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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume X-1/W1-2023
ISPRS Geospatial Week 2023, 2–7 September 2023, Cairo, Egypt

CHALLENGES AND RECOMMENDATIONS FOR 3D PLANT PHENOTYPING IN
AGRICULTURE USING TERRESTRIAL LASERS SCANNERS
Tomislav Medic1,* , Jonas Bömer2 , Stefan Paulus2
1
2

Institute of Geodesy and Photogrammetry, ETH Zurich, Zurich, 8093, Switzerland - tmedic@ethz.ch
Institute of Sugar Beet Research, Goettingen, 37079, Germany - (boemer, paulus)@ifz-goettingen.de

KEY WORDS: LiDAR, structural traits, geometry reconstruction, point clouds, terrestrial laser scanning

ABSTRACT:
Active sensing with LiDAR, and terrestrial laser scanners (TLS) in particular, are increasingly being used in plant phenotyping for
assessing structural or 3D geometrical plant traits. Although these technologies provide the unprecedented possibility for remote,
non-destructive, automatable, and efficient estimation of plant geometry, their deployment does not come without challenges. In this
publication, we present a systematic overview of all challenges impacting TLS-based 3D plant phenotyping. We provide actionable
recommendations for the end users of the technology, as well as the research questions and possible directions that can contribute
the most to resolving these challenges. We specifically focus on TLSs, as we detected a lack in the existing literature dedicated to
this sensing system providing a unique compromise between data quality and resolution vs. measurement efficiency and covered
volume. The presented discussions are based on the literature review and our own experience in estimating the structural traits of
sugar beet and wheat in plant phenotyping experiments.
1. INTRODUCTION
Plant breeding is a time-intensive process that can take up to 20
years to establish a new crop variety. With the onset of global
warming, there is an urgent need for more adaptable and resilient crop species that can cope with environmental changes. To
address this issue, agriculture is undergoing a digital revolution,
facilitated by the deployment of sensor technologies and advanced data processing pipelines (Araus and Kefauver, 2018).
Assessing structural or 3D geometrical traits such as the shape
and size of plants and plant organs is important for breeders to
evaluate physical adaptations to changes in their genetics, environment, and management practices. To enable that, scientists
and industry are developing strategies to quantify observable
cues related to the plant structure. Remote sensing gained a
particular momentum as it is non-destructive, remote, and with
the possibility of automation.
Strategies for observing plant geometry can be primarily divided into passive and active sensing, where passive sensing
relies primarily on RGB cameras and structure from motion
workflows. It is better established than active sensing, primarily
due to the low cost, high availability, and high developmental
stage of RGB cameras and image processing allowing for e.g.
high acquisition speed and resolution. However, active remote
sensing has some compelling advantages. For example, active
remote sensing with LiDAR (Light Detection and Ranging) and
different scanning platforms; e.g. static and mobile terrestrial,
UAV and airborne; demonstrated compelling potential for 3D
phenotyping, due to different and often complementary characteristics to cameras. LiDAR is: 1) illumination invariant and
well suited for diurnal observations, 2) plant structure can be
directly estimated from observed point clouds with high accuracy, 3) it provides accurate information on the vertical distribution of data samples, 4) gives more information about lower
canopy structure and 5) information about soil (Jin et al., 2021).
Terrestrial laser scanners (TLSs) are portable stationary LiDAR
systems with the possibility of achieving the highest meas__________________
* corresponding author

urement quality and resolution of all LiDAR systems, while
still retaining reasonable data acquisition efficiency. They are
present in plant phenotyping since their appearance in the early
2000s and they had an eminent role, especially in the field of
forestry (Jin et al., 2021). They are widely available, allowing
for worldwide applicability necessary for untangling genotypeenvironment-management interaction. Despite mentioned advantages of active sensing with LiDAR, its deployment in plant
phenotyping comes with certain challenges. Existing literature
reviews discuss some of these challenges in a general case of
LiDAR technology, e.g., (Jin et al., 2021), missing specificities
of the TLSs. Hence, our work provides a systematic overview
of the challenges of using TLSs for 3D plant phenotyping in agricultural applications, together with recommendations on how
to mitigate some of them. The work is based on our own experiences of using TLSs and other scanning systems for the phenotyping of sugar beet and wheat. The main goal of the article is
to elucidate bottlenecks that require further research efforts and
to give clear recommendations to the end users.

2. STATE OF THE ART
Current 3D measuring devices enable the parameterization of
plant properties related to their geometry, either as indirect
measurements of shape and size or as descriptors of the overall
structure, such as leaf area and canopy volume. These parameters can be linked to the main biological traits of interest like
biomass, yield, and plant stress reaction (Paulus et al., 2014c).
Commonly, 3D measuring devices come with the trade-off of
reachable resolution and data quality vs. measurable volume
and throughput. As plants depict a permanently deforming object - the change in size has to be taken into account, as well
as the needed resolution for plant organ description and needed
throughput for assuring generalizable results for a plant population (Paulus, 2019). Various active sensing technologies can
be used to measure the 3D geometry at different scales in the

This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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lab, the greenhouse, and the field, but each comes with its own
advantages and limitations.
On one side, there are laborious and accurate measurements
in controlled laboratory conditions with laser triangulation and
structured light scanners capable of estimating, e.g. exact
shape and size of strawberry flower calyx (Paulus, 2019). On
the other side, there are highly efficient moving platforms for
high-throughput in-field plant phenotyping such as UAVs and
ground-based robots often equipped with automotive LiDAR,
capable of estimating crop height and biomass at large scale (Jin
et al., 2021). The measurement quality and volume of these two
phenotyping strategies are drastically different. Laboratory-like
measurements are hardly adaptable for higher throughput measurements, e.g. of numerous plant units in the greenhouses, and
they cannot be used for in-field phenotyping in an uncontrolled
environment. On the other hand, mobile mapping platforms and
robots allowed unprecedented efficiency. However, the number
of plant traits that can be extracted from these measurements
is drastically lower than in the case of laboratory analysis, and
their quality is inferior.
Trait Group
Canopy height
Canopy geometry
Plant height
Plant geometry
Stem length
Stem geometry
Tillers geometry
Foliage geometry
Leaf geometry

Fruits geometry
Fruit geometry

Specific Traits
Canopy height
volume, size, shape, stem
count, density
Plant height
PAI, PAD, volume, surface
area, PAVD, plant skeleton
Stem length
stem diameter, volume, curve,
structure, basal area
tiller count
PLA, LAI, LAD, LA
length, width, area, inclination
angle, azimuth angle, leaf orientation, height of leaf position
ear count, panicle detection
ear size, panicle length, width

Case
F
F
G,F
G,F
L,G,F
L,G,F
F
G,F
L,G

G,F
G,F

Table 1. Literature summary of structural traits observed by
TLSs for: L-lab, G-greenhouse, F-field (PAI, PAD, PAVD-plant
area index, density, and volume density; PLA-plant leaf area;
LA, LAD, LAI- leaf area, density and index).

TLSs with their following characteristics lie somewhere
between these two poles: measurement accuracy, spatial and
temporal measurement resolution, related sensitivity towards
detecting subtle changes, and measurement efficiency. A TLS
device combines the advantage of a resolution of up to 1 mm
at distances of 10 m, measurement accuracy on a level of a few
millimeters, and a measurable volume and reach from tenths of
meters up to kilometers (Vosselman and Maas, 2010). Because
of that, TLSs are successfully utilized for highly demanding
laboratory phenotyping, greenhouse phenotyping with a high
amount of plants, and without an automated plant mover or convey belt, as well as for in-field phenotyping (Jin et a l., 2021).
By now, an abundance of relevant structural traits was extracted
from TLS point clouds (Tab. 1).
Generally, for in-field u se, T LS p oint c louds a re primarily
utilized for estimating canopy height, growth rates, and other
canopy-related properties (Friedli et al., 2016). For larger species, such as maize, the automatic extraction of traits describing
the geometry of individual plants or their organs was demonstrated as well (Jin et al., 2021). More challenging plant types

are primarily investigated in a higher level of detail in the lab
or in the greenhouse experiments, with significantly reduced
measurement volume (Paulus, 2019). Acquiring this information from TLS point clouds is not trivial, there are no standardized approaches, and further progress would enhance information extraction. Each TLS-based phenotyping study tackles the
related challenges in a specific way, and the complexity varies
depending on the use case. However, some of the challenges
are common. Hence, in the following section, we present a
structured overview of these challenges, focusing on the generalizable aspects, possible remedies, and further work necessary
to resolve them.
3. CHALLENGES AND RECOMMENDATIONS
Many of the trait extraction workflows presented in the literature presume unobstructed access to each individual plant from
multiple different viewpoints, from relatively high proximity (a
few meters distance or less), with no strong time limitations for
scanning (Lumme et al., 2008). This assures that the acquired
point clouds have high resolution, high redundancy of data, almost complete coverage of each individual plant, and the most
favorable scanning geometry allowing for minimal uncertainty
related to the measurement process. However, high throughput phenotyping with an increased measurement volume in the
greenhouse or field lacks that luxury. Wider areas with more
plants need to be covered in a limited time. This requires compromises, e.g. reduced resolution and non-resolved occlusions.
Making these compromises poses challenges, as most of the
state-of the-art algorithms are developed for working with ideal
or close-to-ideal point clouds.
Fig. 1 shows the 2D case of the ideal point cloud and all deviations that occur in TLS-based 3D plant phenotyping: variable noise due to measurement uncertainty and its functional
relationship with measurement configuration and the intensity
of the laser beam (Vosselman and Maas, 2010); missing data
due to mutual occlusion of plants; non-uniform sampling due
to scanning pattern; outliers due to mixed pixels and motion;
re-occurrence, fracturing, and non-rigid transformations due to
point cloud registration errors and plant movements.
Standard solutions for some of these distortions are adequate
for the lab or greenhouse measurements with higher data redundancy. They are commonly implemented in dedicated point
cloud processing solutions, like open source software CloudCompare, or point cloud libraries, like Open3D and PCL (Point
Cloud Library) for Python. For noise reduction, there are different strategies relying on local surface fitting or simultaneously denoising and subsampling (Zhou et al., 2022), while
for outlier removal, the standard algorithms are StatisticalOutlier-Removal (SOR) and Radius-Outlier-Removal (ROR).
The problem of non-uniform sampling can be reduced by regular spatial subsampling. However, these algorithms work only
in a case of sufficient resolution, redundancy, and data quality.
For more challenging use cases and other distortions, such
standard solutions are lacking. However, understanding the
causes of the distortion can be used for implementing measurement and data processing strategies that can mitigate them.
Fig. 2 summarizes the main causes of distortions, the effects
they have on the point clouds, and some remedies that can be
used to mitigate these effects. They are primarily grouped into
causes related to the data acquisition principles (blue), properties of the laser beams (orange), and plants (green).

This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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maintaining as uniform as possible point cloud properties is important as many of the point cloud processing algorithms have
tunable hyperparameters that cannot be optimally selected for
such strong variations within a scene.

Leaf
geometry

Missing
data

Ideal
point cloud

Variable
noise

Noise

Reoccurrence

Outliers

Fracturing

Non-uniform
sampling

Non-rigid
transform.

Figure 1. Sketch of a targeted leaf geometry, ideal point cloud
and all deviations from ideal in TLS phenotyping.

However, they are all interrelated and multiple causes have
comparable and overlaying effects on point clouds. This in turn
means that certain solutions can account for multiple causes,
resulting in relatively complex ties between all the elements in
the graph. Hence, the separation presented in Fig. 2 is neither
strict nor definite, a nd t he m ulticolor fi elds in dicate multiple
connections between the elements. The following subsections
will discuss the individual causes and related coping strategies.
3.1

Scanning pattern and measurement configuration

One of the main causes of TLS point cloud deviations (Fig. 1)
is the combination of scanning pattern and suboptimal measurement configuration. T LSs a re c ommonly m ounted o n tripods aside and somewhat above the areas that are densely populated with plants (Fig. 3, left). Such configuration h as undesirable consequences. One issue is varying distances and
angles of incidence (AOI) across the measurement volume,
reaching greater than desired values for both quantities (Fig. 3,
middle). Another is that different plants are sampled with different levels of detail, as TLSs scan with a fixed a ngular resolution, causing irregular sampling in Euclidean space (Fig. 3,
right). However, such configurations a re o ften n ecessary for
in-greenhouse and in-field use due to the often limited mounting possibilities. The combination of this unfavorable measurement configuration and scanning pattern is the direct cause of
the majority of the deviations presented in Fig. 1. High distances and AOIs cause higher noise, lower resolution, and an
increase in the laser beam footprint size (Vosselman and Maas,
2010), which also increases the occurrence of mixed pixels
(Sec. 3.3). Their substantial variation causes variable noise and
non-uniform sampling density. Finally, high AOIs also cause
more missing data due to higher occlusion between the plants,
which especially comes prominent in the later growth stages.
One simple recommendation to minimize these effects is to
limit the maximal magnitude and variability of the distances
and AOIs, either during measurements or in point cloud preprocessing. There is no exact recommendation, as the optimal
solution depends on the variables such as measured volume,
scanning resolution, and laser beam footprint size. However,

Secondly, the TLS is ideally placed upside down above the
plants at viewpoints with regular spacing, using a dedicated
mounting platform instead of a tripod. This solution requires
additional investment in the implementation phase but significantly improves the resulting point cloud quality. This assures
on average smaller distances and AOIs, their smaller variation
within the scene, fewer occlusions, and it reduces the problem
of the mixed pixels. One such example solution is the field
phenotyping platform (FIP) (Kirchgessner et al., 2016).
Advanced solutions requiring further scientific efforts can be
separated into efforts focused on data acquisition and data processing. Regarding data acquisition, it is possible to pose
the viewpoint planning problem as a multi-target optimization problem aiming at minimizing undesired extreme values
and variations of measured distances, AOIs, and point spacing. Works solving similar optimization problems are presented for 2D cases of scanning urban environments (Jia and Lichti,
2022). However, TLS-based plant phenotyping would require
further efforts due to the higher complexity of the observed
scene and the information that needs to be retrieved. Such optimizations could be based on end-to-end pipelines combining
3D TLS point cloud simulations, trait extraction algorithms,
and heuristic optimization. The building blocks for such approaches are already available, e.g. Crops in Silico project
(Marshall-Colon et al., 2017) for simulating 3D plants and Helios project for LiDAR simulations (Bechtold and Höfle, 2016).
Solutions based on data processing require adopting machine
learning (ML) algorithms that can learn stochastic properties
of the point clouds and locally adapt the surface reconstruction or plant traits extraction. Developing such algorithms is a
part of the current research efforts. However, existing solutions
are only demonstrated for simplified CV problems usually tackling a subset of the complications presented in Fig. 1. For example, the algorithms commonly tackle separately the problems
of shape completion, denoising, upsampling, and 3D reconstruction and they are demonstrated on small-scale point clouds
of individual objects with unrealistic properties (e.g. zero mean
Gaussian noise). Their transfer to real-world datasets considering scale and complexity, as well as adaptation for the plant
phenotyping domain (as they are typically over-fitted for a particular task to improve the results) is yet to be achieved. A few
exemptions of such solutions already applied for plant phenotyping will be presented later in the text.
Finally, scanning from mobile platforms, primarily UAVs, allow more homogenous and favorable measurement configurations and scanning patterns. Hence, if this was found to be a
limiting factor in a particular plant phenotyping task, a such
alternative could be more desirable. However, current state-ofthe-art mobile mapping platforms provide at least an order of
magnitude lower point cloud quality and resolution than static
TLSs (Jin et al., 2021).
3.2

Incomplete coverage and occlusions

As TLSs are stationary portable platforms, the incomplete coverage of 3D measurement subjects such as plants is unavoidable
from a single viewpoint. Additionally, in many plant phenotyping cases, plants are densely populated creating occlusions and

This contribution has been peer-reviewed. The double-blind peer-review was conducted on the basis of the full paper.
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Challenges
Effects

Coping strategies

Specific recommendations

Scanning pattern

Causes

Data gaps

Variability restriction

Limiting (variability in) angles of incidence and distances

Measurement configuration

Non-uniform sampling

Viewpoint optimization

Incomplete coverage

Re-occurrence

Heuristic optimization: 3D plants and LiDAR simulations +
automatic trait extraction -> minimizing trait errors

Occlusions

Fracturing

Dedicated infrastructure

Optimized target fields and TLS mounting platform

Appropriate sensor
alternatives (if applicable)

Depth cameras, top-down view mobile LiDAR

Non-rigid registration

Functional structural
Plant models (FSPM) or plant skeleton reconstruction

Scanner selection

By wavelength, speed, footprint size, resolution, data quality

Standard point cloud
pre-processing

Statistical outlier removal (SOR); Hidden Point Removal
(HPR); Mixed-pixel filtering by intensity, RGB, pulse deviation
from Gauss; spatially uniform sub-sampling; denoising by
local surface fitting

Machine Learning –
Computer Vision

Shape completion, surface reconstruction, RGB + LiDAR
integration, non-rigid and multi-body registration,

Laser beam size
Laser-matter interaction
External excitation
Plant egomotion

(Variable) noise
Outliers
Mixed pixels
Non-rigid-transformations

Figure 2. Challenges of TLS-based 3D plant phenotyping; grouped by causes related to the data acquisition principles (blue),
properties of the laser beams (orange), and of the measured subject, i.e. plants (green)

obstructions to lines of sight, making the data gaps unavoidable. Hence, scanning from multiple viewpoints is necessary
and it even helps to mitigate the negative effects of the unfavorable measurement configuration (Sec. 3.1). As the point clouds
taken from each viewpoint are stored in local coordinate systems (CS), it is necessary to perform registration and transform
them into a common global CS.
The errors in registration can cause re-occurrence and displacement (rigid transformation) of parts of the plants, causing fracturing (Fig. 1). Hence, they need to be minimized to avoid systematic biases in extracted geometrical traits. A typical environment in plant phenotyping is unstructured and repetitive, it
changes over time and, in the case of in-field scanning, it is
without clear landmarks. Such an environment is unfavorable
for use of the common point cloud registration approaches, such
as the Iterative Closest Point (ICP) algorithm, as they are prone
to get stuck in local minima. Hence, a common solution is to
use a network of stable and dedicated scanning targets, which
will serve as corresponding points and aid registration, e.g. in
(Kirchgessner et al., 2016).
However, designing such a network assuring high accuracy is
not a trivial task. Many decisions have an impact on the end result, from the selection of the right targets to the network design.
For the highest accuracy, it is beneficial to use black-and-white
planar targets. Also, selecting the right target design and algorithm for center estimation, and assuring a minimum of 100
points per target can further improve the results (Janßen et al.,
2019). Moreover, allowing for a minimum of 4 targets visible in
each scan is a good practice that assures sufficient redundancy,
and it is advisable that visible targets at each viewpoint enclose
a large surface area or 3D volume (Yang et al., 2020).
Advanced solutions for registration will require the development of dedicated algorithms for such repetitive scenes with
geometrically complex and dynamic objects. This will likely
require some form of scene understanding and prior knowledge,
e.g., scene segmentation into stable regions related to soil and
unstable regions related to plants and then minimizing different optimization goals for each region. An example of such a
solution for UAV data can be found in (Günder et al., 2022),
where datasets are registered using locally unique sowing pat-

terns in the field due to specific aberrations like missing plants
or increased or decreased plant or row spacing.
Alternatively, the accuracy of the registration and number of
viewpoints could be reduced if intelligent end-to-end plant
traits extraction algorithms could handle missing data, point
re-occurrence, and fracturing. This could become possible if
sufficient prior knowledge is encoded in the ML models by
providing adequate training data. Such an approach was already
demonstrated in the agricultural domain for RGB-D cameras.
Using a pre-trained fully connected neural network based on
encoder-decoder architecture, the authors were capable of inferring a complete 3D shape of bell peppers and strawberries
using only partial coverage of the fruit (Magistri et al., 2022).
If a comparable approach could be derived for TLS point clouds
is yet to be seen.
3.3

Laser beam size

As the 2D cross-section of the laser beam is not infinitely small,
a single laser beam interacts with the measured object over
a certain surface area, often referred to as a laser beam footprint. Depending on the instrument properties, distance, and
AOI, the footprint can have different shapes and sizes ranging
from a minimum of several millimeters up to some centimeters
in diameter for common plant phenotyping settings. This phenomenon induces variable noise, and limits the size of the smallest observable plant detail. However, the main challenge for
geometrical reconstruction arises when the laser beam hits more
than one surface at the same time, causing mixed pixels (or
ghost points) that fall somewhere in between two sampled surfaces (foreground and background) and do not describe any real
object. For example, Fig. 4 presents the extreme case of mixed
pixels in the case of in-field wheat phenotyping, where the laser
beams sample at the same time leaves (foreground) and the soil
(background) with different proportions.
As the footprint size grows, mixed pixels form a much higher
percentage of total points in the point cloud, which poses two
challenges. First, the algorithms for outlier removal based on
analysis of local point neighborhoods (e.g. SOR) are less likely
to detect mixed pixels and remove them. Second, a successful
removal induces large data losses, which can bias the extracted

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0

Distance [m]

29

33

Incidence Angle [°]

89 33'000 NN in 0.1 m radius

131

Figure 3. Properties of wheat phenotyping TLS point cloud (single viewpoint): distances, angles of incidence, and number of
neighbors in 0.1 m radius (sampling density).

plant traits. For example, as mixed pixels occur on the edges
of the measured surfaces, the estimated leaf size can become
systematically too small after mixed pixel removal.
If the mixed pixels sample plant and soil, rather than two
neighboring leaves, there is a higher chance of detecting them.
Namely, multi-return (echo) time-of-flight scanners are capable
of separating the signal of a single laser beam on two or more
points belonging to different surfaces. However, they are capable of such separation only on the foreground-background distances higher than 0.75 m (Vosselman and Maas, 2010), hence,
making this strategy valid only for taller plants and later growth
stages. Nevertheless, such scanners also often provide a measure of how much the return signal deviates from the expected
(Gaussian) distribution, which is notably higher for the mixed
pixels. This can be well utilized for mixed-pixel filtering. TLSs
with other measurement principles have alternative mixed-pixel
removal strategies that are available in the manufacturer’s software during data import or pre-processing. Their mechanism
is not disclosed, and their success rate is variable. Moreover,
using spectral data, such as laser beam intensity or RGB values
(in the case of the colored point clouds), can help in identifying
mixed pixels, especially in the plant-soil case.
Simple remedies to mitigate the mixed-pixel effect are: to limit
the measurement distance and AOI; select an instrument with a
small footprint size and good mixed-pixel filtering; and adjust
measurement configuration so that it assures the highest foreground and background distance (often top-down view). More
advanced solutions, allowing recovery of sharp object edges
and not the removal of the mixed pixels, will require substantial
further development in point cloud processing algorithms. They
will likely need to rely on ML deconvolution, as the problem is
too complex for established signal processing strategies. Examples of such deconvolution algorithms can already be found
in CV domain. For example, in (Xiang et al., 2021), the authors presented a deconvolution algorithm based on transformer
network architecture capable of reconstructing sharp edges of
simple individual objects with high fidelity. Comparable approaches could potentially be used to tackle mixed-pixel problems in the agricultural domain.
Alternatively, an indispensable source of information for this
task are RGB images of integrated onboard cameras, as the
surface edges are well-preserved. Hence, a reconstruction of
edges distorted by mixed pixels could be possible by inferring
the correct shape from images. Early implementations of such
solutions for RGB-D are already present in CV. In (Metzger et
al., 2022), the authors used an approach that combines guided

Figure 4. TLS point cloud of wheat: line-of-sight direction (top),
side view exposing mixed pixels in black rectangle (bottom)

anisotropic diffusion with a deep convolutional network to obtain super-resolution depth (D) images with sharp edges from
initial low-resolution D and guiding RGB images. The results
were presented on arbitrary scenes, hence, it is to be investigated if the approach is directly applicable to plant phenotyping
using TLSs with high-quality integrated RGB cameras.
3.4

Laser-matter interaction

The laser beam has a complex interaction with plant tissue, resulting in partial surface reflection, a nd p artial p enetration followed by absorption or transmission of the light in various proportions. For example, studies show that laser scanners are able
to measure through the epidermis (Paulus et al., 2014b). This
phenomenon has an influence on the quality of geometrical reconstruction, and the exact impact is specific to the properties of
a used laser scanner and plant material. Depending on the laser
wavelength, more or less of the energy passes the epidermis and
is absorbed by the chlorophyll, which influences the intensity,
and, hence, the measurement noise.
According to the characteristic plant spectra, the highest reflectance is available in the green a rea 550nm and in the NIR
area > 780nm. Hence, the TLSs with the wavelengths in the
mentioned spectrum are preferable for 3D plant phenotyping.

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The systematic bias due to laser penetration is commonly on
the below-millimeter level (Dupuis et al., 2015), while the increase in the noise level can be an order of magnitude larger,
depending on the amount of the absorbed light. Hence, if the
most unfavorable wavelengths are avoided, this effect is primarily concerning high accuracy demanding in-lab cases.
Related phenomena worth mentioning are water bodies, which
act as semi-transparent specular mirroring surfaces, causing
either a total reflection of the laser beam in the direction of the
line of sight or away from the TLS’s photodetector; or strong
absorption of the laser beam (Vosselman and Maas, 2010).
Because of that, scanning wet surfaces can induce increased
measurement noise, outliers, or data gaps (Fig.1). Hence, it
is strongly advisable to avoid scanning plants when they are
notably saturated with water droplets, e.g. due to rain or dew,
which particularly concerns field phenotyping.
3.5

External excitation

One of the most detrimental factors to point cloud quality is
plant movement by external influences. Person movements,
moving devices, and particularly wind cause air motion and this
results in a position change of the highly sensitive plant leaves.
This can only be neutralized by the exclusion of fast external
movement, wind, and air draft, which is feasible in some cases
of in-lab and in-greenhouse plant phenotyping but is often unachievable. The effects on the point cloud quality are multiple,
covering the whole range presented in Fig. 1.
The main problem is that the time required for a single scan is
relatively long, often spawning through several minutes to capture the desired scene. Hence, one solution in severe cases is to
consider alternative geometry capturing technologies, capable
of covering the whole plant(s) with a single snapshot. The main
candidate is using high-quality depth cameras, which allow for
degraded, but still comparable (millimeter-level) data quality
(Frangez et al., 2022). However, they only cover distances of
several meters and are only suitable for indoor or overnight use,
as they are sensitive to external illumination.
The only simple recommendations for the end-users are: selecting faster-scanning instruments, carefully selecting measurement time windows, using wind sensors to monitor the airflow, and repeating the measurements if a certain threshold is
breached during a single scan. More advanced solutions will
require domain-adapted ML approaches that will be able to correct point clouds based on some additional source of information. This could be: RGB images that are taken while scanning
(see Sec. 3.3), scans from the preceding or following epochs
(more in Sec. 3.6), or prior knowledge encoded in the ML models (as the example in Sec. 3.2).
3.6

Plant egomotion

Plants are dynamic objects, moving over time due to growth, or
physiologically reacting to changes in the environment such as
sun-angle alterations due to heliotropism or variations in gasses
and humidity concentration in the air due to adaptable turgor.
Often the goal is to understand these plant dynamics, or at least
to minimize the impact of these movements on the captured
geometry. Comparably to the external excitation, this requires
high data acquisition speed and increased temporal resolution.
If this is not met, the effects such as the re-occurrence and nonrigid transformations can affect the point cloud quality (Fig. 1).

An example of points re-occurrence due to plants’ own movement or egomotion is presented in Fig. 5 for sugar beet leaf
movement over the course of 1 h. We can observe two distinct
cases, one where the point clouds of two epochs that are close
in time (10 min) are still partially overlapping, and one where
there is a clear separation between them. These two cases require different treatments. The first case (Fig. 5, c) can happen
within ”a single measurement epoch” while capturing a complete point cloud from multiple viewpoints. In such a case, it
will often be desired and advisable to treat a point cloud as a
single surface. To reconstruct a surface without bias due to
point re-occurrence, it is possible, for example, to eliminate
the redundant points using the Hidden Point Removal algorithm
(Katz et al., 2007). In the second case (Fig. 5, d) it is necessary
to treat the point clouds as separate measurement epochs and
perform adequate point cloud registration.
There is an unavoidable trade-off between temporal resolution vs. point cloud quality, spatial resolution, and measured
volume, as shorter measurement epochs require fewer viewpoints and shorter scanning times. So planning an adequate
measurement setup considering plant egomotion requires prior
knowledge of plant behavior and desired plant traits to be extracted. Only then setup design can be posed as a well-defined
optimization problem and can be resolved, e.g. with the approaches discussed in Sec. 3.1. Simple recommendations for
the end users, apart from complete setup optimization, are again
selecting instruments with high measurement rates, and adaptation of scanning resolution based on the traits of interest. For
example, canopy and foliage-related traits (Tab. 1) typically do
not require high spatial resolution.
More advanced solutions for increasing temporal resolution
without information and quality loss would require algorithms
capable of fusing sparse low-resolution but frequently taken
point clouds with RGB images. Examples of such solutions
exist in other domains. For example in (Zhu et al., 2023), the
authors fused sparse and low-frequency LiDAR data with high
resolution and frequency RGB images using new network architecture for simultaneous scene-flow estimation and sensor
data integration. The approach was used in geomonitoring,
for tracking mud and debris flow. Comparable solutions could
be derived for plant phenotyping. Alternatively, there are algorithms capable of inferring plant structure in the intermediate
steps between actual measurement epochs, however currently
only in 2D (Yasrab et al., 2021) relying on GANs (Generative
Adversarial Networks). Such solutions would allow asynchronous observations of different plants, increasing the temporal
resolution without loss in comparability, which is relevant for
TLS-based plant phenotyping due to limited acquisition speed.
Another issue related to plant egomotion is the registration of
point clouds from different epochs in a way that they can cope
with changes in the shape of each individual plant and infer
the traits such as leaf area or inclination change as a function of
time. Time series measurements can not be simply mapped onto
each other to measure differences in plant traits. As the plants
move, defining correct point correspondences, and hence, tracking the corresponding parts of the stems, leaves, and other plant
organs is not trivial. Therefore, the correspondences have to be
established and plants have to be semantically and geometrically modeled (Paulus et al., 2014a), e.g. their organs need to
be identified, segmented, and parameterized so that the plant
can be represented as a system of elementary parts that can be
uniquely identified and traced over time.

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There are several ways to approach this issue. The first one
is a reconstruction of plants using models, such as functionalstructural-plant-models FSPM (Henke et al., 2016). Such approaches are advantageous as they spare steps of plant organ
classification and parameterization (Paulus et al., 2013) as the
models themselves provide organ affiliation. The second one is
by plant skeleton reconstruction, detecting corresponding keypoints, e.g. tips of the leaves, and mapping them between the
epochs using non-rigid transformation equations (Chebrolu et
al., 2021). These algorithms are proven to work well with
nearly ideal plant point clouds, so it is necessary to take appropriate measures to mitigate all point cloud distortions from
Fig. 1 to assure a higher success rate.
Finally, with some additional scientific efforts, emerging ML
approaches could be re-applied for tracking plant dynamics.
The applicable approaches are the ones providing the solutions
to the scene-flow estimation problem with some adaptations,
e.g. ones capable of simultaneous segmentation and rigid-body
motion estimation of different scene parts (Huang et al., 2021)
or ones describing non-rigid-body registration (Huang et al.,
2022). The latter algorithms were demonstrated on humanmotion tracking, which could be directly transferable to plant
phenotyping, and tracking the behavior of movable household
objects, which is an overly simplified task for a direct transfer. Such approaches could even help improve corrupted scan
epochs by inferring the true plant structure from other epochs
through spatiotemporal 3D modeling.

of interest) are defined and the properties of the measurement
equipment and measurement subject are fixed and known.
There is evidence in the literature that the breakthrough in
resolving the challenges will primarily come from adopting
emerging solutions for point cloud processing based on machine learning developed by the computer vision (CV) community. The biggest benefits can be expected from algorithms
dedicated to shape completion, surface reconstruction, RGB +
LiDAR data integration and non-rigid and multi-body registration (spatiotemporal 3D modeling).
However, adopting these solutions poses a significant challenge
on its own due to the necessity of bridging a domain gap. It requires collecting an abundance of diverse datasets with ground
truth information for training and fine-tuning the algorithms,
which is either costly or even completely infeasible to acquire
in the case of 3D plant phenotyping. Hence, bridging the gap
will almost certainly require further development of domainspecific synthetic data generation relying on the projects such
as Crops in Silico and Helios LiDAR simulations.
ACKNOWLEDGMENTS
This work was supported by an ETH Zurich Postdoctoral Fellowship and partially funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy - EXC 2070 – 390732324.

4. CONCLUSION
This article provides a systematic overview of the challenges
related to 3D plant phenotyping using TLSs. We give a simple
and actionable recommendation to the end users that can partially mitigate them and suggest research directions that can
further help overcome these challenges. The insights presented in this article are based on the literature review and own
experiences in sugar beet and wheat phenotyping.
The causes of these challenges were classified into three main
categories related to the data acquisition principle, properties
of the laser beam, and plant behavior. The three specific challenges that have the highest impact on the quality of the point
cloud and the subsequent estimation of structural traits are:
non-uniform point cloud properties due to specific scanning
patterns; mixed pixels due to an unfavorable ratio of the laser
beam footprint size and the size of plant structural elements; the
external excitation causing sudden plant motion. These challenges can be seen as general constraints of TLS. However, their
impact on the results of plant phenotyping is notably higher
than in the other application domains. For example, in 3D representation of urban environments the objects are commonly
static and the elements of the surrounding are typically much
larger than a laser beam footprint size, reducing the relevance
of mixed-pixels and non-uniform scanning pattern.
The main actionable recommendations are careful TLS selection, investment in the dedicated supporting infrastructure, informed viewpoint planning focusing on achieving optimal distribution of top-down views, and restriction in the variability
of measurement configuration. The exact implementation of
these recommendations will depend on the necessary trade-off
of the following conflicting goals: spatial resolution, temporal
resolution, throughput or measurement volume, and data quality. These goals can only be correctly optimized once the exact objectives of plant phenotyping (e.g. exact structural traits

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</reference>

<statements>
1. The design contribution is therefore not to invent a new sensor, but to couple these methods into a supervisory digital twin that optimizes scan speed, viewpoint, focus, illumination and scheduling against a trait-uncertainty cost, while explicitly budgeting registration, mixed-pixel, laser-penetration, occlusion and benchmark-transfer errors that the literature quantifies only separately
2. The measured dependencies in the cited pipelines—stage speed, acquisition geometry, focus, illumination and camera height, and plant motion—support abstracting the controllable system as
3. where \(x\) is the platform and object state
4. plant motion is caused by wind, egomotion and physiological movement
5. wind and plant egomotion move leaves during scanning
6. plant reflectance is highest near 550 nm and above 780 nm
7. registration needs fiducial networks because ICP is prone to local minima in unstructured crop scenes
8. mixed pixels can be flagged by deviation from the expected Gaussian return distribution
9. Observability is a central limitation of single-view 3D grain phenotyping
10. A single stationary viewpoint produces unavoidable data gaps and occlusions, especially in dense canopies, and multiple viewpoints are needed to cover the object
11. Registration is another observability bottleneck
12. Point clouds from different viewpoints are stored in local coordinate systems and must be transformed into a common global frame; registration errors cause re-occurrence, displacement and fracturing of plant parts and therefore systematic biases in geometrical traits
13. ICP is prone to local minima in unstructured, repetitive, changing and landmark-free crop scenes, motivating a fiducial network of black-and-white planar targets with at least 100 points per target, at least four visible targets per scan, and targets enclosing a large surface area or volume
14. Advanced registration may require scene understanding, such as separating stable soil from unstable plant regions and optimizing each differently
15. The available evidence does not provide one consolidated per-trait variance decomposition, but it supplies separate quantified terms—placement and scanner error, speed-dependent point-cloud error, registration and mixed-pixel error, representation error and environmental thresholds—that can be assembled into a design budget.
16. ICP is prone to local minima in crop scenes
17. fiducial-network rules are needed for accurate registration
18. The controller should reject or re-plan scans when pose or registration residuals exceed thresholds
19. Mixed pixels increase with footprint size, defeat local-neighbourhood outlier removal, and their removal can bias traits such as making leaf size systematically too small
20. multi-return separation works only for foreground–background distances above 0.75 m
21. laser penetration gives below-millimetre systematic bias and can increase noise by an order of magnitude
22. Wavelength selection, mixed-pixel filtering and distance-aware acquisition are required
23. external excitation and plant egomotion degrade scans
24. mobile platforms provide at least an order of magnitude lower point-cloud quality and resolution than static TLS
25. Acquisition scheduling must be environmentally gated, especially in field settings
26. The error budget also has a benchmark-transfer caveat
27. Published shape-completion, denoising, upsampling and reconstruction results are often demonstrated on small-scale clouds with unrealistic properties such as zero-mean Gaussian noise and over-fitted models, so their transfer to real phenotyping data is unproven
28. A design report should therefore treat benchmark accuracy as a starting hypothesis, not as a guaranteed field error
29. The acquisition layer should use model predictive control over viewpoint
30. The separate cost terms can be drawn from the cited measurements: trait uncertainty from placement, registration, mixed-pixel and representation errors
31. \(U_{\text{trait}}\) is predicted trait uncertainty
32. The terms are populated from the evidence: \(U_{\text{trait}}\) can be estimated from placement error, registration residual, mixed-pixel score and representation bias
33. A reconstruction-quality observer should be added as a design contribution because the sources do not supply a single recursive estimator of point-cloud or trait quality, but they provide ingredients
34. Sampling density can be monitored by neighbour counts in a fixed radius
35. The observer could therefore output a scalar quality score and trigger re-scanning, viewpoint optimization, filtering or task cancellation
36. Are environmental limits respected? wind speed, rainfall, PAR, surface wetness
37. TLS guidance advises avoiding saturated leaves due to rain or dew
38. Mobile and UAV platforms can provide more favourable scanning geometry but give at least an order of magnitude lower point-cloud quality and resolution than static TLS
39. The literature supplies separate quantified terms—scanner accuracy [1][6], speed-dependent point-cloud error [2], placement error [1], registration rules and failure modes [13]
40. The literature supplies separate quantified terms—scanner accuracy [1][6], speed-dependent point-cloud error [2], placement error [1], registration rules and failure modes [13], mixed-pixel bias [13]
41. The literature supplies separate quantified terms—scanner accuracy [1][6], speed-dependent point-cloud error [2], placement error [1], registration rules and failure modes [13], mixed-pixel bias [13], laser-penetration bias and noise [13]
</statements>

Begin the assessment now. Output only the JSON list, without any conversational text or explanations.