ῐ
J. Jpn. Soc. Soil Phys.
῏῍ῌῐῑ῎
No. 3/, p. -ῌ++ ῍,**-῎In-situ Measurements of Density and Distributionof Periphery Roots of Tomato Seedling
Using Machine Vision System
Manzo UCHIGASAKI*, Shingo MIYAMOTO*, Kazuhiro SERATA*,
Sheishu TOJO** and Kengo WATANABE*** Department of Bioenvironmental & Agricultural Engineering, Nihon University, +200,
Kameino, Fujisawa ,/,ῌ2/+*, Japan** Faculty of Agriculture, Tokyo University of Agricultural & Technology,
Saiwai-cho, Fuchu, Tokyo +2-ῌ2/*3, Japan
Abstract
A new, simple method for evaluating and quantifying the peripheral root and its architecture
was developed and applied to tomato (Lycopersicon esculentum Mill.) seedlings. This method
consists of the use of a transparent tray cell and application of shade in roots, using an image
analysis system.
The objectives of this study were to : (+) develop an in-situ measurement technique using
machine vision technology for the quantiﬁcation of the peripheral root growth of seedlings usingtransparent tray cells ; (,) to develop a program to analyze the Peripheral Root Density andDistribution (PRDD) ; and (-) to study the characteristics of the seedling root system in an
air-pruning tray cell. The speciﬁc PRDD value was calculated for the air-pruning tray cell and a
non-air-pruning tray cell and ﬁve stages of the seedling. Analysis of the results showed that thismethod can be useful for evaluating peripheral root architecture in di#erent designs of tray celland can be used not only to analyze the e#ect of air-pruning but also to determine the e$cient sizeand design of tray cells.
Key words : Root growth analysis, Machine vision, Peripheral Root Density and Distribution(PRDD), Air-pruning tray cell.

+.

Introduction

Healthy seedlings of uniform size are
required for transplanting in order to produce
a uniform stand in the ﬁeld. Many studies haveindicated that seedling establishment andgrowth performance depend on the speed of
the root system development (Huang et al.,+33,). Roots are the underground portions of aplant that interact with the soil. The rootsystem of a plant may have more branches
than the shoot system (Wang et al., +33/).
Common problems associated with containerized plant and seedling production are

root-tangling and root-spiraling, commonlyreferred to as “root-binding”. Root-binding
deters development of the root system after theplant or seedling is transplanted, resulting inslower growth of the plant. When the plantroots emerge through the holes of a container
they shrivel due to exposure with air. This is
usually referred to as “root air-pruning”. Oncea root has been air-pruned, the plant immediately starts a new root branch. Proper application of air-pruning to a root system therefore
promotes secondary root growth and eliminates root-binding, resulting in a large root
mass.

῏

4

ΐῐ῎῔῕ῑ ῒ 3/ ῏ ῌ,**-῍

Since each variety of plant grows to a moreor less recognizable form and size at each stageof growth, modeling and simulation techniques
would provide an e#ective means for exploration and prediction of the pattern of plant
growth. A simulation model can be formulated
by considering factors which a#ect the growthand development of root system elements, suchas the main roots, root branches and root hairs.
Microcomputerized image analysis (Machine
Vision) is a technique which has particular
merit for the evaluation of the roots of plantseedlings, since it permits direct, objective,
non-intrusive visualization and measurementof both roots. The essential components formachine vision analysis of a seedling root include : +) a standardized staging environment,,) an image capture device (e.g., video camera),-) a digitizer board housed in a microcomputer,and .) display devices [monitors] for presentation and interactive measurement of thedigitized image.
Evaluations of plant root systems are particularly challenging in nature, because roots areburied in soil, sensitive to excavation and intricately arranged within the soil matrix.
Image analysis has proven to be an excellent
tool for direct analysis of root zone data inplant production and for investigation of root
growth rates. The shoot culture imaging wasused for examining root zones ; close-up imaging was used in some instances to inspect root
surface feature (Smith et al., +33/). Pasian et al.
(+333) developed a new simple method forevaluating and quantifying the root severity.
This method consists of surrounding the root
pan with a transparent ﬁlm and tracing all
roots with a marker. Root-length measurement is important for the evaluation of root
functions and of inﬂuences of the soil environment. Accurate and rapid root-length measurement methods are, however, still under development (Kimura et al., +333). Minirhizotrons
speed up research on root demography, but
image quality often hampers standardizationof the image-processing method. A simple pro-cedure working within the blue band of thecolor image was tested on the ﬁbrous roots ofsugarbeet (Beta vulgaris var. Sacharifera)(Vamerali et al., +333).
Recently the use of high-resolution digital
image analysis based on personal computers
has increased in agricultural science, because
image analysis has several advantages, including its non-destructive nature, high speed andhigh accuracy (Box et al., +330), (Omasa et al.,+332) and (Chikushi et al., +33+).
Smith (+33/) indicated that because powerfulimage-editing software could show the numberof pixels in a selected area and the color index
of the selected pixels, it could easily obtain theratio of root spot area to total root area using asimple procedure of image analysis.
Equipment to measure root area has been
available for some time. A CCD camera tocapture an image, and computer software,using mathematical algorithms, are used tomeasure pixels on the screen and to estimate
the area and length of such an image. This
computer technology allows easy quantiﬁcation of some root characteristics and helps
remove some of the subjectivity of humananalysis. Although it can in theory distinguishbetween soil and roots based on di#erences inshading, its use requires the proper lighting
balance to maximize contrast.
In this study, image-processing techniques
are applied to describe the general growth dynamics of a root system in a tray cell. Since
seedlings generally exhibit complex patternsof root development in a tray cell, a peripheral
root density and distribution (PRDD) program
was developed to measure the number densityof roots and their spatial distribution for aseedling at a speciﬁc growth stage.
The objectives of this study were :(+) To develop an in-situ measurement technique using machine vision technology
for the quantiﬁcation of peripheral root
growth in seedlings using transparent
tray cells ;(,) To develop a program to analyze theῐ῏ : In-situ Measurements of Density and Distribution of Periphery Roots of Tomato Seedling Using Machine Vision System(-)

PRDD and ;To study the characteristics of the seedling root system in an air-pruning tray
cell.
,.

Materials and Methods

Experimental Apparatus and Image Acquisition
A CCD (SONY, XC-1+++) color video camera
was used to capture root images. The camera
was ﬁtted with a lens (VCL ,/ mm, F,.,).
Images were captured and digitized with a resolution of 1/0ῌ.20 pixels at 2 bit/pixel for eachred (R), green (G) and blue (B) color component.
With 2 bit color resolution, the values for eachpixel ranged from * to ,//. Captured images
were stored as bit-map (bmp) ﬁles in a Gateway(,22 MHz Pro) microcomputer and displayed ona Mitsubishi diamontron color monitor. Imageprocessing and analysis software was developed and executed on the Gateway computerand all algorithms were implemented in the
Visual basic (Version 0.*) programming language. Fig. + is a schematic diagram of themachine vision system. Light was provided by
two -**-W lamps with an input voltage of +**V. A pneumatic control system rotated the tray
cell in a circular (-0*῍ degrees) motion andcontrolled each step (3*῍ degrees). The resulting digitized bmp-format image had a width ofFig. +

Schematic of the components of themachine vision system. Artiﬁcial light
was used for illumination.

5

1-0 pixels, a height of /0* pixels and a size of+.,*3 MB. The digitized image was calibrated
to record the precise dimensions of the image
and relate these dimensions to the actual sizeof the tray cell. The carefully-calibrated, digitized image of the seedling root were comprised of a pixel array that preserved all of themorphometric (spatial) and photometric (spectral) features. In this experiment the soil (Dega
Potgrand Delft, Netherland) was used soil andpeat most (+/- of total volume of the soil).
Plant Material and Air-pruning Tray Cell
The design of the air-pruning tray cells wasdeveloped by Huang in +31-. Fig. , shows atransparent air-pruning tray cell and a blackcovered air-pruning tray cell. Tomato seedlings (Lycospersicon esculentum Mill.) weregrown in a growth chamber (Biotron -/*) at an
air temperature of ,-῎, relative humidity 1*ῌ
and a light intensity of +,,*** Lux (with a +,-h
photoperiod). Detached root systems were
sampled at --day intervals after the one-rootstage. Images of the four sides of each tray cell(air-pruning and non air-pruning tray cell)were captured (Fig. +) on ﬁve observation days
from germination : -, 0, 3, +, and +. days, thus
providing a sample of /+, images.
Image Processing (Win-Roof)Figure - a shows the procedures using the
Win-Roof (Mitani Corporation Inc.) imageprocessing system. The original image of theroot underwent the following processes. (+)
Color measurement : measuring the averagecolor of the pixels within an on-screen region.
(,) Color extraction : counting the pixels of a
speciﬁed color within an on-screen region andthereby obtaining the area. (-) Calibration : theroot image was adjusted to give a clear contrast between the tray cell area and the root
area. (.) The root image was obtained by extracting the root area from the tray cell and all
exposed roots were traced in green. (/) Shading
applied to seedling root. The brightness of theroot in the original image varied with the position of the root. In the binary image, roots
were seen clearly, even for areas of low bright-

6

Fig. ,

3/,**-

The conﬁguration of the air pruning and non-air-pruning tray cells used in the study.

ness. In the original image, the tangled roots atthe base of the root system were transformed
into clearer lines than those in the original or
binary image. Although partial root image
was lost by this operation, the loss was relatively small when compared to the total area ofthe root system.
Algorithm Development (PRDD program)(Figure - b) shows a ﬂow-chart of the PRDD
program that was developed. The next procedure was as follows : (+) The starting point was
the image of the root already processed using
the Win-Roof program ; (,) Input of image
data ; (-) Determination of range of image processing ; (.) Setting of conditions for root
(selecting only root images from the background and counting the number of pixels in
the total root area) ; (/) Setting range for all
pixels ; (0) Extraction conditions ; (1) Calculation of the number of pixels (selecting only theroot area and counting the number of pixels) ;(2) Repetition ; (3) Determination of root distribution for each layer and the ratio between
total number of pixels for the root and total
number of the pixels for one layer ; (+*) Calculation of the root area as a percentage of thetotal layer area (pixels) ; and (++) Measurementof the total root area after evaluating the distribution of root area in the periphery of the traycell.
Peripheral root density (PRDD) was calculated
as follows :PRDD (ῌ)(TRA/TTA)+**Where TRA is the total root area in one layer
and TTA is the total area of one layer of the
tray cell (both measured in mm,).
Fig. .
showed a processed image using the PRDD
program showing the distribution of root ratio
in the tray cell of the eight layers.
-.

Results and Discussion

Soil luminance varied only weakly withtime, depending on soil moisture and on theillumination intensity, neither of which wereconstant, whereas root luminance decreased
progressively with time, due to senescence.
The root image obtained using the transparent tray cell method has the advantages ofbeing less subjective and less dependent on
evaluator experience. It also allows a longer
period of evaluation because of its nondestructive nature. On the other hand, it re-῍ῌ : In-situ Measurements of Density and Distribution of Periphery Roots of Tomato Seedling Using Machine Vision System

Fig. .

Fig. -The procedure involved image capture
and processing, followed by measurement using an image analysis
system. Flow-chart for (a) Win-Roof(Mitani Corporation Inc.) and (b) the
PRDD program used to determine theroot distribution in each layer (one tray
cell separated into eight layers) ; see thetext for detailed explanation.

quires an expensive, although easy to use,
video camera that may not be available toeveryone.
The method takes into account only roots at
the periphery of the tray-cell, ignoring roots
located at the center. This should not be aconcern because most actively growing rootsof plants grown in containers tend to be at the
periphery of the root area. Furthermore, inorder to image roots at the center, the growing

7

A processed image using the PRDD
program showing (A) a preprocessedroot image, (B) the distribution of rootsin the eight layers, and (C) the resultsof peripheral root density and distribution calculated for roots in theeight layers.

medium must be removed which can also dislodge damaged roots causing an increase in theevaluation error.
This problem may be
aggravated with vegetable plants, such as thetomato used in this study. In the case of root
areas where there are numerous ﬁne roots too
close to each other to trace individually, theroot image analysis method allows for area
measurement of only total root areas of highand low root growth.
The results of image analysis demonstratethat shortly after the ﬁrst branching, a second
branching occurs midway through the tray
depth in a pattern similar to the ﬁrst branching. Stage - [air-pruning - preprocessing image(AP-B)] shows dominant downward and diagonal root growth. At stage / (AP-B) the cellshows “ﬁlling” of the upper soil layer withroots are ready to produce new roots as soon as
the seedlings are transplanted. This process
can be observed using the PRDD program byrelating the root growth, the root growth rate,
cell size, branching and sub-branching rates, as
shown in several stages of air-pruned root development in Fig. /. Seedling production canΐῐ῎῔῕ῑ ῒ 3/ ῏ ῌ,**-῍

8Fig. /

Five stages of shoot and root development of tomato (Lycospesicon esculentum Mill.)
seedlings using an air-pruning (AP) and non-air-pruning (NP) tray cells. The image
processed using the PRDD program is shown for air-pruning (AP-B) and non-air-pruning(NP-B) tray cells. Stage / (NP-B) shows dominant downward and diagonal root growth
while stage / (AP-B) shows “ﬁlling” of the upper soil layer with roots, stage - (AP-B) shows
rapid elongation of the stem while stage - (NP-B) shows low growth.

be optimized according to plant species, minimum time requirement and cell size. For the
non-air-pruning tray cell at stage - (NP-A), theﬁrst root continues to elongate, spiralingaround inside the base of the container cell. At
stage / (NP-B) the peripheral root can be seen
to be concentrated around the base of the tray

cell. This would result in severe root-binding.
The results using the PRDD program to calculate peripheral root area distribution for the
air-pruning tray cell. The program determinedthe distribution of the roots among eight layers
for each cell. The value of the area of the rootis the average of four repetitions. Images of῍ῌ : In-situ Measurements of Density and Distribution of Periphery Roots of Tomato Seedling Using Machine Vision Systemeach cell were captured for all four sides.
Figure 0 shows the root distribution proﬁleof the air-pruning tray cell with depth. The
results demonstrated a signiﬁcant development of the root 3 days after germination(August +). The air-pruning e#ect could beobserved by the development of the root
through the center of the tray cell (depth : +./ῌ..* cm).
Figure 1 shows the root distribution proﬁleFig. 0Fig. 19of the non-air-pruning tray cell and depth. The
results demonstrated non-signiﬁcant development of the root on all data collection dates.
The di#erence between the maximum area ofthe root using the air-pruning and non-airpruning tray cells was 01.2/ῌ. The root
growth process illustrates that root development within a air-pruning tray cell is far superior to non air-pruning tray cell.
The advantage of the present system is its

Root distribution proﬁles and depth of tomato seedling in air-pruning tray cell.

Root distribution proﬁles and depth of tomato seedling in non-air-pruning tray cell.

ΐῐ῎῔῕ῑ ῒ 3/ ῏ ῌ,**-῍

10

rapid processing time. Measuring a series oforiginal color images and reconstructing thecorresponding surface image with color texture can be accomplished automatically in a
few minutes, which is probably less time thanis required by other methods. Moreover, byincorporating the software that we developed,
the resulting surface image can be observed
the distribution of the root in layers (2 layers).
..

Conclusion

The newly developed method using machine
vision provides good results when used tomeasure peripheral root area. The most important information discovered from the study
was :(+) The results analysis showed that thepresent method can be useful for themeasurement of peripheral root density(PRDD) and architecture in di#erent
designs of tray cell, in order to understand the characteristics of the seedlingroot system ;(,) The use of transmitted light allowed us
to acquire a very clear image, by which
we could quantify, the root spot severity precisely and easily. This result
suggested that image analysis usingtransmitted light could be a very useful
tool to quantify root area in plants withvery complex root growth patterns,
such as tomato seedlings.
(-) The method takes into account onlyroots at the periphery of the root area,
ignoring roots located at the center.
Future work might investigate a discrimination analysis taking into account features suchas root texture and soil color. The fundamental
study to discover new and e$cient designs of
tray cells will be necessary.
Acknowledgments
The authors would like to thank Mr. Ryuichi
Ikuta (Dia-Topy Inc., Japan), for his cooperation in the design of the transparent tray
cell used in this study. Special thanks go to Mr.

Hiroyuki Okawa and Hiroko Nakazato (Graduate student, Department of Bioenvironmental& Agricultural Engineering, Nihon University),
for his assistance in acquiring seedling root
images.
References
Box, J.E. J. (+330) : Modern Methods for Root Investigations, Plant Roots, The Hidden Hall,
Waisel, Y., Eshel, A. and Kafkaﬁ eds. p. +3-ῌ,-1.
Chikushi, J., Yoshida, S. and Eguchi, H. (+33+) :Application of image processing to measurement of total length in root system, Mathematical and Control Applications in Agricultureand Horticulture-IFAC, p. .*1ῌ.++.
Huang, B.K. (+33.) : Computer simulation analysisof biological and agricultural system. CRC
Press Inc.
Huang, B.K. (+31-) : Seedling growing and handling device. Patent No. -1+,,/,, United States
Patent O$ce, Washington, D.C., (Jan. ,-, +31-).
Huang, L.K. and B.K. Huang, (+33,) : Simulation
analysis of automation transplanting impulse
pneumatic system. Mathematical Modelingand Simulation in Agriculture and BioIndustries, IMACS/IFAC, First International
Symposium, Brussels, Belgium, Vol. II, VI. C. /ῌ
+.
Kimura, K., Kikuchi, S. and Yamasaki, S. (+333) :Accurate root length measurement by image
analysis. Plant and Soil, ,+/ : ++1ῌ+,1.
Omasa, K. and Kouda, M. (+332) : --D Color video
microscopy of intact plants : A new method
for measuring shape and growth. Environmental Control in Biology, -0 (.) : ,+1ῌ,,0.
Pasian, C.C., Ramirez, F.V. and Nameth, S. (+333) :Digital video technology as an aid for quantifying root rot. HortScience, -. (,) : ,3.ῌ,3/.
Smith, M.A.L. (+33/) : Image analysis for plant cell
culture and micropropagation. In AitkenChristie, J., Kozai, Smith, M.A.L. : Automationand environmental control in plant tissue culture. Kluwer academic publishers, p. +./ῌ+0-.
Vamerali, T., Ganis, A., Bona, S. and Mosca, G. (+333): An approach to minirhizotron root image
analysis. Plant and Soil, ,+1 : +2-ῌ+3-.
Wang, L., Huang, L.K. and Huang, B.K. (+33/) : Simulation of root air-pruning and plant growth
performance, Mathematical Modeling and
Simulation in Agriculture and Bio-industries,
First International Sysmposium, IMAC/IFAC,
V-III-C : +ῌ0.

¸¹ : In-situ Measurements of Density and Distribution of Periphery Roots of Tomato Seedling Using Machine Vision System 11   *ῌ *ῌ

*ῌ**ῌ***  !,/,῏2/+* "#$%&'(** )*+* !+2-῏2/*3 ),-.(



/01234567 89:;<=>?@5ABCDE FGH IJ>KLMNO5PQC RSTUVWXYZ[8:;?@Z\;E ]^Z_`aCD/HKbK cLycopersicon esculentum Mill.d /Z\;E ef0ghH ῌ IJ>KLMNO5ijkbVlmnolapqr/.010s589
hat[:;uv0wx ; ῍ KLMNO0yz{Z|[}G;10{~ cPRDDd 5TU:;
Y0wx ; ῎ O lKLMNOa;/010s5 / akZ0 PRD
00>5:;Fa\;E t[0TU 10xr5:D0 PRD [80NOZ01 340TU0Da F0?@¡¢iZ£;F¡¤DE ¥D@HO l0¦KLMNO0§¨>©Mªp«0¬ta­®iZ£;¯°G;E : bVlmnol 10[8 10±² O lῌKLMῌNO³´µ¶ : ,*** µ 0 ¶ ,* ³·µ¶ : ,**+ µ 3 ¶ - 