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n
M.S. Borhan , S. Panigrahi , M.A. Satter , H. Gu
aResearch Specialist, Agricultural and Biosyst
b Professor of Electrical and Computer Engine
Agricultural and Biosystems Engineering Dep
cGraduate Student, Computer Science Departm
y Depa
chlorophyll content with a high R2 of 0.88). The multiple regression
otosynthesis. Nitro-
for chlorop
ecule that
t affect th
tion and maintenance of leaf chlorophyll can resu
limited supply of nutrients and from other envir
factors such as excess heavy metal content [3,4]. The defi-
ciency of any of these several nutrient elements generally
reduces pigment formation and subsequent leaf color (green
to pale yellow); limiting those nutrients would increase reflec-
tance because of decreased radiation absorption. To increase
* Corresponding author at: Agricultural and Biosystems
Engineering, North Dakota State University, PO Box 6050,
Fargo, ND 58108-6050, USA.
E-mail addresses: md.borhan@ndus.edu (M.S. Borhan),
spanigr@purdue.edu (S. Panigrahi), msatter@ndsu.edu (M.A.
Satter), huanzhong.gu@ndsu.edu (H. Gu).
Avai lab le a t www.sc ienced i rec t .com
INFORMATION PROCESSING IN A
journal homepage: www.e
Peer review under responsibility of China Agricultural University.
1. Introduction
Chlorophyll is a green pigment found in plants, algae, and
bacteria that gives them their green color. It also enables
them to absorb the light necessary for ph
gen (N2) is an essential nutrient element
thesis and is a part of the chlorophyll mol
photosynthesis [1,2]. The conditions tha
http://dx.doi.org/10.1016/j.inpa.2017.07.005
2214-3173 � 2017 China Agricultural University. Publishing services by Elsevier B.V.
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
hyll syn-
enhances
e produc-
lt from a
onmental
Prediction accuracy model (using all features) provided anaverageprediction accuracyof 85.08%andamaximum
accuracy of 99.8%. The prediction model using only mean gray value of red band showed an
average accuracy of 81.6% with a maximum accuracy of 99.14%.
� 2017 China Agricultural University. Publishing services by Elsevier B.V. This is an open
access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-
nd/4.0/).
SPAD meter
Regression model predicted the
d Senior Software Developer, Psycholog
A R T I C L E I N F O
Article history:
Received 8 May 2017
Received in revised form
11 July 2017
Accepted 18 July 2017
Available online 28 July 2017
Keywords:
Computer imaging
Chlorophyll
ems Engineering Department, North Dakota State University, Fargo, ND 58105, USA
ering Technology, Purdue University West Lafayette, IN 47907, USA; and former professor of
artment, North Dakota State University, Fargo, ND 58105, USA
ent, North Dakota State University, Fargo, ND 58105, USA
rtment, North Dakota State University, Fargo, ND 58105, USA
A B S T R A C T
Facilitating non-contact measurement, a computer-imaging systemwas devised and evalu-
ated to predict the chlorophyll content in potato leaves. A charge-coupled device (CCD) cam-
era pairedwith two optical filters and light chamber was used to acquire green (550 ± 40 nm)
and red band (700 ± 40 nm) images from the same leaf. Potato leaves from 15 plants differing
in coloration (green to yellow) and age were selected for this study. Histogram based image
features, such as mean and variances of green and red band images, were extracted from
the histogram. Regression analyses demonstrated that the variations in SPADmeter reading
couldbeexplainedby themeangrayandvariancesof grayscalevalues.Thefitted least square
models based on themean gray scale levelswere inversely related to the chlorophyll content
of the potato leaf with a R2 of 0.87 using a green band image andwith an R2 of 0.79 using a red
band image.With the extracted four image features, thedevelopedmultiple linear regression
a,* b c d
Evaluation of computer imagin
predicting the SPAD readings i
technique for
potato leaves
GRICULTURE 4 (2017) 275–282
l sev ier .com/ locate / inpa
over time may affect its physiological status [9]. Piekielek and
to estimate water stress in muskmelon. All these systems
g r
crop yield, techniques for monitoring the nutritional status of
crop plants, which involve the routine collection of foliar tis-
sue and chemical analysis (solvent extraction method), have
been developed. Such monitoring techniques are relatively
expensive and time consuming. Thus, easier and more effi-
cient methods of rapid screening would be useful to farmers.
The SPAD-502 chlorophyll meter (Spectrum Technologies
Inc., Plainfield, IL), is a rapid, non-destructive, and hand held
spectral device that is widely used for in-situ measurement of
N deficiency in the laboratory and in the field [5–8]. Plant
greenness can be measured non-destructively using a SPAD-
502 meter. This meter, which is sensitive to photosynthetic
green pigment of individual leaf, is suitable for small plot
areas. The SPAD meter reading is related to the amount of
chlorophyll present in the leaf. These meter readings are
related to leaf greenness by transmitting light from light emit-
ting diode through a leaf at wavelengths 650 and 940 nm. The
650 nm light corresponds to a peak chlorophyll attenuation of
red light. The infrared (IR) 940 nm signal is not absorbed by
chlorophyll. The signal from the silicon photo diodes used
to detect the transmitted light is received by a microproces-
sor, which linearizes the signal and calculates a unit-less
SPAD value [9]:
SPAD ¼ A log10
RC�
RC
� �
� log10
IRC�
IRC
� �� �
þ B ð1Þ
where A and B are constants; RC and IRC are current
(amperes) from red and infrared (IR) detectors, respectively,
with the sample in place; RC� and IRC are currents from the
red and IR detectors, respectively, without a sample [10]. A
SPAD or chlorophyll meter reading is calculated by a micro-
processor and is determined from the difference in light
attenuation.
Generally, the SPAD (chlorophyll) meter clamps on intact
leaves and instantly generates readings that ranged from 0
to 50. Those readings are merely the indication of greenness,
not the chlorophyll concentration. Thus, it is necessary to find
a numerical correlation between SPAD meter readings and
the foliar chlorophyll concentration to predict the chlorophyll
amount in plant leaves [6]. Relationship (R2) between SPAD-
502 meter readings and extractable total chlorophyll (fresh
weight basis) in strawberry leaves was reported to be 0.92
[11]. The regression between total chlorophyll contents and
SPAD readings of wheat, rice, and soybean leave samples
extracted with dimethyl sulfoxide results a relationship (R2)
of 0.93 [12]. Similarly, a relationship (R2) of 0.98 was found
between fresh leave tissue chlorophyll determined using sol-
vent extraction methods and SPAD meter readings [8]. They
also reported that chlorophyll values were differed by �6%
when SPAD values converted from radiometric to solvent
extracted chlorophyll units.
Chlorophyll meter readings can be influenced by many
factors other than nitrogen (N) alone such as environmental
and crop leaves characteristics [2,13,14]. Anything that can
alter the color of plants (i.e., diseases, other nutrient deficien-
cies, variety differences, etc.) can influence the chlorophyll
meter readings [15]. It is also recommended that growers
use a calibration curve specific for each crop variety. The
276 I n f o r m a t i o n P r o c e s s i n g i n A
SPADmeter has been used to estimate chlorophyll concentra-
tions and infer the nitrogen status of single leaves of wheat
demonstrated that a CCD cameracould produce data that
can be used to assess the status of the plants in the field.
Therefore, the overall goal of this research was to determine
the suitability of a computer imaging technique (using black
and white CCD camera) to predict chlorophyll content of
potato plants. The objective of this paper was to evaluate
the capability of a monochrome computer imaging technique
for predicting SPAD meter readings while acquiring pictures
on potato leaves.
2. Materials and methods
2.1. Leaf sample collection and chlorophyll measurements
Potato leaves from one genotype of potato cultivar Russet Bur-
bank (Solanum Tuberosum) were collected from an experimen-
tal field of North Dakota State university. Leaf from stage five
(5th leaf), counted from the top of the potato plant and was
collected 65 days after planting. Fifteen potato leaves differing
in chlorophyll content (SPAD values in this research) were
randomly selected from potato plants grown in an experi-
mental plot at North Dakota State University, ND, Fargo.
These leaves were selected based on the color (green to pale
yellow) and age difference. A Minolta brand chlorophyll meter
(Model SPAD-502; Spectrum Technologies Inc. Plainfield, IL)
was used to measure the chlorophyll reading of each leaf
before its images were acquired. Mean of five readings from
each leaf was recorded as SPAD value.
2.2. Image acquisition
Each leaf was placed on a smooth, black surface, directly
under the vertically mounted camera (Fig. 1). The leaf was
Fox [17] found that chlorophyll meters detect N deficiency in
corn as early as the V6 growth stage by measurement on
the fifth leaf.
The SPAD meters cannot be used for on-the-go measure-
ments of nitrogen due to their configurations and working
principle that needs physical contact with leaves [18]. In con-
trast, portable handheld radiometers measure reflected light
remotely without making any contact, and the same area
can be measured without any damage to the plants or leaves.
In this technique, many plants are averaged in a single mea-
surement, avoiding point sampling problems with the SPAD
meter. Use of a CCD (charge-coupled device) camera to cap-
ture images offers an additional method for assessing green-
ness of crops as well as the parameters that can be sensed
remotely. Dymond and Trotter [19] used a CCD camera to
obtain color images of forest and posture targets from air-
craft. Clarke [20] used a pair of black and white cameras with
filters in paired with a thermal imaging system in an aircraft
and corn, and other crops [10,16]. Its use for characterizing
the senescence of whole canopies is limited by operator bias
particularly in selecting leaves for SPAD measurements [9].
On the other hand, repeated measurements on the same leaf
i c u l t u r e 4 ( 2 0 1 7 ) 2 7 5 –2 8 2
adjusted for better exposure of the entire leaf. Two tungsten
halogen lamps with an input of 120 VAC were used for
segmentation was not required. To process the images and
extract features, a subroutine was written and implemented
in the C++ programming language. Image features were cal-
culated using the relations illustrated in Fig. 2 [23].
2.4. Data analysis
Four features (mean and variance at each of these two bands)
were extracted from the images to predict chlorophyll meter
readings. Image features, such as mean gray value and vari-
I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 4 ( 2 0 1 7 ) 2 7 5 –2 8 2 277
illumination. The output of these lamps was 250W. Positions
of the lamps were adjusted such that the leaves were uni-
formly illuminated without any shadow. Front lighting was
used. It has been observed that the reflectances in the green
band at 550 nm and the upper edge of the red band at
710 nm were better correlated with crop nitrogen status.
Based on the findings of other researchers [21,22], two band-
pass filters, i.e., green filter (550 ± 40 nm) and red filter (700
± 40 nm) were used for this study. Acquired images using
those two filters were termed as green and red band images
respectively.
A Sony CCD (Model XC-77) black and white camera (Sony
Corporation, New York, NY) fitted with a Navitron (Navitron
Communication, Vancouver, BC) TV zoom lens (18–108 mm)
was used to capture the images of potato leaves. The zoom
lens was fitted with 550 and 700 nm filters to acquire images
of same leave in green and red band, respectively. The camera
has a 6.4 � 4.8 mm CCD imager with active pixels of
682 H � 492 V resolutions. Image processing hardware used
to digitize the images consisted of a Matrox Meteor frame
grabber (Matrox Inc., Dorval, Quebec). The frame grabber
has 640 H � 480 V (RS-170) pixels. The digital video to PCI
interface supports 8-bit mono (256 gray levels) and 15-bit
and 24-bit RGB resolution that can display acquired images
at 30 frames per second.
2.3. Image processing and feature extraction
The captured images were stored as ‘tiff files’ in a microcom-
puter. Histogram-based image features, such as mean gray
Fig. 1 – Schematic diagram of image acquisition system.
value and variance, were used to predict chlorophyll meter
readings. In this study, whole leaflet was under the field of
view of the camera i.e. whole leaflet was pictured, thus
Fig. 2 – Calculation of image features from histogra
ances, were extracted from each spectral image (550 nm and
700 nm band) of the potato leaves. Statistical analyses of
those features were also conducted to evaluate the pre-
dictability of chlorophyll content using histogram based
image features. Various regression models, such as simple
and multiple linear regression, were used to describe the rela-
tionship among these image features with measured chloro-
phyll meter readings. All regression models were developed
using the SAS package [24]. The average prediction accuracies
and associated errors of five different statistical models were
determined using the following equation [25].
Average Prediction Accuracyð%Þ
¼ 1
N
X
1� Absoulute error
Actual output
� �� �
� 100 ð3Þ
where, N = Total number of observations in a dataset. The fol-
lowing additional parameters were also calculated to com-
pare the performance of the models.
Absolute error = Absolute difference between actual and
predicted output values.
Minimum error = Minimum among individual absolute
error in a given dataset.
Maximum error = Maximum among individual absolute
error in a given dataset.
Minimum accuracy = Minimum prediction accuracy
observed in a given dataset.
Maximum accuracy = Maximum prediction accuracy
observed in a given dataset.
3. Results
A total of 15 leaves randomly selected varying in color and
thus chlorophyll content. Spectral images were acquired at
the 550 nm and 700 nm bands (center wavelength) from each
leaf sample (Fig. 3). Figs. 4 and 5 show the histograms of 15
images at the 550 nm and 700 nm bands, respectively.
m data from both green and red band images.
showed a decreasing trend with the increased chlorophyll
content as indicated in the SPAD meter readings (Table 1).
This finding is likely due to more greenness of the leaves
absorbs more light in the visible spectrum and decreases
the reflectance. In general, chlorophyll absorbs light in the
red and the blue regions of the visible light spectrum.
Regression models were developed and evaluated to pre-
dict chlorophyll meter readings based on four extracted
image features such as mean and variances of green and
red band images. Initially, simple linear regression models
were evaluated taking one feature at a time. Then, a multiple
linear regression model was also developed to predict chloro-
phyll contentrepresented by SPAD meter readings. All four
features showed negative relationships with the chlorophyll
(SPAD meter readings; Table 2). The regression between red-
278 I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 4 ( 2 0 1 7 ) 2 7 5 –2 8 2
Histograms of the images for both bands showed variations
over 15 levels of leaf color (chlorophyll content). Overall,
mean values of all four features extracted in this study
Fig. 3 – Potato leaf images at two spectral bands. Top (green
band) and bottom (red band).
Fig. 4 – Histogram of images
band mean gray values (GLMRB) and the SPADmeter readings
(Model 1) showed a linear relationship with a coefficient of
determination (R2) of �0.87 (Fig. 6). Subsequently, regression
between green-band mean gray values (GLMGB) and the SPAD
meter readings (Model 2) showed a linear relationship with an
R2 of �0.79 (Fig. 7). This implies that the regression models
account for approximately 87% and 79% of the total variations
in the SPAD readings about their mean, respectively, were
explained by mean gray level values of the spectral images
acquired at the 700 ± 40 and 550 ± 40 nm bands, respectively.
Additional regression analyses were performed to evaluate
the capabilities of green and red band variances to predict
SPAD readings. The linear regression using (red band variance
(GLVRB) referred as Model-3 and green band variance (GLVGB)
designated as Model-4 (Table 3) resulted R2 of 0.58 and 0.61,
respectively (Figs. 8 and 9). It was observed that statistical
regression models using mean gray values for both band
images were performed better than those using variances.
Analyses also showed that image features such as mean gray
values and variances at both spectral bands (550 and 700 nm)
were inter correlated (Table 2). A stepwise regression proce-
dure was also followed to identify the significant features
since extracted image features were correlated. The stepwise
procedure identified the redmean gray value as the significant
feature at 15% (/ = 0.15) significance level, which was Model-1
with a R2 of 0.87. Regression analysiswas also conducted using
the multiple regression model to predict chlorophyll content.
acquired at 550 nm band.
I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 4 ( 2 0 1 7 ) 2 7 5 –2 8 2 279
The multiple regression model (Model-5) used all 4 features
and it resulted in the highest coefficient of determination of
0.88 (Fig. 10) compared to other previously developed regres-
sion models. In this model (Model 5), account for 88% of the
total variations in the SPAD reading about their mean were
explained using 4 image features together (Table 3). The
Fig. 5 – Histogram of images
Table 1 – Calculated image features from 15 leaf images (n = 15)
Leaves Gray values of red band Images Gra
Mean Variance Me
1 75.43 158.45 101
2 209.35 629.14 207
3 169.79 382.99 162
4 140.26 137.94 133
5 77.02 100.58 103
6 167.22 370.64 159
7 145.80 422.49 146
8 110.30 223.08 124
9 125.40 250.64 129
10 128.94 190.17 133
11 84.82 122.09 85.3
12 79.44 128.25 82.0
13 91.09 107.32 88.4
14 140.26 137.94 124
15 78.15 94.62 81.9
Table 2 – Correlation coefficient of different image features with
Variable GLMGB GLVGB
GLMGB 1.000 0.7438
GLVGB 0.7438 1.000
GLMRB 0.9267 0.8286
GLVRB 0.6840 0.7628
CHLO. �0.8685 �0.5809
GLMRB = Gray level mean red band image (770 nm), GLMGB = Gray level
image (550 nm), GLVRB = Variance of red band image (700 nm).
observed R2 of Model-5 was very close to the R2 of Model-1
(0.87). Table 3 compares the prediction accuracies and errors
of different statistical models. Model-1 (GLMRB) performed
best of all regression models. The observed minimum, maxi-
mum, and average prediction accuracies were 20.49, 99.19,
and 82%, respectively. Subsequently, minimum, maximum,
acquired at 700 nm band.
differing in color.
y values of green band Images SPAD readings
an Variance
.51 298.91 29.90
.63 785.87 3.00
.05 365.91 11.30
.20 271.83 19.90
.86 249.48 40.10
.76 421.74 11.85
.14 595.32 21.30
.82 343.70 32.85
.07 467.04 22.55
.44 421.96 31.25
3 178.01 37.55
0 132.43 43.90
1 102.18 35.05
.85 415.00 11.55
7 129.32 39.00
SPAD meter readings.
GLMRB GLVRB CHLO.
0.9267 0.6840 �0.86085
0.8286 0.7628 �0.5809
1.000 0.8025 �0.7918
0.8025 1.000 �0.6083
�0.7918 �0.6083 1.000
mean green band image (550 nm); GLVGB = Variance of green band
Fig. 6 – Linear regression of gray level mean and chlorophyll
content (represented by SPAD meter readings) at 700 nm
band.
Fig. 8 – Linear regression of gray level variance and
chlorophyll content (represented by SPADmeter readings) at
700 nm band.
280 I n f o r m a t i o n P r o c e s s i n g i n A g r i c u l t u r e 4 ( 2 0 1 7 ) 2 7 5 –2 8 2
and average absolute errors of prediction were found to be
0.28, 9.34, and 3.19, respectively.
Multiple regression model (Model-5) produced the highest
average prediction accuracy (85%) and the lowest average
absolute error of prediction (2.97) among all regression
Table 3 – Comparison of actual and predicted SPAD meter readi
Model number Feature (s) Absolute error
Min. Max.
Model-1 GLMRB 0.282 9.34
Model-2 GLMGB 0.426 14.34
Model-3 GLVRB 0.018 26.25
Model-4 GLVGB 0.198 13.67
Model-5 All four features 0.065 9.78
Model-1: Linear regression between red band mean gray value (GLMRB)
Model-2: Linear regression between green band mean gray value (GLMGB
Model-3: Linear regression between variances of red band images (GLVR
Model-4: Linear regression between variances of green band images (GLV
Model-5: Multiple regression taking all four features and SPAD meter rea
*1 Implies an over prediction (2.24 times) with respect to actual value.
*2 Implies an over prediction (3.27 times) with respect to actual value.
*3 Implies an over prediction (2.24 times) with respect to actual value.
Fig. 7 – Linear regression of gray level mean and chlorophyll
content (represented by SPAD meter readings) at 550 nm
band.
models. The maximum prediction accuracy was 99.8%.
However, the observed minimum prediction accuracy using
Model-5 was only 50.8%. The observed maximum and average
absolute errors were found to be 0.065 and 9.78 respectively.
Although the difference between Model 1 (with redmean only
ngs using different regression models.
R2 Prediction accuracy (%)
Mean Min. Max. Mean
3.19 �0.87 20.49 99.19 81.57
4.33 �0.79 �24.14*1 98.90 72.24
6.93 �0.58 �127.3*2 97.55 48.64
6.48 �0.61 �21.02*3 96.07 65.05
2.97 0.88 50.79 99.83 85.08
and SPAD meter reading.
) and SPAD meter reading.
B) and SPAD meter reading.
GB) and SPAD meter reading.
ding.
Fig. 9 – Linear regression of gray level variance and
chlorophyll content (represented by SPADmeter readings) at
550 nm band.
g r i
as feature) and Model 5 (with all four features) based on aver-
age prediction accuracy is small, both the models could be
used to predict SPAD meter readings. Thus, this study
inferred that histogram based image features have potential
to predict chlorophyll content of potato leaves.
4. Discussion
SPAD readings are the indicator of leaf greenness and thus
chlorophyll or nitrogen status of plants. The SPAD reading is
calculated by an inbuilt microprocessor after acquiring and
comparing current signals from red and infrared band detec-
tors (photo diodes) with and without leaf. In this study, unlike
SPAD readings, image based features extracted from two
specific band images (550 ± 40 and 700 ± 40) were attempted
to correlate with SPAD readings aiming at determining the
leaf chlorophyll content using computer imaging. Our
assumption was that if a computer imaging technology can
be used to predict SPAD readings,then image based features
can also be used to predict chlorophyll contents of the plant
Fig. 10 – Multiple regression results taking all four features
to predict chlorophyll content (represented by SPAD meter
readings).
I n f o r m a t i o n P r o c e s s i n g i n A
leaves.
To interpret or convert SPAD readings into chlorophyll con-
centrations, it is required to establish a numerical relation-
ship (correlation) between SPAD readings and the
chlorophyll amount (concentration) in plant leaves deter-
mined by wet chemistry (solvent extraction) method [6]. Pre-
vious studies on establishing relationship of SPAD readings
and actual foliar chlorophyll concentrations were found in
the literature [8,11,12]. A coefficient of determination (R2)
between SPAD-502 readings and extractable total chlorophyll
in strawberry leaves was stated 0.92 [11]. Monje and Bugbee
[12] reported R2 of 0.90–0.93 between total chlorophyll con-
tents and SPAD readings of wheat, rice, and soybean leave
samples extracted with dimethyl sulfoxide. Similarly, a R2 of
0.98 was found between fresh leave tissue chlorophyll deter-
mined by solvent extraction methods and SPAD meter read-
ings with an error of approximately �6% between actual
chlorophyll concentration and chlorophyll concentration pre-
dicted by SPAD readings when SPAD values converted from
radiometric to solvent extracted chlorophyll units [8].
However, none of the previous studies reported prediction
accuracy of the SPAD readings and chlorophyll concentra-
tions to be compared with the results from this study.
In this research, we did not determine the chlorophyll
concentration of the leaves to be correlated with image fea-
tures instead regressed with SPAD readings. A multiple
regression model (using all features) provided an R2 average
prediction accuracy, and a maximum accuracy of 0.88, 85%,
and 99.8%, respectively. Average prediction accuracy of 85%
and an R2 of 0.88 with a low absolute error (�3) are reason-
ably good with this types of application. Inherently, the
chlorophyll concentration sensing (SPAD and imaging tech-
nologies) is affected by environmental and crop leaves char-
acteristics such as the light-scattering properties of leaf
cells and the nonhomogeneous distribution of chlorophyll
in leaves appear to regulate the ability of the sensing systems
to estimate in vivo chlorophyll concentration [12,25]. Thus,
this research demonstrated that image based features can
also be used to predict chlorophyll contents of the plant
leaves.
However, for field application, prototype of a portable,
handheld, compact, and miniature version of the laboratory
prototype needs to be developed. In a field prototype, a minia-
ture imaging system will consist of an imaging sensor (cam-
era), LCD image display, inbuilt LED light chamber, and leaf
platform. All optoelectronic components will integrate and
interface in a light proof enclosure coupled with a smart but
low-cost microprocessor (raspberry Pi-2 with field pro-
grammable gate array) based image storage, image signal pro-
cessing and modeling, and will result in displaying
capabilities. With the rapid advancement of computer and
electronic technologies, low-cost electronics such as camera
and microprocessors are available for both laboratory and
field uses. An imaging system for measuring chlorophyll
can be a cost effective alternative for SPAD meter ($2000.00).
A well designed and tested imaging system can be used as a
conventional method by the farmers. However, the size of
the imaging systemmay be little larger than an existing alter-
native (SPAD meter) and the prolonged use of the system in
the field may cause the battery to run out. The imaging sys-
tem can be provided with a strap to support it from shoulder.
A larger capacity battery can be used or a light emitting diode
(LED) light can be used to increase the operational duration of
the imaging system in field condition.
5. Summary and conclusions
A study was conducted to evaluate the effectiveness of com-
puter imaging techniques as a non-contact measurement
option for predicting SPAD meter reading as chlorophyll con-
tent in potato leaves. Appropriate image acquisition and pro-
cessing techniques were developed to acquire the images of
potato leaves with varying measured chlorophyll levels. His-
togram based features (mean and variance) from two spectral
band images were extracted. The multiple linear regression
model (Model-5) predicted SPAD meter readings with a very
high correlation coefficient (R2 = 0.88). The individual fea-
tures, such as mean gray values at 700 nm and 550 nm bands,
were found to be highly correlated with measured SPAD read-
c u l t u r e 4 ( 2 0 1 7 ) 2 7 5 –2 8 2 281
ings with a R2 of �0.87 and �0.79, respectively. Average pre-
diction accuracy of the multiple regression model (using all
four features) was 85%. The average prediction accuracy
obtained using only red mean gray level was 81.6%. The
observed average prediction accuracy using green mean gray
level was 72.24%. Thus, this research indicates the suitability
of laboratory-based monochrome imaging technique for
chlorophyll prediction. Additional investigation is needed to
further evaluate this finding on a large sample size and in
the field condition. For field application, prototype of a porta-
ble, handheld, compact, and miniature version of the labora-
tory prototype needs to be devised, in which a miniature
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Acknowledgements
The financial assistance of the Red River Potato Growers
Association, North Dakota, is gratefully acknowledged.
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	Evaluation of computer imaging technique for predicting the SPAD readings in potato leaves
	1 Introduction
	2 Materials and methods
	2.1 Leaf sample collection and chlorophyll measurements
	2.2 Image acquisition
	2.3 Image processing and feature extraction
	2.4 Data analysis
	3 Results
	4 Discussion
	5 Summary and conclusions
	Acknowledgements
	References