Received: 23 October 2020

| Accepted: 4 May 2021

DOI: 10.1111/2041-210X.13645

PR ACTICAL TOOLS

EasyDCP: An affordable, high-­throughput tool to measureplant phenotypic traits in 3D
Alexander Feldman1
Seishi Ninomiya1,2| Haozhou Wang1| Wei Guo1

1
Institute for Sustainable Agro-­Ecosystem
Services, Graduate School of Agriculturaland Life Sciences, The University of Tokyo,
Tokyo, Japan
2

Plant Phenomics Research Center, Nanjing
Agricultural University, Nanjing, China
Correspondence
Wei Guo
Email: guowei@g.ecc.u-tokyo.ac.jp
Funding information
Strategic International Collaborative
Research Program, Grant/Award Number:
JPMJSC16H2; Japan Society for the
Promotion of Science, Grant/Award
Number: 18K14464 and 18KT0087; Japan
Science and Technology Agency, Grant/Award Number: JPMJCR1512, JPMJCR1601
and JPMJCR1602
Handling Editor: Arthur Porto| Yuya Fukano1| Yoichiro Kato1|

Abstract
1. High-­throughput 3D phenotyping is a rapidly emerging field that has widespread
application for measurement of individual plants. Despite this, high-­throughput
plant phenotyping is rarely used in ecological studies due to financial and logisticallimitations.
2. We introduce EasyDCP, a Python package for 3D phenotyping, which uses
photogrammetry to automatically reconstruct 3D point clouds of individuals
within populations of container plants and output phenotypic trait data. Here
we give instructions for the imaging setup and the required hardware, whichis minimal and do-­it-­yourself, and introduce the functionality and workflow of
EasyDCP.
3. We compared the performance of EasyDCP against a high-­end commercial laser
scanner for the acquisition of plant height and projected leaf area. Both tools hadstrong correlations with ground truth measurement, and plant height measurements were more accurate using EasyDCP (plant height: EasyDCP r2 = 0.96, Laser
r2 = 0.86; projected leaf area: EasyDCP r2 = 0.96, Laser r2 = 0.96).

4. EasyDCP is an open-­source software tool to measure phenotypic traits of container plants with high-­throughput and low labour and financial costs.
KEYWORDS

3D point cloud, container plants, photogrammetry, plant phenotyping

1 | I NTRO D U C TI O N(Cobb et al., 2013; Furbank & Tester, 2011; Houle et al., 2010;

Plant ecology and evolution emerge from interactions between

phenotyping aims to alleviate this bottleneck by introducing tools

the phenome, genome and environment, requiring scholars to de-capable of quickly generating large morphological datasets (Araus

velop a deep knowledge of all three components. However, there& Cairns, 2014; Houle et al., 2010; Pieruschka & Schurr, 2019;

currently exists a mismatch between phenomics and genomics, as

Tardieu et al., 2017; Walter et al., 2015).

Minervini et al., 2015; Pieruschka & Schurr, 2019). High-­throughput

modern genotyping techniques enable low-­cost generation of large

Measurement of phenotypic traits can be conducted in multiple di-amounts of data, whereas phenotyping throughput is still limited

mensions, including two-­dimensional (e.g. shoot growth (Li et al., 2020),

Alexander Feldman and Haozhou Wang contributed equally to this work.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium,provided the original work is properly cited.
© 2021 The Authors. Methods in Ecology and Evolution published by John Wiley & Sons Ltd on behalf of British Ecological Society
Methods Ecol Evol. 2021;00:1–8.	﻿

wileyonlinelibrary.com/journal/mee3

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FELDMAN et al.

projected leaf area (Guo et al., 2017), herbivory (Machado(An et al., 2016; Paulus et al., 2014) or only support 2D traits (Gehanet al., 2016)) using digital image processing, and three-­dimensional& Kellogg, 2017).

(e.g. morphological structure: Paproki et al., 2012; shoot biomass:

The purpose of this work was to develop and evaluate a tool to

Golzarian et al., 2011; total leaf area: Xiao et al., 2020; plant posture:

calculate phenotypic traits from a set of captured images of con-

Wu et al., 2019) using mainly laser scanning (Kjaer & Ottosen, 2015;

tainer plants at different size scales with minimal labour time andPaulus et al., 2014) and photogrammetry (Agapito et al., 2015; Duanfinancial cost. Additionally, support for multiple image sets (e.g.

et al., 2016) techniques.

time-­series data, large populations) was required so that datasets

3D phenotyping is a promising new toolkit to study plant ecol-

could be processed with high throughput.

ogy and evolutionary biology. First, 3D phenotyping at the individual

Here we introduce EasyDCP (Easy Dense Cloud Phenotyping),

plant level will enable detailed quantification of morphological trait

a software tool which operates a photogrammetry pipeline to ex-

differences among individuals, populations and related species that

tract 3D phenotypic traits from container plants using a regularcould not be measured manually and can therefore advance our un-digital camera and a combination of commercially available andderstanding of local adaptation and plasticity of these traits. Second,

open-­source software. EasyDCP has the following advantages: (a)non-­destructive 3D phenotyping measurement has the advantage

populations of container plants can be measured outdoors or in aof being able to measure individual morphological changes overcontrolled environment with little to no relocation, (b) large popula-time. Third, 3D morphological data can be useful for modelling andtions can be measured quickly and (c) low financial and labour costs.

simulation in genetics studies (Chen et al., 2019).

Additionally, although the scope of this paper is limited to the mea-

Despite its advantages, 3D phenotyping has not been used ex-

surement of container plants, EasyDCP may be used to measure any

tensively in ecology studies, primarily due to the large populationgroup of appropriately set up objects. We provide detailed instruc-sizes in ecology research requiring high-­throughput measurement.

tions to operate EasyDCP from image acquisition to data output.

Existing high-­throughput 3D phenotyping systems are costly, large-­

We evaluate the performance and accuracy of EasyDCP, compare

scale and require special equipment or dedicated facilities, suchwith a commercial plant phenotyping tool and provide a case study

as the PHENOARCH (Cabrera Bosquet et al., 2015) and The Plant

demonstrating the high-­throughput capability of EasyDCP.

Accelerator (Honsdorf et al., 2014). Many of these high-­throughput
phenotyping platforms employ an imaging chamber, limiting the
maximum plant size suitable for measurement (Czedik-­Eysenberg

2 | E a s y D C P OV E RV I E W

et al., 2018; Rahaman et al., 2015, table 2). Some lower-­cost phenotyping tools are in development but they have either limited through-The EasyDCP workflow (Figure 1) consists of an image acquisition

put capacity (Tovar et al., 2018), significant hardware requirementscomponent (Section 2.1) and two data processing components:

(a)

(b)

(c)
F I G U R E 1 The overall workflow of
EasyDCP. (a) Each group of plants must
be imaged, and images and configuration
files must be prepared for EasyDCP. (b)EasyDCP_Creation performs sequential
processing on all image sets, creating a 3D
point cloud for each group of plants. (c)EasyDCP_Analysis performs classification,
segmentation and calculation on each
3D point cloud to measure plant traits
including plant height and projected leaf
area

Methods in Ecology and Evolu on

FELDMAN et al.

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EasyDCP_Creation (Section 2.2), which creates a 3D point

canopy perimeters is required. Using the same number of plants

cloud from 2D images; and EasyDCP_Analysis (Section 2.3),

for all measurement groups and maintaining the same plant spac-

which analyses that point cloud and performs trait calcula-ing is recommended. Covering the ground with a non-­reflectivetion. EasyDCP source code and documentation are available

cloth (Figure 2c) is recommended especially if weeds are present

on GitHub (https://github.com/UToky​o -­F ield​P heno​m ics-­L ab/in the image acquisition area.
Images may be captured using any digital camera with recom-

EasyDCP).

mended resolution of 8 megapixels or higher. Imaging angle, distance, capture interval (i.e. distance between capture locations),

2.1 | Image acquisition

resolution and sharpness all affect the quality of the resulting 3D
point cloud. The 3D point cloud is reconstructed using the struc-

Plants must be imaged prior to EasyDCP measurement, and theture from motion photogrammetry technique, which relies on

image acquisition area can be set up according to the user's needs

overlapping area between neighbouring images (Ullman, 1979). To(Figure 2a,b). The image acquisition area should have as little in-avoid biases and to maximize overall image sharpness, we suggest

clination as possible. One printed target page (.pdf provided withdesigning an imaging protocol that allows 90% of overlap between

the software) must be placed in a corner of the image acquisi-

individual images (Andújar et al., 2018; Kawamura et al., 2020;tion area, to set the orientation and scale. A second target page

Madec et al., 2017) and capturing as close to nadir (directly down-

may be installed in the opposite corner, to define the region ofward) as possible. For example, 90% image overlap will be achieved

interest within EasyDCP and reduce processing time. Additional

if images are captured at a distance of 1 m from the plants with atarget pages may be installed at intervals throughout the image

capture interval of 10–­15 images per meter and a horizontal fieldacquisition area if its length exceeds 3 m. By default, EasyDCP ex-of view of 70°. Capturing at least two rows of images will cre-pects the first target page to be located at the bottom-­left cornerate side overlap, reducing the likelihood of measurement bias dueof the scene, and the second target page at the top-­r ight cornerto spherical distortion (An et al., 2016, 2017). Additional rows ofof the scene (Figure 2). Plants must be arranged in a single row

images can be captured to increase the image overlap and mea-

(Figure 2a) or a staggered double row (Figure 2b). Plant ID (i.e.

surement accuracy (e.g. 5–­15° off-­nadir; Figure 2c). Higher image

measurement order) will increase starting from the side contain-

resolution will ensure accurate measurement at longer imaging

ing the first target page. A gap of at least 10 cm between plantdistances.

(a)

(c)

1

4

3

2

1m

(b)
3

1

5

7

2m
1m

2

4

6

8

F I G U R E 2 Image acquisition. (a) and (b) show the basic image acquisition protocol for EasyDCP. Target pages are placed in the corners of
the image acquisition area. Plants can be arranged in either (a) a single row or (b) a staggered double row. Numbers next to plants indicate
plant ID, which always increases from the first target page to the second. (c) shows oblique view of a 3D point cloud of a group of threecontainer plants (Amaranthuspatulus) in a 1 m × 2 m image acquisition area. Four parallel rows of images indicated by blue rectangles were
captured, totalling 92 images and averaging 11.5 images per meter for each angle

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FELDMAN et al.

2.2 | Point cloud creation

Open3D and the clustering algorithm DBSCAN (Ester et al., 1996) in

4Scikit-­learn. In some cases, groups of noise points can be incorrectly
EasyDCP_Creation is an automatic pipeline that creates a 3D point

segmented and considered as plants. To correct this, the user may en-

cloud from a set of 2D images (Figure 1b). This pipeline controls

able the K-­means clustering algorithm (Arthur & Vassilvitskii, 2007)

the commercial photogrammetry software Metashape Professionalin Scikit-­learn to remove non-­plant segments based on point count.

1.6.6 (Agisoft LLC) via its Python API (Agisoft, 2020b; Van Rossum

Segmentation works best when the number of measured plants re-& Drake, 2009) to perform 3D reconstruction via structure-­from-­

mains constant so that the user may inform EasyDCP_Analysis of the

motion (Hartley & Zisserman, 2003; Paulus, 2019; Seitz et al., 2006;

expected number of plant segments.

Ullman, 1979). EasyDCP_Creation supports sequential processing
for multiple image sets (i.e. groups of plants).

The calculation step performs trait measurement on each individual plant point cloud (Figure 1c.3). Traits can be calculated in

First, images are imported into a Metashape project. The image

one, two and three dimensions, and the scope of this paper is lim-

quality (IQ) is estimated by Metashape's built-­in function and all im-

ited to plant height and projected leaf area (PLA). Plant height is cal-ages with an IQ value below the iq_threshold parameter are disabled.

culated by finding the mean distance from the ground of all plant

Next, coded targets are detected in the images and Metashape's

points above the percentile parameter, which is user-­adjustable andAlign Cameras function is executed to create a tie point cloud con-

set to 98 by default. The ground height parameter can be automati-

taining key matching points. The locations and known distancescally detected or manually specified. The container height parameter

between the coded targets are used to scale and orient the point

is used to offset ground height and must be entered by the user if

cloud and define the edges of the image acquisition area. Finally, anonzero. PLA is calculated by finding the area occupied by the pro-dense point cloud is created (Figure 1b) and exported to .ply for-jection of the voxelized point cloud onto the ground plane. The voxel

mat. Additionally, a Metashape report is exported to .pdf format andsize parameter may be adjusted to improve PLA measurement ac-

the Metashape project is saved to .psx format. The user may checkcuracy. Other traits supported by EasyDCP include the lengths ofan output .ply file with CloudCompare (https://www.cloud​compa​

the long and short axis by ellipse regression and convex hull volume

re.org) or check a .pdf report to view a top-­down image of the point(Figure 1c.3).

cloud and to see if any images were excluded from the process.

2.3 | Point cloud analysis

3 | PE R FO R M A N C E TE S T
We tested EasyDCP on 24 container plants (Amaranthus patu-

EasyDCP_Analysis (Figure 1c) includes three steps, which aim tolus (n = 6), Commelina communis (n = 6), Eleusine indica (n = 6) anddistinguish plants from the background (classification); individual-

Galinsoga quadriradiata (n = 6)) in a greenhouse at Institute forize plants (segmentation); and measure 2D and 3D traits (calcula-

Sustainable Agro-­ecosystem Services, the University of Tokyo,tion). EasyDCP_Analysis is implemented in the open-­source Python

Tokyo, Japan. We selected two phenotypic traits for comparison:(Van Rossum & Drake, 2009) programming language and based onplant height and projected leaf area (PLA) due to their wide interest

several popular packages, including Open3D (Zhou et al., 2018),

among biologists (Andújar et al., 2018; Christian Rose et al., 2015;

SciPy (Virtanen et al., 2020), scikit-­learn (Pedregosa et al., 2011),

Fahlgren et al., 2015; Kjaer & Ottosen, 2015; Machado et al., 2016;

scikit-­image (Van Der Walt et al., 2014), Matplotlib (Hunter, 2007),

McCormick et al., 2016; Paulus et al., 2014; Tovar et al., 2018; Xiao

NumPy (Van Der Walt et al., 2011) and Pandas (McKinney, 2010).

et al., 2020; Zhou et al., 2019) and being representative of all three

Sequential processing of multiple point clouds previously generated

dimensions. To evaluate EasyDCP performance, one investigatorby EasyDCP_Creation is supported, providing high-­throughput phe-measured the same traits both manually and with a commercial laser

notyping functionality.

scanner (PlantEye F500 DualScan; Phenospex LLC). We captured

The classification step identifies each point of the input point

images for EasyDCP and concurrently scanned the plants on thecloud as vegetation (Figure 1c.1) or background using the classi-

PlantEye platform in groups of three due to the size limitations offication and regression tree algorithm (Breiman et al., 1984; Guo

the PlantEye. Additional details on the performance test methodol-

et al., 2013) in Scikit-­learn. The user must provide training data forogy are provided in Supporting Information Section 1.

the classification algorithm in the form of .png image files contain-

We evaluated the scaling accuracy of the point clouds gener-

ing samples of vegetation and background. The classification result

ated by EasyDCP and the PlantEye using three precision scale bars

strongly depends on the quality of the training data, and example(Cultural Heritage Institute, San Francisco, USA) installed at differ-training data are provided with the software for reference. Noiseent angles (0, 6 and 12°) throughout the image acquisition area (seepoints are removed from the resulting vegetation and backgroundFigures 1b and 2a,b). The precision scale bar lengths were not used

point clouds using the remove statistical outlier and remove radius out-

as input for the scaling step of EasyDCP_Creation. We measured thelier functions in Open3D.

scale bar lengths within the point clouds and calculated the differ-

The segmentation step separates individual plants within theences between the actual and measured values. We used a simple

vegetation point cloud (Figure 1c.2) using the voxelization function in

linear regression model to compare the measured trait data from

Methods in Ecology and Evolu on

FELDMAN et al.

both EasyDCP and the PlantEye to ground truth, using the coeffi2| 5

were measured on different days. The successful measurement bycient of determination (r ) to evaluate the correlation between the

EasyDCP in a similar time period as the PlantEye demonstrates thatindependent (ground truth) and dependent (measured) variables.

EasyDCP is a viable alternative to a commercial laser scanner for

Additionally, to evaluate high-­
throughput efficacy, we ap-

high-­throughput measurement.

plied EasyDCP on a population of 217 Digitaria ciliaris individuals(Supporting Information Section 2) from an ecology study (Fukano
et al., 2020).

4 | R E S U LT S A N D D I S CU S S I O N
4.1 | Performance test

4.3 | Using the system
We created EasyDCP to be suitable for a wide variety of measurement conditions. The user may decide the dimensions of the image
acquisition area and the number of plants per measurement group.
If plants and targets are appropriately set up, plants may be imaged
in place (e.g. on cultivation tables), significantly reducing imaging

All measured scale bar lengths (n = 24) were found to be within 0.7%

time. Advanced photogrammetry techniques, such as using a multi-­of the actual values (Figure 3a), indicating that the point cloud gen-camera system (as in An et al., 2016, Figure 3) or extracting frames

erated by EasyDCP_Creation was scaled with acceptable accuracy.

from video, may further expedite imaging. EasyDCP has several

When comparing the measurements of plant height and pro-parameters described in the documentation that can be modified

jected leaf area (PLA) with the ground truth data (n = 24), the cor-to ensure compatibility with the user's image acquisition protocol.

relation coefficient (r2) equalled or exceeded that of a high-­
end

EasyDCP can run on any Windows, Mac or Linux computer thatcommercial laser scanner for phenotyping (Figure 3b; plant height:

meets Metashape's minimum requirements (notably 16GB RAM)

EasyDCP r2 = 0.96, PlantEye r2 = 0.87; PLA: EasyDCP r2 = 0.96;(Agisoft, 2020a). A graphics processing unit (GPU) is recommendedPlantEye r = 0.96).

but not required.

2

One challenge is that EasyDCP tends to overestimate PLA, likely

4.2 | Case study: High-­throughput phenotyping
application for ecology

due to the voxelization technique and the unpredictable density ofpoint clouds produced by photogrammetry. The bias may be reduced
by adjusting the voxel size parameter or by calibrating with plants ofknown PLA.

Our high-­throughput test was very efficient. Images were acquiredin under 3 hr and the EasyDCP pipeline completed processing inapproximately 3 hr on our high-­end desktop PC. We measured the

4.4 | Future priorities

same plants using the PlantEye laser scanner the following day,completing the process in under 6 hr. We did not directly compare

We intend to optimize the image acquisition technique to mini-

the results from EasyDCP and the PlantEye because the plants

mize the required time and materials without compromising theF I G U R E 3 (a) The differences between the actual and measured scale bar lengths using EasyDCP and the PlantEye laser scanner.
(b) Calculation of plant height and projected leaf area plotted on the y-­axis against ground truth. The black line represents a 1-­to-­1
measurement with ground truth

6| Methods in Ecology and Evolu on

measurement accuracy. Extraction of frames from video files is

FELDMAN et al.

Yuya Fukano

https://orcid.org/0000-0001-9057-4742

currently in development. We may add a color calibration function

Yoichiro Kato

https://orcid.org/0000-0002-7131-0220

to EasyDCP_Creation to improve the 3D point cloud quality (Berry

Seishi Ninomiya

et al., 2018). We acknowledge that there are relevant licensing costs

Wei Guohttps://orcid.org/0000-0002-2123-4354

https://orcid.org/0000-0002-3017-5464

associated with EasyDCP_Creation and hope to replace Metashapewith a free and open-­source photogrammetry tool. Lastly, we plan to

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5 | CO N C LU S I O N
We presented EasyDCP, a software tool to extract plant phenotypic
traits in 3D from images of container plants using a digital camera.
We have shown EasyDCP to be a viable low-­cost tool for phenotyping populations of plants, measuring traits with comparable orslightly greater accuracy than a commercial laser scanner. Computer
hardware and commercial 3D reconstruction software are financial
costs, but the total cost is affordable compared to other 3D plantmeasurement tools and high-­
throughput phenotyping solutions.
EasyDCP has the advantages of greater ease of use and fewer manualsteps than other 3D-­based approaches, enabling high-­throughput
operation with minimal training.
AC K N OW L E D G E M E N T S
This study was partially funded by the JST CREST Program (grant
numbers JPMJCR1512, JPMJCR1601 and JPMJCR1602), SICORP
Program JPMJSC16H2, and JSPS KAKENHI grant number 18K14464
and 18KT0087. The authors declare that we have no conflicts ofinterest.
AU T H O R S ' C O N T R I B U T I O N S
A.F. and W.G. conceived the ideas and designed the methodology;
A.F. and H.W. collected and analysed the data; A.F. and H.W. led thewriting of the manuscript with the input of all authors; Y.F. provided
all plant materials; W.G., S.N. and Y.K. supervised the whole work.
All authors contributed critically to the drafts and gave final approval
for publication.
PEER REVIEWThe peer review history for this article is available at https://publo​ns.​com/publo​n/10.1111/2041-­210X.13645.
DATA AVA I L A B I L I T Y S TAT E M E N T
Data from the performance test (source images, point clouds, trait
data and R files), EasyDCP source code, example scripts and detailed
documentation are archived using Zenodo https://doi.org/10.5281/
zenodo.4756537 (Feldman et al., 2021).
ORCID
Alexander Feldman
Haozhou Wang

https://orcid.org/0000-0002-1162-5917
https://orcid.org/0000-0001-6135-402X

FELDMAN et al.

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S U P P O R T I N G I N FO R M AT I O N
Additional supporting information may be found online in the
Supporting Information section.

How to cite this article: Feldman, A., Wang, H., Fukano, Y.,
Kato, Y., Ninomiya, S., & Guo, W. (2021). EasyDCP: An
affordable, high-­throughput tool to measure plant
phenotypic traits in 3D. Methods in Ecology and Evolution,
00, 1–­8. https://doi.org/10.1111/2041-­210X.13645