Indian Journal of Agricultural Research
214
RESEARCH ARTICLE
Indian Journal of Agricultural Research, Volume 56 Issue 2: 214-219 (April 2022)
Application of a Visible/Near-infrared Spectrometer in
Identifying Flower and Non-flower Buds on ‘Fuji’ Apple Trees
Alisher Botirov
1
, Songhao An
1
, Osamu Arakawa
2
, Shuhuai Zhang
2
10.18805/IJARe.A-655
A
BSTRACT
Background:
Forecasting bud physiologic conditions can help ‘Fuji’ apple farmers manage their orchards more efficiently. Being able
to determine the nature of a bud before bud burst is one such forecast that could be of use to these ‘Fuji’ growers. The aim of this
research project was to determine if a device, a visible/near-infrared spectrometer, could be employed to determine whether a bud is
a flower or non-flower bud without destroying the bud.
Methods:
Experiments were conducted on buds taken from a ‘Fuji’ apple tree, beginning on January 29 through to March 31, 2021,
three days before bud burst. The data from the visible/near-infrared spectrometer clarified whether a bud was a flower or a non-flower
bud. The Spectro data Classification Learner App proved to be an accurate classification method to analyze flower and non-flower bud
Spectro data.
Result:
Three days before bud burst, the chlorophyll content levels of the non-flower buds were markedly higher (P
0.05) than those
of the flower bud, which explains why the visible border of the near-infrared spectrometer might have been changed by the chlorophyll
content of buds. The visible and near-infrared bands of the buds showed that the Spectro data of the non-flower buds were higher than
those of the flower buds when measurements were made three days before bud burst. Three days before bud burst Cubic KNN of
KNN classifier analyzed flower and non-flower buds smoothly. Spectro data were labeled as accuracy 75.9%, sensitivity 86% and
specificity 67%. The results that were obtained suggest that farmers could use a visible/near-infrared spectrometer to identify flower
and non-flower buds in their orchards, without damaging the buds, three days before bud burst.
Key words:
Bud burst, Chlorophyll content, Classifier learner app, Non-destructive method.
I
NTRODUCTION
The ‘Fuji’ apple (
Malus domestica
Borkh.), a cross between
‘Delicious’ and ‘Ralls Janet,’ was introduced in Japan in
1962 (Soejima
et al
., 1998). This fine-grained apple, with
its high sugar and low acid content, is juicy, firm and crisp
and has a sweet, spicy flavor (Rojas-Grau
et al
., 2006).
Today, it is one of the world’s most widely consumed
apples and is cultivated in apple-producing regions across
the globe.
On the other hand, the ‘Fuj i’ poses a number of
problems for growers. Due to its vigorous growth, it is
necessary to prune aggressively in order to open up the
canopy to control this growth. In the case of the ‘Fuji,’ it is
crucial to distinguish between flower and non-flower buds
when pruning, because if these buds are not identified and
flower buds thinned, they will cause over-vigorous growth
and lower productivity.
Chlorophyll is the pigment that gives a plant its green
color and is a crucial component of a plant’s physiology
(Palta, 1990; Gitelson
et al
., 2003). Until now, the bud
chlorophyll content of ‘Fuji’ apple buds has not been used
to identify flower and non-flower buds. Moreover, no research
has been reported on the detection of flower and non-flower
buds using non-destruc tive measurement methods.
Therefore, we decided to check bud chlorophyll content and
changes in chlorophyll levels, and to use a visible/near-
infrared spectrometer to identify and enable the separation
of flower from non-flower buds. In this study, the classification
of flower and non-flower buds was determined by a near-
infrared and visible border that identifies chlorophyll content.
How ever, althou gh identifying c hanging levels of
chlorophyll in the weeks/days leading up to bud burst can
determine which buds are flower buds and which are non-
flower buds it also involves the destruction of the bud itself.
On the other hand, it offers verification of and insights into
non-destructive approaches.
Buban and Faust (1982) have reported that determining
whether a bud is a flower bud or non-flower bud is crucial
for ‘applied horticulture’ and the future productivity of a young
orchard. Flower bud formation is a complicated process
because it is affected by the tree’s spurs and long shoots,
the character of the cultivar, as well as the age and strength
1
The United Graduate School of Agricultural Science, Iwate
University, Morioka, Iwate 020-8550, Japan
2
Faculty of Agriculture and Life Science, Hirosaki University,
Hirosaki, Aomori 036-8560, Japan.
Corresponding Author:
Osamu Arakawa, Faculty of Agriculture and
Life Science, Hirosaki University, Hirosaki, Aomori 036-8560, Japan.
Email: oarakawa@hirosaki-u.ac.jp
How to cite this article:
Botirov, A., An, S., Arakawa, O. and
Zhang, S. (2022). Application of a Visible/Near-infrared Spectrometer
in Identif ying Flower and N on-f lower Buds on ‘Fuji’ Apple
Trees. Indian Journal of Agricultural Research. 56(2): 214-219.
DOI: 10.18805/IJARe.A-655.
Submitted:
18-05-2021
Accepted:
04-09-2021
Online:
22-09-2021
Volume 56 Issue 2 (April 2022)
215
Application of a Visible/Near-infrared Spectrometer in Identifying Flower and Non-flower Buds on ‘Fuji’ Apple Trees
of the tree (Pratt, 1988). Moreover, apple tree flower bud growth
emerges on different parts of the tree. However, smart agriculture
technologies now make it possible to discern bud characteristics
before pruning, without destroying the bud in the process. The
device that can be used to determine hidden parts of examined
objects is the visible/near-infrared spectrometer. This
spectrometer is easy to use and produces results quickly.
Non-destructive testing includes a broad range of
techniques that are used in science and technology
industries to evaluate a material, component, or a system’s
properties without causing damage. According to Crowley
(2020), the visible region of the spectrum of electromagnetic
radiation identified by a visible/near-infrared spectrometer
is typically considered to be made up of wavelengths ranging
from 400 nm (violet light) to between 700 and 800 nm
(red light). Manley and Baeten (2018) have noted that, “the
essential origins of near-infrared spectroscopy include the
production, reporting, and understanding of spectra resulting
from the interaction of electromagnetic radiation with an
object.” Corresponding to Gogoi
et al
. (2018) spectroscopical
and photographic al equipment had been used for
discovering plant disorders and Venkatesan
et al
. (2020)
near-infrared spectrometer had been used for crops seed
characteristics. Osborne (2000) reported that “the infrared
(IR) region comprises that part of the electromagnetic
spectrum in the wavelength range between 780 and 100,000
nm and is divided into near-IR, mid-IR and far-IR subregions;
the near-infrared region covers the wavelength range from
780 to 2500 nm.” Furthermore, Rathore
et al
. (2021) citated
that near-infrared spectrometer include from 700 nm to 2500
nm wavelength ranges. Therefore, we use a visible/near-
infrared spectrometer to identify and enable the separation
of flower from non-flower buds.
The aim of this research project was to detect flower
and non-flower buds on ‘Fuji’ apple trees before bud burst
without destroying the buds. To do so, we 1) analyzed buds
before bud burst using an ultra-mini visible/near-infrared
spectrometer and 2) measured the chlorophyll content
before bud burst to explain what was visible on the
spec trometer. Results showed that the most reliable
spectrometer readings of flower and non-flower buds
occurred three days before bud burst.
M
ATERIALS AND
M
ETHODS
Plant materials
Flower and non-flower buds from a ‘Fuji’ apple tree were
used in this study. The ‘Fuji’ tree
studied was in one of the
orchards located on the grounds of the “Hirosaki University
Fujisaki Research Station” (Fujisaki, Aomori Prefecture,
Japan). The tree used was ten years old and had been
grafted on semi-vigorous Marubakaido rootstock. The dates
selected to test the buds were January 29, February 15,
March 1, March 15 and March 31 (accordingly 64, 47, 33,
19 and 3 days before bud burst), the latter being three days
before the tree’s 2021 bud burst (Table 1). Buds used for
testing on the respective testing dates, different branches
were taken randomly from the same tree. On each of these
dates a branch was cut off and brought to a laboratory in
the Hirosaki University Faculty of Agriculture and Life
Science. There, the buds were separated from the branch
and examined with an ultra-mini visible/near-infrared
spectrometer and tested for chlorophyll content as well.
Non-destructive measurement
The visible/near-infrared spectrometer is a device that
measures the amount of light that passes through an object
without destroying it. The OMT-NIR-M1 spectrometer used
in this study was manufactured by Optcom Co., Ltd. using
SpectralRatio Version 1.1.0.1 software. This spectrometer
measures from a range of 640 nm to 1050 nm, with an
interval of 2 nm. Measurement parameters were adjusted
to amp gain-high, to memory integration-16 and to
smoothing points-16 nm. All buds were measured with this
spectrometer and the spectral data were collected. The buds
were then examined under a microscope and the spectral
data were used to determine whether a bud was a flower or
a non-flower bud.
Grouping into flower and non-flower buds
The buds were weighed and sliced in two with a razor blade
from the middle of the top through to the bottom of the bud and
then checked with an Olympus microscope (Olympus
Corporation Tokyo, Japan, made in the Philippines). In the
upper part of the flower buds was a yellowish-green oval
stamen, whereas in the upper part of the non-flower buds there
was no such oval, though there was some light green matter
(Fig 1 A and B). The flower buds were then separated from the
non-flower buds and their chlorophyll content was measured.
Measurement by destructive means
The flower and non-flower buds were classified by shape,
weight, and also by chlorophyll content. Bud chlorophyll
content is related to bud physiology and differs depending
upon the type of bud. The separated buds were carefully
placed inside 2 ml tubes and pulverized in a Homogenizer
ShakeMan6 (model PS-SMNO6), after which the buds were
infused for 10 minutes in a 1.5 ml 80% acetone solution
inside 2 ml tubes. The mixtures were then moved to different
1.5 tubes, which were then put into a high-speed Micro
Centrifuge and set at a speed of 140,000 (rpm), for 10
minutes. The liquid rose to the top of the tubes and debris
from the buds settled at the bottom. The liquid solution at
the top of the tubes was then moved into a quartz glass
Table 1:
Flower and non-flower buds: destructive and non-destructive
testing and their distribution until bud burst for ‘Fuji’
in January through March in 2021.
Name
Number of buds
29-Jan
1-Mar
15-Mar
31-Mar
Flower
17
23
20
17
Non-flower
7
10
9
12
Total bud
24
33
29
29
Bud burst occurred on April 2, 2021.
Indian Journal of Agricultural Research
216
Application of a Visible/Near-infrared Spectrometer in Identifying Flower and Non-flower Buds on ‘Fuji’ Apple Trees
tube in order to measure the chlorophyll content with a
ultraviolet-visible (UV) spectrophotometer (Shimadzu
Access Corporation). The UV spectrophotometer was set
at three measuring ranges (645, 663 and 750 nm) and
chlorophyll content was checked within these Spectral
ranges and calculated using the following equation (1):
C = 7.22 (A663 nm - A750 nm) + 20.30 (A645 nm-A750 nm)
......(1)
Statistical analysis
Chlorophyll levels were analyzed using a one-way ANOVA
(comparing the difference between dates) and a Tukey test.
Flower and non-flower bud chlorophyll content differences
were analyzed using the Student’s t-test. All of the above
analyses were conducted by applying the R studio version
1.3.1073 (© 2009-2020 RStudio, PBC). Different software
was used to obtain Spectro data.
The bud Spectro data were analyzed using MATLAB
R2018b version 9.5.0.1298439 (©1984-2018 the MathWorks,
Inc), with the Classification Learner App tool. Spectro data
were analyzed individually for the dates on which the
measurements were taken and each new session was set
as cross-validation folds: 10 without PCA. Although all 22
machine-learning algorithms in the Classification Learner
App were chosen, we used only the results of 9 of these in
our table (Table 2). Accuracy, sensitivity and specificity were
verified by a confusion matrix plot.
R
ESULTS AND
D
ISCUSSION
Changes in bud chlorophyll content
Flower and non-flower bud chlorophyll levels exhibited
signific ant differences three days before bud burst.
Chlorophyll levels in the flower buds were significantly lower
than those in the non-flower buds (Fig 2). Flower bud
chlorophyll levels showed dramatic differences depending
upon the date. These levels increased significantly with the
approach of bud burst.
In this study, the classification of flower and non-flower
buds was determined by a visible/near-infrared border that
can identify their chlorophyll content. Until now, the bud
chlorophyll content of ‘Fuji’ apple buds has been measured
by examining the leaves and not the buds themselves. Our
analysis of the buds showed that, three days before bud
burst, the chlorophyll level of flower buds was significantly
higher than that of non-flower buds. However, no significant
differences in chlorophyll levels were found between flower
and non-flower bud when measured 33 days before bud
burst. This suggests that neither destructive nor non-
destructive measurements of chlorophyll levels are reliable
for distinguishing flower from non-flower buds.
640-798 nm range
The amount of light absorbed by the non-flower buds, seen
on the visible spectrometer three days before bud burst,
Table 2:
Buds Spectro data classification results using the classification learner app without using the PCA three days before bud burst
(DBBB) for the ‘Fuji’ flower and non-flower buds on the visible/near-infrared spectrometer on 2021.
Classifier
Classifier type
Classification accuracy (%) 3 DBBB
Accuracy
Sensitivity
Specificity
Discriminant analysis
Linear discriminant
62.1
65
56
Quadratic discriminant
F
F
F
Logistic regression classifier
Logistic regression
58.6
65
50
KNN
Fine KNN
62.1
67
55
Medium KNN
72.4
80
64
Coarse KNN
58.6
59
0
Cosine KNN
72.4
80
64
Cubic KNN
75.9
86
67
Weighted KNN
72.4
80
64
This table made according to analyze data by MATLAB and some of that data shown in here, F- False.
(a) (b)
Fig 1:
Difference between flower and non-flower buds for ‘Fuji’ in January 29, 2021, (a) non-flower bud, (b) flower bud.
Volume 56 Issue 2 (April 2022)
217
was higher than that of the flower buds (Fig 3). Bud Spectro
absorption dropped sharply from 670 nm to 720 nm.
800-1050 nm range
The absorption in the flower bud was lower than the Spectro
absorption in the non-flower bud on the as shown on the
near-infrared spectrometer three days before bud burst (Fig 4).
Flower and non-flower bud Spec tro absorption was
increased from the 930 nm and dropped on 1016 nm.
Spectro variation of buds on different dates
The flower and non-flower bud absorption seen on the visible
spectrometer decreased near the approach of bud burst
(Fig 5). Non-flower bud absorption observed on the visible
Fig 2:
Changes in chlorophyll content (mL/g fresh weight): A comparison of flower and non-flower buds tested 33 and 3 days before
bud burst (DBBB). Means±standard error and different letters indicate statistically significant differences between the days according
to a T- test; (*)- Significant at P
0.05.
Fig 3:
Flower and non-flower
bud absorbance of the visible spectrometer for ‘Fuji’ on March 31, 2021; DBBB-days before bud burst;
Green line-flower bud; Red line-non-flower bud
Fig 4:
Flower and non-flower
bud absorbance of the near-infrared spectrometer for ‘Fuji’ on March 31, 2021; DBBB-days before bud
burst; Green line-flower bud; Red line-non-flower bud.
A
a
B
a
0.0
20.0
40.0
60.0
80.0
100.0
120.0
140.0
Flower
Non-flower
Flower
Non-flower
33 DBBB
3 DBBB
C
h
lo
r
o
p
h
y
ll
c
o
n
te
n
t
(m
L
/g
F
W
)
Change of chlorophyll on buds
*
0
0 .1
0 .2
0 .3
0 .4
0 .5
0 .6
0 .7
0 .8
0 .9
1
6
4
0
6
4
6
6
5
2
6
5
8
6
6
4
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0
6
7
6
6
8
2
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4
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0
0
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6
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1
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1
8
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2
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7
3
6
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4
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5
4
7
6
0
7
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2
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7
8
7
8
4
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9
0
7
9
6
A
b
s
o
r
b
a
n
c
e
(
lo
g
(1
/R
))
Wa ve len gth (nm)
Visible part
Flower bud (3DBBB)
Non-flower bud (3DBBB)
-0.1
-0.05
0
0.05
0.1
0.15
0.2
8
0
0
8
1
0
8
2
0
8
3
0
8
4
0
8
5
0
8
6
0
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9
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0
9
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0
9
7
0
9
8
0
9
9
0
1
0
0
0
1
0
1
0
1
0
2
0
1
0
3
0
1
0
4
0
1
0
5
0
A
b
s
o
r
b
a
n
c
e
(
lo
g
(
1
/R
)
)
Wavelength (nm)
Near-infrared part
Flower bud (3DBBB)
Non-flower bud (3DBBB)
Application of a Visible/Near-infrared Spectrometer in Identifying Flower and Non-flower Buds on ‘Fuji’ Apple Trees
Indian Journal of Agricultural Research
218
Application of a Visible/Near-infrared Spectrometer in Identifying Flower and Non-flower Buds on ‘Fuji’ Apple Trees
spectrometer 33 days before bud burst was lower than the
flower bud absorption measured on the visible spectrometer.
Flower bud absorption seen on the visible spectrometer was
lower than that in the non-flower buds.
Absorption in the flower bud shown on the near-infrared
spectrometer absorption three days before bud burst was
lower than it was 33 days before bud burst (Fig 6). On the
other hand, absorption in the non-flower buds seen on the
near-infrared spectrometer three days before bud burst was
greater than it was 33 days before bud burst. Near-infrared
spectrometer light absorption readings showed that that light
absorption of the non-flower buds was greater than that of
the flower buds, for both 33 and three days before bud burst.
Classification analyses of spectro data
The Classification Learner App of the MATLAB was used
as a classification model and the 10-fold of cross-validation
was used to set the training data for the model (Table 2).
The c lassification results were obtained using the 22
machine learning algorithms; 9 of which are also shown in
Table 2. The highest classification accuracy was 75.9%,
performed by cubic k-nearest neighbor (KNN), accompanied
by medium KNN with 72.4% accuracy, cosine KNN (72.4%
accuracy) and weighted KNN (72.4% accuracy). The highest
sensitivity was found by cubic KNN (86%), then medium
KNN with 80% accuracy, cosine KNN 80% (accuracy) and
weighted KNN (80% accuracy).
This study introduced a non-destructive method of
identifying the flower and non-flower buds of ‘Fuji’ apple
trees. The results show that this method accurately classifies
and distinguishes flower buds from non-flower buds near
bud burst. This non-destructive method is an important way
to ascertain chlorophyll content, the water index (Agati
et al
., 1995; Penuelas
et al
., 1997). However, this non-
destructive way of differentiating the flower from the non-
flower buds of ‘Fuji’ apple trees has once not been examined.
Here, we found that the absorption of light in the flower buds
shown on the visible/near-infrared spectrometer just before
bud burst had diminished, whereas non-flower bud
absorption had risen. Classification Learner App testing
methods confirmed that the classification and differentiation
of flower from non-flower buds was 75.9% accurate when
tested with Cubic k-nearest neighbor (k-NN). Vitola
et al.
(2017) has reported that Cubic KNN is the simplest way to
separate various data and obtain accurate results. According
to Buban and Faust (1995), bud growth and development
Fig 5:
Flower and non-flower
bud absorbance seen on the visible spectrometer for ‘Fuji’ on March 15 and March 31, 2021;
33 and 3 days before bud burst (DBBB); Green and red lines = Flower buds; Green and red ring lines = Non-flower buds.
Fig 6:
Flower and non-flower
bud absorbance seen on the near-infrared spectrometer for ‘Fuji’ on March 15 and March 31, 2021;
33 and 3 days before bud burst (DBBB); Green and red lines = Flower buds; Green and red ring lines = Non-flower buds.
-0.1
-0.05
0
0.05
0.1
0.15
0.2
8
0
0
8
1
0
8
2
0
8
3
0
8
4
0
8
5
0
8
6
0
8
7
0
8
8
0
8
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9
9
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1
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1
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1
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1
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2
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1
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0
1
0
4
0
1
0
5
0
A
b
s
o
r
b
a
n
c
e
(
lo
g
(
1
/R
))
Wavelength (nm)
Near infrared part
Flower (33 DBBB)
Flower (3 DBBB)
Non-flower (33 DBBB)
Non-flower (3 DBBB)
0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
6
4
0
6
4
4
6
4
8
6
5
2
6
5
6
6
6
0
6
6
4
6
6
8
6
7
2
6
7
6
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8
0
6
8
4
6
8
8
6
9
2
6
9
6
7
0
0
7
0
4
7
0
8
7
1
2
7
1
6
7
2
0
7
2
4
7
2
8
7
3
2
7
3
6
7
4
0
7
4
4
7
4
8
7
5
2
7
5
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7
6
0
7
6
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7
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0
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2
7
9
6
A
b
s
o
r
b
a
n
c
e
(
lo
g
(
1
/R
)
)
Wav elength (nm )
Visible part
Flower (33 DBBB)
Flower (3 DBBB)
Non-flower (33 DBBB)
Non-flower (3 DBBB)
Volume 56 Issue 2 (April 2022)
219
depend indirectly on the availability of “free water amounts
of buds. Penuelas
et al
. (1997) reported that 680, 900 and
970 nm reflectance provide the best estimation of plant water
content. Fruitlet drop measured by visible/near-infrared
in situ
is beneficial in terms of time efficiency and its high
level of accuracy (Orlova
et al
., 2020). In previous research
we reported winter planted young ‘Miyabi Fuji’ trunk moisture
content changes (Botirov and Arakawa, 2021) and this bud
light absorbanc e changes might be oc c urred some
physiological changes of buds before bud burst.
Deficiency of this study is that we measured only within
a restricted wavelength range (640-1050 nm). Further
research should be done using a more comprehensive range
on the near-infrared spectrometer (above 1050 nm).
C
ONCLUSION
In this study, we investigated the non-destructive detection
of flower and non-flower buds before bud burst on a ’Fuji’
apple tree. W e found that the best time to detect and
differentiate between flower and non-flower buds, utilizing
a visible near-infrared spectrometer, is three days before
bud burst. We also observed significant changes in bud
chlorophyll in the flower and non-flower buds. This suggest
that deeper non-destructive measurements espec ially
adapted for chlorophyl might be distinguish flower from non-
flower buds before bud burst.
Hence, the use of a device that does not destroy the
bud can be beneficial for detecting flower and non-flower
buds before bud burst and can help growers in their
management of ‘Fuji’ apple orchards. The ultra-mini visible/
near-infrared spectrometer could offer apple growers a tool
that would enable them to distinguish flower from non-flower
buds, which could then help them fine tune their pruning
practices to better manage their orchards and forecast future
harvest yields. Additionally, researchers working on smart
agriculture technologies could use this data to develop
pruning robots that can identify and separate flower from
non-flower buds.
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