackground region on the subsequent process of fruit tree canopy height estimation,
Remote Sens. 2024, 16, x FOR PEER REVIEW
21 of 29
this module uses the foreground segmentation network (in this paper, we use theU2 -Net) to realize the separation of the front background of the fruit tree canopy.
3.

FigureA1.A1.
Fruit
treetree
CFM-RGB
generation
modulemodule
workflow.
FigureFruit
CFM-RGB
generation
workflow.

1.
2.

Neighboring frame extraction: Extract neighboring frame images from UAV aerial
videos or image sequences.
Simulate binocular transformation: Since the UAV trajectory may not be parallel tothe camera lens CMOS sensor, it is necessary to convert the adjacent frames into astandard binocular image. This paper provides two transformations for simulating

5.

Remote Sens. 2024, 16, 1570

the binocular image is segmented.
Canopy foreground segmentation: In order to reduce the impact of the ground background region on the subsequent process of fruit tree canopy height estimation, thismodule uses the foreground segmentation network (in this paper, we use the U2-Net)
to realize the separation of the front background of the fruit tree canopy.
20 of 27

Appendix A.1.2. Canopy Structure Reconstruction Module
The measurement
fruit treeReconstruction
canopy structuralcharacteristic parameters is based
Appendix
A.1.2 Canopyof
Structure
Moduleon the 3D structure of fruit tree canopies. This module recovers the 3D point clouds of
The measurement of fruit tree canopy structural characteristic parameters is based
fruit tree canopies from the CHM obtained from the canopy height calculation module inon the 3D structure of fruit tree canopies. This module recovers the 3D point clouds ofSection 2.2.2 and calculates the characteristic parameters of a canopy on the basis of the
fruit tree canopies from the CHM obtained from the canopy height calculation module in
3D point
cloud
the canopy,
and the structural
restoration
processof basisthe canopy
isSection
2.2.2
and ofcalculates
the characteristic
parametersof a canopyon theof the 3D
shownin
FigureA2.
point cloud of the canopy, and the structural restoration process of the canopy is shown in
Accordingto the ℎrecorded in the process of canopy height estimation in Section
FigureA2.
2.2.2 Accordingof the maintotext,
wedetermined
thethetrue
canopycorresponding
to eachthe hmax recorded inprocessofheight
canopyheight estimationin grey
Seclevelinthehigh-quality
CHM
outputfromthecanopyheightcalculation
networkandtion 2.2.2 of the main text, we determined the true canopy height corresponding to eachthenreconstructedthecanopypoint
clouds
for
the
left
and
right
eyes,
respectively,
acgrey level in the high-quality CHM output from the canopy height calculation network
cording
totheprincipleofsmall-hole
imagingsoastoobtainthecanopypoint
clouds𝑝𝑐_𝑙
and then reconstructed the canopy point clouds for the left and right eyes, respectively,
and 𝑝𝑐_𝑟, toalignmergeofthe
two piecesof point
in thethe
3D canopy
space, and
carry
out
accordingtheandprinciple
small-hole
imaging
soclouds
as to obtainpoint
cloudssmoothingprocessingonthemergedpoint
clouds(z-direction
mean
processingofpoint
pc_l and pc_r, align and merge the two pieces of point clouds in the 3D space, and carry out
clouds at thesame horizontal
position)to obtain
final 3D point
𝑝𝑐 of ofthepoint
cansmoothing
processing
on the merged
pointcloudsthe(z-direction
meancloudprocessing
opy. at the same horizontal position) to obtain the final 3D point cloud pc of the canopy.
cloudsFigure A2. Schematic diagram of the restoration
restoration ofof thethe 3D
3D structure
structure ofof thethe fruit
fruit tree
tree canopy.
canopy.
FigureAppendix A.2 Canopy Structure Parameter Error Rates (Absolute Values) for the Proposed Method,
the SFM+MVS Method, and Ground Truth
Table A1. Error rates (absolute values) between the canopy structure parameters measured using
proposed method and the SFM+MVS method and the ground truth based on a summer fruit tree
canopy with leaf cover.
CH (mm)Tree_NumGT

Our Method

SFM+MVS

Error_Our Method

Error_SFM+MVS

BC1

3291.9998

3180.4354

2643.0954

3.389%

19.712%BC23296.9971

3161.1417

3481.7983

4.121%

5.605%

BC3

3493.9995

3389.718

3780.952

2.985%

8.213%

BC4

2644.9966

2731.0678

2773.5854

3.254%

4.862%

BC5

2705.9975

2592.0667

2167.6885

4.210%

19.893%

Remote Sens. 2024, 16, 1570

21 of 27

Table A1. Cont.
CH (mm)Tree_NumGT

Our Method

SFM+MVS

Error_Our Method

Error_SFM+MVS

BC63050.9987

2992.4442

2744.8381

1.919%

10.035%BC72990.9973

2843.4558

2763.0019

4.933%

7.623%

BC8

2583.9996

2546.8567

2197.591

1.437%

14.954%BC92954.9980

2882.5761

3045.1062

2.451%

3.049%

BC10

2892.9977

3036.5482

2716.9824

4.962%

6.084%

BC11

3206.9969

3134.6084

2922.3992

2.257%

8.874%

BC12

3138.9999

3024.7864

2969.2662

3.639%

5.407%

3.296%

9.526%

Average
CV (mm3 )Tree_NumGT

Our Method

SFM+MVSerror_Our Method

error_SFM+MVS

BC1

72,988,178,184

78,534,836,967

31,289,459,492

7.599%

57.131%

BC2

55,946,120,754

51,911,611,874

25,312,103,772

7.211%

54.756%BC3

55,218,551,779

50,042,335,368

31,180,146,570

9.374%

43.533%

BC4

48,632,835,775

44,404,385,165

19,454,252,047

8.695%

59.998%BC536,500,327,835

31,794,054,227

17,249,641,227

12.894%52.741%

BC6

39,610,631,527

36,172,979,005

21,360,270,901

8.679%

46.074%

BC7

39,052,282,765

35,616,992,140

18,155,816,993

8.797%

53.509%BC852,181,807,543

46,413,451,781

23,608,660,174

11.054%54.757%BC960,609,531,275

55,582,427,180

25,678,835,565

8.294%

57.632%

BC10

60,231,686,918

54,224,943,037

34,921,278,335

9.973%

42.022%

BC1151,930,116,269

55,804,523,508

29,566,996,423

7.461%

43.064%BC12

29,354,802,482

31,931,216,927

17,505,795,232

8.777%

40.365%9.067%

50.465%

Average
CW (mm)Tree_NumGT

Our Method

SFM+MVSerror_Our Method

error_SFM+MVS

BC18897.1615

9107.1821

7778.5201

2.361%

12.573%

BC2

8349.0019

8105.6921

7548.2131

2.914%

9.591%

BC3

7238.9471

6925.4361

6423.6768

4.331%

11.262%BC48635.5524

8798.5615

7110.0029

1.888%

17.666%BC57194.3135

6848.8549

6817.7399

4.802%

5.234%

BC6

8179.3771

8419.1807

7694.8620

2.932%

5.924%

BC7

6737.5763

7018.0028

5716.5546

4.162%

15.154%BC88297.6803

8228.9672

6716.77660.828%

19.052%BC97299.5998

7120.3661

6409.4224

2.455%

12.195%BC108204.6801

8371.931

6656.2883

2.038%

18.872%BC117155.1607

7227.6965

6593.3290

1.014%

7.852%

BC12

6569.0604

6801.6852

5475.4785

3.541%

16.647%2.772%

12.669%AverageRemote Sens. 2024, 16, 1570
Remote Sens. 2024, 16, x FOR PEER REVIEW

22 of 27

24 of 29

Table A1. Cont.

Average
Tree_Num
Tree_Num
BC1 BC1BC2 BC2BC3 BC3BC4 BC4BC5 BC5BC6 BC6BC7 BC7BC8 BC8
BC9
BC9
BC10
BC10
BC11
BC11
BC12
BC12
Average

CPA (mm2 )2GT GT
40,411,613.06
40,411,613.06
35,585,441.19
35,585,441.19
26,751,863.59
26,751,863.59
38,070,053.30
38,070,053.30
26,422,989.60
26,422,989.60
34,154,166.95
34,154,166.95
23,174,513.56
23,174,513.56
35,149,295.93
35,149,295.93
27,202,031.82
27,202,031.82
34,365,806.90
34,365,806.90
26,136,174.62
26,136,174.62
22,029,759.19
22,029,759.19

CPA (mm )MethodSFM+MVS
OurOurMethodSFM+MVS
42,341,991.34
30,888,526.73
42,341,991.34
30,888,526.73
33,541,574.36
29,086,505.21
33,541,574.36
29,086,505.21
24,484,852.43
21065,443.24
24,484,852.43
21065,443.24
39,520,878.15
25,807,313.27
39,520,878.15
25,807,313.27
23,946,341.33
23,729,254.56
23,946,341.33
23,729,254.56
36,186,193.38
30,227,686.49
36,186,193.38
30,227,686.49
25,143,765.19
16,682,915.49
25,143,765.19
16,682,915.49
34,569,563.87
23,031,649.66
34,569,563.87
23,031,649.66
25,882,599.11
20,972,056.82
25,882,599.11
20,972,056.82
35,781,168.46
22,618,691.96
35,781,168.46
22,618,691.96
26,668,774.15
22,192,832.34
26,668,774.15
22,192,832.34
23,617,628.86
15,305,495.50
23,617,628.86

15,305,495.50

Average

2.772%

12.669%error_Ourerror_OurMethod
Methoderror_SFM+MVS
error_SFM+MVS

4.777%
4.777%

23.565%23.565%5.744%
5.744%

18.263%18.263%8.474%
8.474%

21.256%21.256%3.811%
3.811%

32.211%32.211%

9.373%
9.373%

10.195%10.195%5.950%
5.950%
8.497%
8.497%
1.649%
1.649%
4.850%
4.850%
4.119%
4.119%
2.038%
2.038%
7.208%
7.208%
5.541%

11.496%
11.496%22.903%34.183%34.183%15.088%15.088%30.524%30.524%23.514%5.541%

23.514%28.012%28.012%34.475%34.475%22.903%

Appendix A.3. Winter Time Application
Appendix A.3 Winter Time Application

The flight was carried out in March 2018 over an orchard in Akaiwa, Okayama preThe flight was carried out in March 2018 over an orchard in Akaiwa, Okayama
fecture, Japan (34°46′52.9″N,134°01′01.2″E). At the same time, a FARO Focus 3D Laser
prefecture, Japan (34◦ 46′ 52.9′′ N, 134◦ 01′ 01.2′′ E). At the same time, a FARO Focus 3D
Scanner laser scanner was used to scan the 3D point cloud of this winter peach orchard in
Laser Scanner laser scanner was used to scan the 3D point cloud of this winter peach
Okayama.
A3 shows
image of image
the experimental
field in Okayama
orchard inFigure
Okayama.
Figure anA3orthophotoshows an orthophotoof the experimental
field inprefecture.
Okayama prefecture.

Figure(DigitalOrthographic
Orthographic
Map)
peach
tree object
labels (BF1–BF12)of experiFigureA3.
A3. DOM
DOM (DigitalMap)
andandpeachtree object
labels (BF1–BF12)of experimental
mentalin Okayama
prefecture.
field infield
Okayamaprefecture.

Remote
Sens.
2024,
16,
xxFOR
Remote
Sens.
2024,
16,
1570
Remote
Sens.
2024,
16,
FORPEERPEERREVIEWREVIEW

252523
ofofof2929
27

FigureA4.
A4. Comparison
Comparison between
between the
Figurethe canopy
canopy structure
structureparametersparametersmeasured
measuredusing
usingthetheproposed
proposed
FigureA4.
betweenstructureparameters
measuredusing
theproposedmethodandthe
SFM+MVS
method
and
the
truth
value
based
on
a
winterfruit
tree
canopymethodandthe
SFM+MVS
method
and
the
truth
value
based
on
a
winterfruit
tree
canopywithout
method and the SFM+MVS
winter fruit tree canopywithout
without
leaf
cover.
(a)Comparison
of
CH.
(b)
Comparison
of
CV.
(c)
Comparison
ofofCW.
(d)Comparison
ofof
leaf
cover.
(a)Comparison
of
CH.
(b)
Comparison
of
CV.
(c)
Comparison
CW.
(d)Comparison
leaf(a) Comparison of CH. (b) Comparison of CV. (c) Comparison of CW. (d) CompariCPA.
CPA.
sonof CPA.

Figure A5. Cont.

RemoteSens.
Sens.2024,
2024,16,
16,1570
x FOR PEER REVIEW
Remote26 of 29
24 of 27

FigureA5.
A5.Comparison
Comparisonofofthe
thelinearlinearcorrelations
correlations
betweencanopystructureparameters
measFigure
betweenthethecanopystructureparameters
measured
ured
using
the
proposedmethodandthe
SFM+MVS
method
and
their
truth
valuesbasedonausing the proposed method and the SFM+MVS method and their truth values based on a winterwinfruit
ter fruit
treewithout
canopy leaf
without(a) Comparison
CH. (b) Comparison
CV. (c) Compartree
canopycover.leaf(a)cover.
Comparisonof CH. (b)ofComparisonof CV. (c) of
Comparison
of CW.
ison of CW. (d) Comparison of CPA.
(d) Comparison of CPA.
Table A2.
A2. Error
Error rates
rates (absolute(absolute values)
values) between the canopy structure
Table
structure parametersparameters measured
measured using
using
proposed method
method andand thethe SFM+MVS
SFM+MVS method
proposedmethod andand thethe groundground truth
truth based
based onon aa winterwinter fruit
fruit tree
tree
canopywithout
leaf
cover.
canopy without leaf cover.

Tree_Num
Tree_Num
BF1
BF1
BF2
BF2
BF3
BF4
BF3
BF5
BF4
BF6
BF5
BF7
BF6
BF8
BF7
BF9
BF8
BF10
BF9
BF11
BF12
BF10
Average
BF11
BF12

Tree_Num
Average
BF1
BF2
Tree_Num
BF3
BF1
BF4
BF5
BF2
BF6

GT
GT
4484.98050
4484.98050
4331.69937
4331.69937
4360.79979
4746.90247
4360.79979
4175.70496
4746.90247
4905.89905
4175.70496
4717.59796
4905.89905
4128.70026
4717.59796
5077.59857
4128.70026
4382.09534
5077.59857
5107.99408
4266.70075
4382.09534

Our Method
Our Method
4618.509
4618.509
4424.6815
4424.6815
4502.2321
4681.1086
4502.2321
4092.5167
4681.1086
4851.6634
4092.5167
4559.8403
4851.6634
4195.3521
4559.8403
5185.1781
4195.3521
4432.1185
5185.1781
5226.1024
4217.5366
4432.1185

5107.99408

5226.1024

4266.70075

GT
136,229,237,885
136,613,113,912
GT
147,484,106,788
136,229,237,885
139,481,684,081
114,622,326,464
136,613,113,912
205,824,240,590

CH (mm)
CH (mm)SFM+MVS
SFM+MVS5169.901
5169.901
4073.0618
4073.0618
3770.4123
5517.4742
3770.4123
4544.5342
5517.4742
5298.6295
4544.5342
5621.1942
5298.6295
4171.5054
5621.1942
5536.7487
4171.5054
5041.5632
5536.7487
4316.4606
4173.9611
5041.5632
4316.4606

3)
CV (mm4217.5366
4173.9611
Our Method
SFM+MVS133,655,821,175
94,728,440,093
CV (mm3 )
131,467,520,473
89,458,954,398
Our Method
SFM+MVS
148,983,106,913
97,456,201,958
133,655,821,175
94,728,440,093
137,920,598,688
85,926,643,851
106,985,450,087
75,644,170,444
131,467,520,473
89,458,954,398
191,391,325,177
127,680,818,551

Error_Our Method Error_SFM+MVS
Error_SFM+MVS
2.977%
15.271%2.977%
15.271%2.147%
5.971%
2.147%
5.971%
3.243%
13.539%1.386%
16.233%3.243%
13.539%1.992%
8.833%
1.386%
16.233%
1.106%
8.005%
1.992%
8.833%
3.344%
19.154%
1.106%
8.005%
1.614%
1.037%
3.344%
19.154%2.119%
9.043%
1.614%
1.037%
1.142%
15.049%2.119%
9.043%
2.312%
15.496%
1.152%
2.174%
1.142%
15.049%2.044%
10.817%2.312%
15.496%

Error_Our Method

1.152%

error_Our Method
2.044%
1.889%
3.766%
error_Our
Method
1.016%
1.889%
1.118%
6.662%
3.766%
7.012%

2.174%

error_SFM+MVS
10.817%30.464%34.517%
error_SFM+MVS
33.921%30.464%38.395%34.006%34.517%37.966%

Remote Sens. 2024, 16, 1570

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Table A2. Cont.
CV (mm3 )Tree_NumGT

Our Method

SFM+MVSerror_Our Method

error_SFM+MVS

BF3

147,484,106,788

148,983,106,913

97,456,201,958

1.016%

33.921%

BF4

139,481,684,081

137,920,598,688

85,926,643,851

1.118%

38.395%BF5114,622,326,464

106,985,450,087

75,644,170,444

6.662%

34.006%BF6205,824,240,590

191,391,325,177

127,680,818,551

7.012%

37.966%

BF7

152,807,118,136

155,486,208,774

85,291,725,418

1.753%

44.183%

BF8

115,944,151,016

112,997,614,846

73,508,895,423

2.541%

36.600%

BF9

177,349,329,903

189,563,398,636

116,836,062,676

6.887%

34.121%

BF10

137,619,496,656

127,814,375,449

93,184,910,986

7.124%

32.288%BF11178,161,183,739

194,327,302,860

102,912,299,940

9.074%

42.236%BF12

110,476,314,241

101,518,577,788

78,535,122,836

8.108%

28.912%4.746%

35.634%

Average
CW (mm)Tree_NumGT

Our Method

SFM+MVSerror_Our Method

error_SFM+MVS

BF110034.82887

9808.0548

7927.4768

2.260%

21.000%BF210472.58835

9987.9103

8051.3289

4.628%

23.120%BF3

9698.44070

9220.4719

6987.5975

4.928%

27.951%

BF4

9485.52571

9202.128

7186.9222

2.988%

24.233%BF58848.54280

8674.1266

6989.1535

1.971%

21.014%BF610574.19065

10077.6282

7977.6013

4.696%

24.556%BF79685.14630

9508.4412

7049.7687

1.824%

27.211%BF88794.72770

8994.9784

6217.5931

2.277%

29.303%BF910297.73217

9877.8708

7934.2353

4.077%

22.952%BF109595.50221

9238.7074

6781.9227

3.718%

29.322%

BF1110089.13285

9845.3625

7600.5276

2.416%

24.666%BF129132.31919

9409.2268

6995.2876

3.032%

23.401%3.235%

24.894%

Average
CPA (mm2 )Tree_NumGT

Our Method

SFM+MVSerror_Our Method

error_SFM+MVS

BF1

51,407,108.77

49,109,894.98

32,082,868.96

4.469%

37.591%

BF2

55,990,107.86

50,927,517.27

33,093,170.3

9.042%

40.895%BF348,018,331.73

43,401,979.28

24,926,367.84

9.614%

48.090%BF445,933,130.88

43,229,456.74

26,368,724.64

5.886%

42.593%BF5

39,971,159.81

38,410,923.68

24,937,470.29

3.903%

37.611%

BF6

57,081,780.4

51,846,554.61

32,489,863.98

9.171%

43.082%

BF7

47,886,777.1

46,155,332.97

25,371,899.03

3.616%

47.017%BF839,486,444.75

41,305,081.9

19,735,486.28

4.606%

50.020%

BF9

54,136,032.3

49,811,534.49

32,137,596.21

7.988%

40.635%

BF10

47,004,415.65

43,573,822.86

23,480,584.78

7.298%

50.046%

BF11

51,964,998.49

49,484,212.18

29,491,082.82

4.774%

43.248%BF1242,576,053.49

45,197,156.4

24,981,262.74

6.156%

41.326%6.377%

43.513%

AverageRemoteSens.Sens.
2024,
16, 1570
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16, x FOR PEER REVIEW

28 of
26 29
of 27

FigureA6.
A6. Visualization
Visualization results
betweenthe thecanopystructureof fruit
Figureresultsofofthe
thecomparison
comparison
betweencanopystructureof trees
fruit retrees
constructed using
anditsitsterrestrial
Lidarscanning
reconstructedusing thethe proposed
proposedmethod
methodand
andSFM+MVS
SFM+MVSmethod
methodandterrestrial
Lidarscanning
point cloud (in the case of peach tree canopy No. BF3). (a) Structure of ground-based Lidar scanningpoint cloud (in the case of peach tree canopy No. BF3). (a) Structure of ground-based Lidar scanningpoint cloud. (b) Canopy point cloud structure reconstructed using the proposed method. (c) Canopy
pointcloud.
Canopyreconstructed
point cloud structureusing the proposed method. (c) Canopy
pointcloud(b)structureusing the reconstructed
SFM+MVS method.
point cloud structure reconstructed using the SFM+MVS method.
Table A3. Comparison of the time and number of images required to reconstruct the canopy strucTable A3. Comparison of the time and number of images required to reconstruct the canopy structure
ture of 12 fruit trees with SFM+MVS based on the canopy scene of fruit trees with no leaf cover inof winter(↓:
12 fruit trees
with SFM+MVS
based
on themethod
canopyscene oftofruit
trees withmethod).
no leaf cover in
The reduction
ratio of the
proposedcompared
the SFM+MVS
winter(↓: The reduction ratio of the proposed method compared to the SFM+MVS method).

MethodMethodSFM+MVS
Our
methodSFM+MVS
Our method

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Average Reconstruction
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Reconstruction Time
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01 h:33 m:04 s
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6 m (↓88.7%)30
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266 s
130

Reconstruction Time6 m (↓88.7%)

30 s (↓88.7%)

24 (↓81.5%)

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