environments
Article
Urban Environmental Quality Assessment by Spectral
Characteristics of Mulberry (Morus L.) Leaves
Snejana Dineva 1,2 , Petya Veleva-Doneva 1,2 and Zlatin Zlatev 1,2, *
1
2
*
Faculty of Technics and Technologies, Trakia University, 8602 Yambol, Bulgaria;
snezhana.dineva@trakia-uni.bg (S.D.); petya.veleva@trakia-uni.bg (P.V.-D.)
Department of Agricultural Engineering, Faculty of Agriculture, Trakia University, 6000 Stara Zagora, Bulgaria
Correspondence: zlatin.zlatev@trakia-uni.bg
Abstract: In this paper, an analysis of the possibility of passive determination of the degree of
environmental pollution based on data from the leaf blade of mulberry is made. With existing
solutions in this area, the mulberry has been found to be under-researched. A disadvantage of the
available solutions is that spectral indices are used, which is not a sufficient criterion for passively
determining the degree of air pollution based on the surface characteristics of the mulberry leaves.
Methods have been used to reduce the amount of data by latent variables and principal components.
It has been found that a kernel variant of the principal components, combined with linear discriminant
analysis, is an appropriate method for distinguishing the degree of air pollution from mulberry leaf
data. The results obtained can be used to refine the approaches used to passively determine the
degree of air pollution in the habitat area of the plant. Methods and software tools could be used to
develop mobile applications and new approaches to remote sensing, in express determination of the
degree of environmental pollution, according to data from the mulberry leaves.
Citation: Dineva, S.; Veleva-Doneva,
P.; Zlatev, Z. Urban Environmental
Keywords: passive biomonitoring; mulberry leaves; PCA; discriminant analysis; spectral indices
Quality Assessment by Spectral
Characteristics of Mulberry (Morus L.)
Leaves. Environments 2021, 8, 87.
https://doi.org/10.3390/
environments8090087
Academic Editors: Yu-Hsiang Cheng,
Elisabete Carolino and Chi-Chi Lin
Received: 29 July 2021
Accepted: 31 August 2021
Published: 1 September 2021
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Attribution (CC BY) license (https://
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1. Introduction
Environmental quality monitoring in urban areas is a method that offers the opportunity to avoid adverse effects on human health. Passive and active biomonitoring of air
quality has both advantages and disadvantages. Passive biomonitoring has the advantage
of using tree species already present in the ecosystem, making this approach affordable
and effective over time [1].
Detailed studies have been conducted on passive bio-monitoring of air quality based
on leaf data from Tilia (Tilia sp.) [2], hornbeam (Carpinus betulus) [3], and white willow
(Salix mucronata) [4]. In the studies of these plants, both classical laboratory methods
and contactless measurement techniques were used, such as spectral characteristics in
the visible and near infrared, as well as hyperspectral analysis. The requirements for the
application of different types of plants to passively determine the quality of the air in cities
include identifying features of pollution that alter plant characteristics, needing to develop
new methods, or refining existing ones [5–9]. The aim of this study is to address precisely
these requirements.
A plant that has a proven content of bioactive substances [10], is resistant to contaminated soils, and allows pesticide treatment [11] is the mulberry. Additionally, the
plant is sensitive to changes in the environment [12]. The economic importance of mulberries is to feed silkworms, cattle, and goats. In some cases, it is used as a park tree.
In Bulgaria it is mainly found near major roads in the cities. The wide use of mulberry
for creating plantations for different purposes (leaf mass, fruit, wood, landscaping), and
the versatile application that the individual parts of the tree have, determine its great
economic importance.
4.0/).
Environments 2021, 8, 87. https://doi.org/10.3390/environments8090087
https://www.mdpi.com/journal/environments
Environments 2021, 8, 87
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Many of the published results related to mulberry analysis and evaluation, include
spectral and hyperspectral analysis methods, to define different qualitative indicators
such as proteins, vegetable oils, starch, dry matter, moisture, acidity, various mycotoxins,
and infections affecting indicators, relating to the application of the plant for medical
and business purposes. For the most part, the laboratory methods used for analyzing
mulberries are subjective and require considerable time to process plant samples. The
accuracy of diagnosis is not high and depends on the expert’s qualifications. That is why
the creation of highly efficient automated technologies for the evaluation of mulberry
indicators is a priority objective of current research in this field. The purpose of spectral
methods is to speed up the process of determining the state of the environment, not to
replace classical laboratory methods.
Directive 2008/105/EC determines the good chemical status to be achieved by all
Member States of the European Union (including Bulgaria), the legal basis for monitoring
priority substances in sediments and flora and fauna. The laboratory and field methods
described in this normative document include determination of O2 , CO2 , CO, SOx, NOx,
Cl2 , H2 S, HCl, VOC, and PM in the air. Additionally, it describes determining the content
of heavy metals, anions, and pesticides.
There are few studies on the qualitative indicators related to the surface texture of
mulberry leaves, as well as the influence of the polluted environment in the habitat area of
the plant. The question of whether the mulberry is suitable for passive biomonitoring for
air quality remains unclear.
The purpose of this study is to analyze changes in the spectral reflectance characteristics of mulberry leaves depending on the level of air pollution in the habitat area of
the plant.
2. Material and Methods
The mulberry leaf samples were taken from six areas with high and low car traffic.
25 leaves were used from each area taken from the sun-exposed side of the trees. The
leaves were transported in a cooler bag. The measurements were made as soon as they
were delivered to the laboratory.
To determine the environmental parameters in the analyzed areas, an experimental setup was used, developed at the Faculty of Technics and Technologies, Yambol, Bulgaria [13].
The measuring device consists of a sensor module and a microprocessor control system
offering wireless communication.
By the system were measured: smoke gasses, ppm; particle matter PM > 0.5 µg/m3 ;
equivalent CO2 , (eCO2 ), ppm; and total volatile organic compounds, TVOC, ppb.
The measurements were made at a temperature of 22 ± 3 ◦ C and a relative humidity
of 39 ± 5% RH.
Table 1 shows the data on the areas in which the leaves were taken. Pollution rates
and geographical coordinates are indicated. The area is located in the southeastern part of
Bulgaria. Mulberries are more common urban plants in the studied geographical region.
Table 1. Areas for collecting mulberry leaves.
Zone
Degree of Air Pollution
Geographic Coordinates (WGS 84)
Z1
P
42◦ 28′ 44.20′′ N; 26◦ 31′ 24.19′′ E
Z2
P
42◦ 28′ 48.29′′ N; 26◦ 30′ 29.18′′ E
Z3
P
42◦ 28′ 55.27′′ N; 26◦ 30′ 15.23′′ E
Z4
LP
42◦ 15′ 25.34′′ N; 26◦ 37′ 35.29′′ E
Z5
LP
42◦ 15′ 26.24′′ N; 26◦ 37′ 35.71′′ E
Z6
LP
42◦ 28′ 40.75′′ N; 26◦ 30′ 25.27′′ E
LP—less polluted; P—polluted.
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The leaves are grouped into 2 groups—derived from less polluted zones (LP) and
polluted zones (P). Passive determination is equal to passive biomonitoring in this case.
2.1. Measurement of Air Environmental Parameters
To determine the environmental parameters in the analyzed zones, an experimental setup was used, developed at the Faculty of Technics and Technologies, Yambol, Bulgaria [13].
The measuring device consists of a sensor module and a microprocessor control system
offering wireless communication. The system measured smoke gasses, ppm; particle matter
PM > 0.5 µg/m3 ; equivalent CO2 , eCO2 , ppm; and total volatile organic compounds, TVOC,
ppb. The measurements were made at a temperature of 22 ± 3 ◦ C and a relative humidity
of 39 ± 5% RH.
2.2. Planar Chromatography
The method used was that presented in Priyadarshini et al. [14], with some modifications. The mulberry leaves from the polluted and less polluted areas were cut into
10 × 10 mm pieces. Their handles were removed. They were soaked for 4 h in acetone.
On a white paper with a density of 80 g/m2 and dimensions 105 × 19 mm, a drop of
extract was applied at a distance of 15 mm from the end of the paper. The sample was
dried for 15 min. Four mm from the end of the paper was immersed in acetone. After
15 min, the samples were removed from acetone and dried for 1 h. The values of the
5 separated fractions were then reported. Three replicates were made and the mean and
standard deviation of Rf were reported. Rf = A/B, where A is the distance recorded by the
solvent, and B is the distance reached by the corresponding fraction. Carotene, xantophyll,
chlorophyll a, chlorophyll b, and anthocyanin fractions are reported.
2.3. Determination of Physicochemical Parameters
The preparation of the measurement samples was carried out according to the procedure presented in AACC 02-52.01 [15], with some modifications suitable for the electrometric measurement of leaf parameters, in the following order: distilled water was heated to
70 ◦ C; the leaf mass was crushed and placed in distilled water at a ratio of 1/10 (5 g of raw
material in 50 mL of distilled water); stirring; and after cooling to ambient temperature,
3 consecutive measurements of each indicator were made and their average value and
standard deviation were determined.
Measuring instruments used: Technical balance MH-200 (ZheZhong Weighing Apparatus Factory, Yongkang, China), maximum defined mass 200 g, with a resolution of 0.02 g;
active acidity pH, pH meter PH-108 (Hangzhou Lohand Biological Co., Ltd, Hangzhou,
China); EC conductivity, µS/cm, Conductivity Meter AP-2 (HM Digital, Inc., Redondo
Beach, CA, USA); total dissolved solids, ppm, TDS-3 measuring instrument (HM Digital,
Inc., Redondo Beach, CA, USA); and redox potential ORP, mV, Measuring Instrument
Model ORP-2069 (Shanghai Longway Optical Instruments Co., Ltd, Shanghai, China).
2.4. Experimental Set-Up for Obtaining Spectral Characteristics
The experimental set-up used consists of a personal computer with software for
receiving and processing images and spectral characteristics in the visible and near-infrared
areas. The spectral characteristics of leaves were captured with a spectrophotometric
sensor TCS230 (TAOS Inc., Premstaetten, Austria). The sensor is controlled by singleboard microcomputer Arduino Nano compatible (Kuongshun Electronic Ltd., Shenzhen,
China). The measuring distance was 0.5 cm from the leaf to the sensor. White LEDs with a
maximum light intensity of 450 nm were used. The measurements are for 5 points of the
adaxial and also 5 points on the abaxial part of the leaves.
2.5. Obtaining Spectral Characteristics
The transformation of values from XYZ and LMS models into reflection spectra in the
VIS and NIR, in the 390–730 nm and 800–1000 nm ranges, was performed mathematically
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and the transformation was possible in both directions of equality [16]. Mathematical
dependencies, with the possibility of converting in both directions of equality:
X=
Z λ2
λ1
A(λ) X (λ)dλ; Y =
Z λ2
λ1
A(λ)Y (λ)dλ; Z =
Z λ2
λ1
A(λ) Z (λ)dλ
(1)
where A(λ) is a matrix for converting color to reflection spectra in the VIS range, for
accepted observer and illumination.
The used matrices for converting (matching functions) color components to spectrum
are available in [17] for the VIS region. Conversion functions for observer 2◦ are applied
(LMS 2◦ , CIE 2006).
The conversion to NIR was performed using the compliance functions presented
in [18]. The illumination data used to convert the VIS and NIR characteristics were in
accordance with D65 (average daylight with UV component (6500 K)) illumination. The
conversion function between the RGB and XYZ models, in the range λ1 –λ2 (380–780 nm),
can be represented as:
XYZ = RGB· M
0.5767 0.2974 0.0270
M = 0.1855 0.6273 0.0707
0.1882 0.0753 0.9911
(2)
where M is the transformation matrix under the specified conditions for observer 2◦ and
illumination D65. From here, the spectral characteristic is of the form:
p
SVIS = ∆X 2 + ∆Y 2 + ∆Z2
(3)
Conversion functions change the way spectral data is stored or the way that it is
represented. The conversion function in the range λ1 –λ2 (800–1000 nm) between the XYZ
and the LMS model can be represented as:
LMS = XYZ · T
0.7328 0.4296
T = −0.7036 1.6975
0.0030 0.0136
−0.1624
0.0061
0.9834
(4)
where T is the transformation matrix under the specified conditions for observer 2◦ and
illumination D65. From here the spectral characteristic is of the form:
p
SNIR = ∆L2 + ∆M2 + ∆S2
(5)
2.6. Determination of Information Indices by Spectral Characteristics
The NDAI (Normalized Dorsiventral Asymmetry Index) is defined as a linear combination of the reflections of the adaxial and abaxial parts of the leaves.
NDAI =
ρ I,ab − ρ I,ad
ρ I,ab + ρ I,ad
(6)
where ρI,ab is the reflection of the abaxial part of the leaf; ρI,ad —reflection of the adaxial part
of the leaf.
The two types of reflection of the leaves are determined in the same wavelength. The
blue part of the visible spectrum is at 420 nm, the green at 520 nm, and the red at 620 nm.
2.7. Reducing the Amount of Spectral Characteristics Data
Latent variables (LV), principal components (PC), and kernel variant of principal
components (kPC) were used to reduce the amount of spectral characteristics data [19]. The
kernel version uses three kernel functions: Simple; Polynomial; and Gaussian. Software
tools described by Wang [20] were used to obtain the kernel principal components.
The PCA kernel method can be summarized in the following steps:
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✓
✓
Creating a K kernel matrix from the training sample {xi } by:
Ki,j = k xi , x j
Gram
K′
matrix calculation:
K ′ = K − 1 N K − K1 N + 1 N K1 N
✓
Calculating vectors ai by dividing K by
(8)
K′ :
Kak = λk Nak
✓
(7)
(9)
Calculation of kernel principal components yk (x):
yk ( x ) = Φ( x ) T vk =
N
∑ aki k(x, xi )
(10)
i =1
2.8. A Correlation Method Was Used
This method determined the strength of the relationship between the NDAI spectral
index and the physicochemical characteristics of mulberry leaves. The distribution of the
data was checked by the methods: Shapiro–Wilks test; Kolmogorov–Smirnov test; and
Lilliefors test.
As a criterion for evaluation, a correlation coefficient R was used. At R < 0.3, there is
no or a very weak relationship between the data; at 0.3 < R < 0.5, the relationship is weak;
at 0.5 < R < 0.7 the relationship is moderate; and for R > 0.7 the relationship is strong.
2.9. Classification Methods Used
The Naïve Bayes classifier was used as a reference [21,22]. One of the classic algorithms
in machine learning is the Naïve Bayes Classifier, which is based on the Bayes theorem
for determining the posterior probability of an event occurring. Accepting the “naïve”
assumption of conditional independence between each pair of attributes, the Naïve Bayes
classifier effectively handles too many attributes to describe an example, i.e., with the
so-called “The curse of dimension”. Bayes’s theorem:
P(y = c| x ) =
P( x |y = c) P(y = c)
P( x )
(11)
where P(y = c|x) is the probability of an object belonging to a class c (posterior probability
of the class); P(x|y=c)—the probability of the object x to meet in the middle of the object
of class c; P(y = c)—unconditional probability of occurrence of object y in class c (a priori
probability of class); and P(x)—unconditional probability of object x.
The purpose of the classification is to determine to which class the object x belongs.
Therefore, it is necessary to find the probability class of the object x, i.e., it is necessary for
all classes to select the one that gives the maximum probability P(y = c|x).
copt =
argmax
P( x |y = c) P(y = c)
c∈C
(12)
The definition of boundary values for the separation of polluted and less polluted
zones, depending on the characteristics of the mulberry leaves, was made by discriminant
analysis using a linear separation function (LDA) [23]. LDA is suitable for datasets that
have high clustering and low variance. In general, the linear separating function is:
1
(13)
δk ( x ) = x T Σ−1 µk − µk T Σ−1 µk + log(πk )
2
where δk is a separating function; µk is the average vector; x—observations; and
Σ −1 —covariance matrix. For practical purposes, it is convenient to present the separation function as:
δ(v) = K + v· L
(14)
where K is a constant; L—linear coefficient; and v = [x;y]—vectors (matrix) of the data x
and y.
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Among use of the classifiers, one of the most important parts of the work is the choice
of an appropriate measure in order to properly assess the classification performance. The
evaluation of the performance of the classifiers used is based on a general classification
error, which is described by the formula:
e=
∑in=1 (∑nk=1 yik − yii )
·100 in %
∑in=1 ∑nk=1 yik
(15)
where yik is the number of class i samples classified by classifier in class k; yii —number of
correctly recognized samples; k = 1, . . . , n—number incorrectly assigned to a class i relative
to the total number of samples; and n—number of classes. All data were processed at a
level of significance of α = 0.05.
3. Results and Discussion
Effective application of mulberry leaves data to determine the degree of pollution of
the habitat area is entirely aimed at using methods that would be sufficiently effective with
respect to rapid and simple classification, and at the same time giving satisfactory accuracy
according to generally accepted standards to that end.
The results presented can be summarized in three groups. In the first stage, technological measurements of the mulberry leaves were made, including chromatographic and
physicochemical methods of analysis. In the second stage, data from the analysis of spectral
characteristics in the visible and near infrared spectral ranges are presented, both in their
direct use and by methods of the amount of data reduction and classification. Finally, a
discussion is made in which the results obtained are compared with those reported by
other authors.
The measured parameters of the environment in the habitats of mulberry are shown
in Table 2. It was found that in the high-pollution areas, the parameter values were
significantly higher than in the low-pollution zones.
Table 2. Mean annual pollutants concentration for polluted (P) and less polluted (LP) areas.
Zone
Parameter
Smoke Gasses, ppm
PM > 0.5 µg/m3
eCO2, ppm
TVOC, ppb
P
0.57 ± 0.1
79.32 ± 0.6
641 ± 37
35 ± 2
LP
0.06 ± 0.002
12.36 ± 1.4
422 ± 12
3 ± 0.3
Table 3 presents the results of planar chromatography on mulberry leaves from polluted (P) and less polluted (LP) areas. It is seen that for the leaves of the polluted areas, the
values of the individual parameters are significantly lower than those of the less polluted
areas. It is also seen that the coefficient of variation (CV) is below 30% (CV = SD/mean).
Table 3. Results from planar chromatography.
Component
Carotene
Xantophyll
Cholophyll a
Chlorophyll b
Anthocyanin
P
0.67 ± 0.11
0.53 ± 0.15
0.45 ± 0.14
0.26 ± 0.07
0.09 ± 0.02
LP
0.75 ± 0.08
0.62 ± 0.15
0.54 ± 0.16
0.31 ± 0.09
0.14 ± 0.04
Zone
Table 4 shows the results of physicochemical parameters of mulberry leaves from
polluted (P) and less polluted (LP) areas. Compared to the less polluted areas, the leaves
from the polluted areas have higher values of active acidity and redox potential, lower
values of electrical conductivity, and completely dissolved substances. It is also seen that
the coefficient of variation (CV) is below 30% (CV = SD/mean). As in the previous cases,
this indicator is higher for mulberry leaves than less polluted areas.
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Table 4. Physicochemical characteristics of mulberry leaf.
Characteristic
Zone
pH
P
7.8 ± 0.5
LP
7.5 ± 0.9
EC, µS/cm
μ
450 ± 34
514 ± 74
TDS, ppm
ORP, mV
130 ± 12
165 ± 12
139 ± 23
151 ± 21
Figure 1 shows the averaged VIS spectral characteristics for the adaxial and abaxial
part of mulberry leaves. It can be seen that the adaxial part has a separation between the
spectral characteristics between the leaves in the polluted and less polluted areas. Only
overlap 490–510 nm is observed. There is a strong overlap in spectral characteristics at
the abaxial part of the leaves. Only in the 380–500 nm range is there a visible resolution
between these characteristics.
(a)
(b)
Figure 1. VIS spectral characteristics of mulberry leaves (a) adaxial part and (b) abaxial part.
Figure 2 shows the averaged NIR spectral characteristics for the adaxial and abaxial
part of a mulberry leaf. Both parts have strong overlapping spectral characteristics. The
separation is observed in the 820–860 nm ranges as well as at 880–950 nm. In the second
spectral range, the separation of the characteristics is more pronounced at the abaxial part
of the leaves.
(a)
(b)
Figure 2. NIR spectral characteristics of mulberry leaves (a) adaxial part and (b) abaxial part.
Figure 3 presents the results for NDAI indices obtained from the correlation between
the spectral characteristics measured from the adaxial and abaxial parts of the leaf petal.
As can be seen from the figure in the mean values, there is a difference in the spectral
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indices for mulberry leaves from the polluted and less polluted area. Their standard
deviations overlap, which indicates that a breakdown of these indices cannot be made for
all measurement cases. These results indicate that the direct use of spectral characteristics
data is not appropriate in distinguishing between mulberry leaves from polluted and less
polluted areas.
Figure 3. NDAI VIS spectral indices.
The correlation between the NDAI spectral index and the physicochemical characteristics of mulberry leaves was evaluated. From the analysis of the distribution, it was found
that p = 0.07–0.09. At df = 18–74, it can be assumed that the data have a distribution close
to normal.
Figure 4 shows the correlation between the analyzed
λ values. At λ = 420 nm, corresponding to the blue color of the spectrum, a strong correlation (R > 0.7) of the NDAI index
was observed with anthocyanin, pH, EC, and TDS. At λλ= 520 nm, corresponding to the
green color of the spectrum, a strong correlation (R > 0.7) of the NDAI index was observed
with carotene, xanthophyll, chlorophyll a, and pH. At λ = 620 nm, corresponding to the
red color of the spectrum, a strong correlation (R > 0.7) of the NDAI index was observed
with carotene, xanthophyll, chlorophyll a, and pH.
Figure 4. Correlation between NDAI spectral index and physicochemical characteristics of mulberry leaves.
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The strong relationship between the NDAI spectral index and chlorophyll is due to
the fact that they absorb light most strongly in the blue part of the spectrum, as well as in
the red part. Conversely, they are a poor absorber of green and almost green parts of the
spectrum, which it reflects, producing a green color to tissues containing chlorophyll [24].
The possibility of distinguishing mulberry leaves from polluted and less polluted areas
was examined by using methods to reduce the amount of data of the spectral characteristics
of mulberry leaves in the visible and near infrared ranges of the spectra.
Figure 5 shows an example of the work of a Naïve Bayes classifier. The results shown
are using reduced spectral characteristics data from the adaxial part of the bilberry leaf,
reduced by the kernel principal components method.
(a)
(b)
Figure 5. An example of a Naïve Bayes classifier work: (a) VIS adaxial, kPCA simple and (b) NIR
adaxial, kPCA simple.
Table 5 shows the results of a common classification error using the accepted Naïve
Bayes classifier. It can be seen that, using latent variables and the linear variant of the
principal components, too-large values of the total classification error of over 45% are
obtained. This is an expected result, since these two methods produce reduced values that
are close in nature to the spectral characteristics.
Table 5. Common classification error (e, %) of the Naïve Bayes classifier.
Method
Leaf Part Spectra
VIS adaxial
LV
PC
kPC Simple
kPC Polynomial
kPC Gaussian
49%
45%
0%
1%
41%
VIS abaxial
41%
45%
0%
0%
12%
NIR adaxial
44%
48%
0%
0%
0%
NIR abaxial
45%
48%
0%
0%
0%
Significantly lower error values were obtained using the kernel variant of the principal
components. Only with the use of the Gaussian kernel, for spectral characteristics in the
visible spectral range, high values of the common classification error of more than 40%
are obtained.
For the next stages of work, a kernel variant of the principal components using the
simple and polynomial kernel functions is selected.
Figure 6 shows in general the results of using a linear discriminant classifier. It can be
seen that, when using a polynomial kernel, it produces worse results than using a simple
kernel. The data overlap, which is a prerequisite for increasing the classification error and
hence reducing the accuracy in distinguishing polluted and less polluted areas according
to mulberry leaf data.
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(a)
(b)
(c)
(d)
(e)
(f)
(g)
(h)
Figure 6. Linear discriminant classifier results, general view: (a) VIS adaxial, simple; (b) VIS abaxial, simple; (c) VIS adaxial,
polynomial; (d) VIS abaxial, polynomial; (e) NIR adaxial, simple; (f) NIR abaxial, simple; (g) NIR adaxial, polynomial; and
(h) NIR abaxial, polynomial.
Table 6 shows the results of a common classification error using a linear discriminant
classifier, in combination with the two methods selected to reduce the amount of data on
the spectral characteristics of mulberry leaves. Spectral data from the adaxial and abaxial
parts of the leaves, reduced with simple and polynomial variants of kPCA, were compared.
Table 6. Common classification error (e, %) for a linear discriminant classifier.
Leaf Part Spectra
Method
kPC Simple
kPC Polynomial
VIS Adaxial
VIS Abaxial
NIR Adaxial
NIR Abaxial
0%
1%
0%
0%
0%
0%
0%
0%
The results show that the lowest values of the common classification error are obtained
with the combination of linear discriminant classifier and kernel principal components
with “simple” kernel function. For this variant of application, the VIS and NIR spectral
characteristics, reduced by the specified method, are defined. The separation functions
defined are:
VIS adaxial : δ( PC1, PC2) = −0.2099 + [ PC1, PC2]·[0.155; −18.1305]
VIS abaxial : δ( PC1, PC2) = 0.0536 + [ PC1, PC2]·[0.0228; −68.4246]
h
i
NIR adaxial : δ( PC1, PC2) = −5.8636 + [ PC1, PC2]· −241.6463; −2.022 × 104
h
i
NIR adaxial : δ( PC1, PC2) = 8.3898 + [ PC1, PC2]· −252.4848; 4.1228 × 104
(16)
(17)
(18)
(19)
The results obtained indicate that the direct use of the spectral characteristics of the
adaxial and abaxial part of leaf petals to passively determine the degree of air pollution in
the area of the mulberry habitat is not appropriate. In contrast to the reported results for
Tilia leaves by Zadeh et al. [2], spectral indices obtained from the spectral characteristics of
the adaxial and abaxial part of the leaves cannot be directly used in mulberry analysis.
The data obtained corroborate those reported by Sun et al. [11], which use spectral
characteristics to predict pesticide content in mulberry leaves. For accurate prediction,
with an accuracy of 87%, a more sophisticated method of analysis is required, such as
regression of the support vectors. In the present work, the separation between leaves
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collected from polluted and less polluted areas is obtained after applying the kernel variant
of the principal components.
Prediction of water stress in mulberry caused by various factors, including air pollution, was reported by Bhosle et al. [24]. Their proposed method, using REP spectral index,
shows a predictive power of 93%. Similar high-resolution accuracy as shown in the present
work can be obtained using the averages of these spectral indices. According to the results
of other authors and of the results obtained here, it may be recommended to use complex
methods of analysis to evaluate changes in mulberry leaves, depending on the pollution of
the habitat area of the plant.
4. Conclusions
An approach has been adapted to passively determine the degree of pollution by the
spectral characteristics of mulberry leaves, based on extracted features and classification.
A comparative analysis of the application of methods for reducing the amount of data of
spectral characteristics was performed. This analysis found that the direct use of latent
variables and the linear variant of principal components is not appropriate in distinguishing
between polluted and less polluted areas in an urban environment, according to mulberry
leaf data, because the common classification error in using them exceeds 40%.
In the study conducted to determine the degree of air pollution by the spectral characteristics of the mulberry leaf, it was found that this can be realized with a common error of
0–1%, using a linear discriminant classifier, in combination with the kernel variant of the
principal components. Analytical dependencies of the separation functions were derived.
They were shown to be effective in solving the problem of determining the degree of air
pollution in the mulberry habitat.
A strong relationship between the NDAI spectral index and chlorophyll was found,
due to the fact that the mulberry leaves absorb light most strongly in the blue part of the
spectrum, as well as in the red part.
The results obtained improve and complement those reported in the available literature. They can be used to refine the approaches and methods used so far to passively
determine the degree of air pollution in the habitat area of the plant.
The proposed methods and software tools could be used in the development of mobile
applications and methods for remote measurement, in express determination of the degree
of environmental pollution, according to data from the mulberry leaves. More research
can be carried out in the subject area, including data on color and spectral indices, as well
as combinations of them. Organizing them into feature vectors and processing them with
methods to reduce the volume of data would increase the accuracy of forecasting the state
of the environment based on data from mulberry leaves.
Author Contributions: Conceptualization, S.D. and P.V.-D.; methodology, Z.Z.; software, Z.Z. and
P.V.-D.; validation, S.D., P.V.-D. and Z.Z.; formal analysis, S.D.; investigation, Z.Z.; resources, Z.Z.;
data curation, P.V.-D. and Z.Z.; writing—original draft preparation, Z.Z.; writing—review and
editing, S.D. and P.V.-D.; visualization, Z.Z.; supervision, S.D. All authors have read and agreed to
the published version of the manuscript.
Funding: This research received no external funding.
Data Availability Statement: The Data is available at: https://drive.google.com/file/d/16
DuLMoYipRPq2a0V5sbH53cwTZ_u7NK0/view?usp=sharing (accessed on 1 September 2021).
Conflicts of Interest: The authors declare no conflict of interest.
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