=Paper=
{{Paper
|id=Vol-3126/paper47
|storemode=property
|title=Spectral-spatial analysis of data of images of plantings for identification of stresses of technological character
|pdfUrl=https://ceur-ws.org/Vol-3126/paper47.pdf
|volume=Vol-3126
|authors=Natalia Pasichnyk,Dmytro Komarchuk,Korenkova Hanna,Sergey Shvorov,Oleksiy Opryshko,Nikolay Kiktev
}}
==Spectral-spatial analysis of data of images of plantings for identification of stresses of technological character==
https://ceur-ws.org/Vol-3126/paper47.pdf
Spectral-Spatial Analysis of Data of Images of Plantings for
Identification of Stresses of Technological Character
Natalia Pasichnyk1, Dmytro Komarchuk2, Hanna Korenkova 3, Sergey Shvorov4, Oleksiy
Opryshko5 and Nikolay Kiktev6
1,2,4,5,6
National University of Life and Environmental Sciences of Ukraine, Heroyiv Oborony st., 15, Kyiv, 03041,
Ukraine
3
Odessa I.I.Mechnikov National University, Dvoryanskaya str., 2, Odessa, 65082, Ukraine
Abstract
Methods of spectral-spatial analysis are promising for the identification of technological
stresses. The most common solution for interpreting the causes of stress is the use of machine
learning technologies, namely neural networks. As at technological stresses in particular at
chemical poisoning of crops, there can be various options of the coloring of the affected plants
the possibility of providing a sufficient amount of initial data for training of neural networks is
doubtful. An alternative is graph analysis of the distribution of stress areas on the field map.
Given the urgency of the problem for promising technologies of precision agriculture, the work
aimed to develop a spectral-spatial method of monitoring technological stresses, namely the
algorithm and software for its.
Experimental studies of the manifestation of technological stresses on winter crops on the
example of wheat and rapeseed were conducted during 2018-2020 in production fields using
universal cameras in the visible range and special multispectral Slantrange systems.
For remote monitor, the state of winter crops, an algorithm for identifying technological stresses
was developed, which is implemented in the developed software in Python for spectral-spatial
analysis of stress index maps. It has been experimentally confirmed in the production fields that
the use of the developed software allows identifying the contours of areas of plants with stresses
of technological nature based on stress index distribution maps.
Keywords 1
UAVs, winter crops, vegetation indices, stresses, herbicides
1. Introduction data processing systems for satellite data filtering
and machine learning technology for object
recognition. That is, in the spectral-spatial
The prospects for agricultural production
analysis, the field of the field as a whole was not
management based on objective remote
considered as the object of research. This
monitoring data were obvious both at the state
approach is used in particular in aviation for the
level and for agricultural enterprises.
implementation of orientation in the use of
Accordingly, research was carried out to develop
electronic warfare as shown in the work of S.
various theories and methods for obtaining
Shvorov and others (2018) in [2]. Regarding
information about vegetation. Under uncertainty,
agricultural production, Xianlong Zhang and
M. Lotfi et al. (2009) in [1] proposed computer
others (2019) in [3] proposed the division of
ISIT 2021: II International Scientific and Practical Conference
«Intellectual Systems and Information Technologies», September
13–19, 2021, Odesa, Ukraine
EMAIL: N.Pasichnyk@nubip.edu.ua (A. 1);
dmitruyk@gmail.com (A. 2); av.korenkova@gmail.com (A. 3);
sosdok@nubip.edu.ua (A. 4); ozon.kiev@nubip.edu.ua (A. 5);
nkiktev@gmail.com (A. 6);
ORCID: 0000-0002-2120-1552 (A. 1); 0000-0003-3811-6183 (A.
2); 0000-0001-7207-3688 (A. 3); 0000-0003-3358-1297 (A. 4);
0000-0001-6433-3566 (A. 5)
©️ 2021 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
spectral-spatial monitoring methods into 2 need to be adapted to respond to such problem
conditional categories. The first category uses the areas, as weakened plants are easily affected by
spectral characteristics of terrestrial objects and pests and can become a breeding ground for them.
then obtains vegetation information by comparing A possible technology for the analysis of
the difference with the results of spectral spectral-spatial distribution is artificial neural
monitoring. An example of such monitoring is the networks which, due to the rapid development of
identification of trees in densely populated cities multi-core processors, have become available to
based on satellite images shown in S.W. Myint et farmers. There is a positive experience of using
al. (2013) in [4]. neural networks for various monitoring tasks
The second category is based on a combination which, if necessary, can be adapted to monitor
of external knowledge such as decision tree for technological stresses. Section 1 shows that in the
image classification shown in Andrea S. Laliberte initial stages of the growing season, the
et al. (2007) in [5], neural networks, and wavelet dimensions of plants may indicate their stress.
transforms described in Mitch Bryson and others Neural networks for estimating plant height
(2010) in [6]. This promising method has not been during rice lodging are shown in Ming-Der Yang
widely used in satellite monitoring because the et al. (2020) [11]. According to the provided
combination of time delay and low resolution results, it was possible to detect rice lodging with
leads to unacceptably large errors, which was acceptable accuracy based on images from
shown in the article by Passang Dorji et al. (2017) universal cameras in the visible range, but the
in [7]. UAV monitoring is devoid of these calculations were performed using cloud services,
shortcomings and accordingly, this method can be which is difficult to implement in our country.
implemented on a new technological basis. Thus, Autonomous work of neural networks is shown in
in the work of J. Senthilnath et all (2017) [8], it the work of Wojciech Gruszczyński and others
was possible to successfully identify weeds in (2019) [12] to identify grass among general
crops by fixing plants in automatically vegetation. When analyzing the image was
determining technological tracks. segmented into parts and carried out training of
Wavelet analysis methods do not require the the network on the distribution of the cloud of
division of the image into blocks, because the points. This approach is promising for the
required localization properties are already identification of low-growing grass because only
embedded in the wavelet system. Accordingly, it one manifestation is considered, but under
is possible to filter out a significant number of technological stress, there may be more. In
errors inherent in pixel analysis methods. The principle, for neural networks, there can be
method of wavelet analysis for the identification several options for identifying objects. They can
of affected areas due to technological stresses, be used in particular to determine the state of rice
namely the prolonged action of herbicides was yield at the stage of ripening, as shown in Qi Yang
shown in the work of M. Dolia and others (2019) and others (2019) [13], or the state of mineral
in [9]. The proposed solution proved its nutrition described in V. Lysenko and others
effectiveness when, as a result of a dosing error in (2017) [14]. Spatial distribution was also
a part of the field, a higher dose of herbicides was considered in Yan Pang et al. (2020) [15] to
applied. Since the application was made by calculate the number of plants in a ridge. All these
appropriate ground equipment, the authors in the works are combined by a limited number of
analysis of the map image focused on the search classification options and a large sample of source
for linear functions. The authors noted some data for neural network training. In this case, in
difficulties in the established systems when contrast to the vegetation indices, which focus on
choosing thresholds. The complexity of this pixel-by-pixel analysis, the training of neural
controversial issue was confirmed by Yu-Hsuan networks was based on crop areas obtaining more
Tu et al. (2020) [10] where it was the limit values accurate results.
that were recommended to be studied at higher As at technological stresses in particular at
resolutions. Due to this specificity of the method, chemical poisoning of crops, there can be various
the analysis will be effective for the affected crops options of the coloring of the affected plants the
on a large scale, which significantly limits its possibility of providing a sufficient amount of
effectiveness. Large-scale impressions can be initial data for training of neural networks is
easily identified by satellite technology or doubtful.
ground-based monitoring, but small areas will be There are no ready-made software solutions
difficult to detect. Crop management technologies for analyzing the distribution of stress areas on the
field map to identify the nature of stress. Given controllers and flight control of UAVs, the
the urgency of the problem for promising prospects of the Python language for these tasks
technologies of precision agriculture, the work were emphasized primarily due to the use of
aimed to develop a spectral-spatial method of technical means from different manufacturers on
monitoring technological stresses, namely the different operating systems.
algorithm and software for its implementation.
2.3. Experimental research
2. The state of the issue
2.1. Identification of direction of Experimental studies of the manifestation of
technological stresses on winter crops on the
movement of technological example of wheat and rapeseed were conducted
equipment during 2018-2020 in production fields.
Photography was performed using: in 2018-2019,
Stressful conditions of crops of technological to monitor the stationary experiment and
character are caused by human actions which are fragments of production fields - hexacopter based
realized by the use of the ground technological on multi-rotor platform CD600 with a set of
equipment. The identification of equipment specialized sensor equipment in the digital action
directions was considered in Junfeng Gao et al. camera GoPro HERO4, in 2019-2020 -
(2018) [16] regarding the detection of weeds in multispectral system 3p, mounted on a DJI
row crops, where all plants between rows were Matrice 600 hexacopter, which allowed to obtain
considered weeds. In Carlos Henrique Wachholz orthophotos of industrial fields. It is the spectral-
de Souza et al. (2017) [17], sugar cane rows were spatial analysis of the obtained orthophoto plan
identified to estimate row gaps. In both cases, the that allowed us to establish the dependences on
rows were considered to be the arrangement of the basis of which the identification of
plants in a row, because this is how ground technological stresses is carried out.
equipment moves. However, in agricultural Technological stresses on winter oilseed rape
practices, the directions of ground equipment can be detected by means of leaf diagnostics
movement should change from year to year, and, because in September-November there is an
accordingly, the distribution of stress areas may abnormal color of the lower leaves, which is easy
differ from the direction of crop rows. to establish both by ground visual assessment and
Accordingly, the identification of stresses can be research using UAVs. For winter wheat, such
based on the assessment of the contour of the manifestations suitable for reliable identification
stress section, which for technological stresses from the UAV platform on an industrial scale
must have the correct geometric shape inherent (height from 60 meters) could not be detected. In
exclusively in artificial objects. In particular, in ground-based monitoring, it was noted that plants
the case of chemical poisoning of plants, the have a characteristic deformation of the leaves,
boundary between affected and healthy crops will which can be a characteristic criterion for
be directly linear. identifying the nature of stress. Affected areas
inside the field are more dangerous for industrial
fields, which are difficult, often impossible to
2.2. Choice of the software visually detect by ground monitoring means. This
situation is extremely dangerous, as areas with
environment weakened plants appear in the field, which is more
susceptible to pests and can become centers for
Assessing the nature of stress for crops is an the spread of the latter. Accordingly, it is
urgent task to be solved both by agronomists advisable to develop a technology that will
directly in the fields and by relevant specialists identify stress areas regardless of their location.
using cloud services. Accordingly, for the This was taken into account when choosing the
versatility of the operating system used, it is experimental production field.
advisable to use a cross-platform programming
language such as Python, which is adapted to the 2.4. Analytical research
fate of large data processing and machine
learning. In the work of Emad Ebeid and others
(2018) [18], devoted to the review of flight Laboratory studies accompanied all stages of
plantation monitoring. A sampling of plants and
soil was performed on the day of monitoring or 3.2. Data processing
within two days thereafter. Soil samples were
taken from a layer of 0-25 cm, prepared for 3.2.1. Evaluation of the contour of the
analysis according to DSTU ISO 11464: 2007. map
Agrochemical analysis was performed in
scientific and research laboratories of the To manage the harvest, farms, regardless of
Department of Agrochemistry and Plant Product weather conditions, need maps of the distribution
Quality, Ukrainian Laboratory of Agricultural of vegetation indices in many production fields
Products Quality, in compliance with accepted available on the farm. Based on these
methods and techniques. circumstances, the Slantrange sensor complex
was created to survey up to 10,000 ha/day, which
can be provided on aircraft platforms. Since the
3. Algorithm for identification of average area of production fields in the plains of
Ukraine is 70-100 hectares, it is desirable to
technological stresses, its software survey several fields at once during one flight. In
implementation, and results of the analysis of a particular field, it is necessary to
experimental data processing determine its boundaries. The Python-supported
OpenCV library contains ready-made procedures
3.1. Select the source data format for finding the contours of graphical objects that
can be used in this case. An example of the result
To process spectral monitoring data, the of card processing is shown in Figure 1.
Slantrange sensor system has its own Slantview
software, which allows you to save the received
maps in several data formats, namely Shapefile,
KMZ, GeoTiff. The shape format contains
attributive information of geometric objects and is
designed primarily to create tasks for ground
equipment. KMZ files are 3D data in Google
Earth and represent a map of the distribution of
vegetation indices on satellite images. According
to the results of experimental studies on the
recognition of the values of vegetation indices, it
was found that the data was distorted during the
overlay of the images - the recorded colors were Figure 1: Green Chlorophyll index distribution
missing in the palette for the specified vegetation
map (a) for wheat crops affected by the after-
indices. Probable explanation in image correction
effects of herbicides, (b) - a photograph in
for overlay on the satellite image to facilitate
visual perception by the user. By comparing the pseudo-colors of the area highlighted by the
data for the distribution points of the distribution square on the map, and the results of the field
map from the working window of the SlantView contour search by the proposed software (c)
program, it was found that for the GeoTiff format
color distortion and, consequently, the values of It should be noted that the forest-steppe zone
vegetation indices do not occur. Unlike the KMZ of Ukraine is characterized by strong forest belts,
format, the file does not have positioning labels, the leaf cover of which, as well as the shadow
but when you save the map, the program retains from them are also fixed by the system. From the
the scaling, and, accordingly, when using available experience, maintenance of forest belts
landmarks, the calculation of positioning is quite and their renewal is not carried out regularly and
possible. In view of the above, the GeoTiff format there are many cases when tree crowns
was adopted for analysis. completely cover the road surface due to which
significant errors are possible in determining the
contours of crops. Since the field boundaries are
stable, to analyze the presence of technological
stresses on the maps of the distribution of
vegetation indices stored in the Geotiff format, it
is advisable to enter them manually, using certain amount of data that does not belong to the field
reference points. under study, it is necessary to change the
orientation from geographical to local reference to
3.2.2. Estimation of the orientation of the dimensions of the field. Due to the change in
the orientation of the image, the number of pixels
the sections of the field caused by of the image obtained from the GeoTIFF file
stresses of technological character decreased from 1100 × 1660 to 245 × 1521, ie the
amount of data decreased almost 5 times.
With the identification of crop rows, the Figure 3 shows the interface of the developed
direction of crop rows was stable, but this is not a program in python to identify stressful areas of
prerequisite for technological stresses. Thus, technological nature.
Figure 1 (a) shows the presence of a green band
on the left and top, which for this index Green
Chlorophyll index corresponds to healthier crops
than those with yellow. This condition may be due
to the best condition of mineral nutrition at the
field boundaries because it is there that the
equipment slows down, turns, adjusts the
operation of nozzles, augers, and more. The width
of such a layer, as a rule, does not exceed the
radius of reversal of ground equipment, which can
be taken into account when analyzing the
distribution of stress areas
The distribution in the field of stress areas
caused by phytotoxic action (aftereffect) of Figure 2: Distribution maps of stress areas for the
herbicides, as well as violation of the seeding rate, GreenNDVI index for winter wheat crops from
is related to the direction of technological tracks,
April 27, 2019. Image of the Slantview software
the organization of which meets certain rules. This
map window (left) and converted from a GeoTIFF
is due to the fact that the introduction of chemical
reagents or seeds during sowing is not carried out file to jpeg format
in an arbitrary manner, namely in compliance with
the laid technological tracks. The directions of
technological tracks in one field can change from
year to year to maintain soil fertility, but their
number usually does not exceed 2, in some cases
3 directions. Soil loosening can be carried out in
any order, but technological stresses cannot be
caused by this operation. Determining the
direction of technological tracks has certain
prerequisites, so mechanics when planning work
are interested in the maximum length of the runs.
Accordingly, in the absence of data from
technological maps for the implementation of
mechanical tillage, the orientation of the Figure 3: Picture of the program command-line
experimental field should be carried out along the interface when entering analysis parameters
maximum length of the field.
Figure 2 presents a map of the distribution 3.2.3. Convert data from color format
of stress areas for winter wheat crops where to numeric view
chemical poisoning of winter wheat crops as
a result of the after-effects of herbicides from Figure 4 shows the map window palette, which
the predecessor crop was recorded. is used to encode data and save them in tiff format
The specificity of SlantView software data according to the method presented in S. Shvorov
processing is the observance of the north-south et all (2020) [19]. Since the NDVI indices for
geographical orientation. As a result, to reduce the plants change in the range 0… 1 for visualization
in the 8-bit color model, the index values were Figure 5: Image of the command line interface of
multiplied by 255 (Fig. 4) the program during the program with the output
of intermediate results of the analysis (in areas
highlighted in green probable stress of a
technological nature)
3.2.6. Filtering of the received data
Figure 4: The image of the Green NDVI map is Previous studies have found that stress should
listed from the Slantview software palette be determined by the magnitude of the standard
(encoding "shades of gray" where the index value deviation. For filtration, a limit value was set at
is multiplied by 255. which the stress status was set for the plots.
3.2.4. Image segmentation 3.2.7. Graph analysis for in-depth
search
To assess the presence of stress of a
technological nature, the field image was divided The stressful state of plantations is caused by
into separate sections. The size of the plot was chemical poisoning of plants or their thickening
determined based on the resolution of the due to non-compliance with production
distribution map and the standard nomenclature of technology when moving ground equipment.
ground equipment available on the farm. The size Accordingly, stress areas will form bands. The
was 13 × 13 pixels (6.5 × 6.5 m). DFS (Depth-first search) method was used to
identify such stress areas. That is, single
3.2.5. Calculation of distribution manifestations of plant stress due to differences
from the total mass of the water supply regime,
parameters etc. are not taken into account. The results
obtained are presented in Figure 6.
The GaussAmp equation was used to The developed software passed a production
approximate the experimental data on the color test, during which its accuracy and selectivity
intensity when color-coding the values of the were confirmed.
intensity of the GreenNDVI index. Determined
the value of the average value. Figure 5 shows the
image of the program interface when indicating
the intermediate results for statistical processing
of the distribution of index values on the map
segment.
Figure 6: Picture of the command line interface
of the program during the output of the analysis
results
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