=Paper=
{{Paper
|id=Vol-3826/short16
|storemode=property
|title=Data protection in the automated agribusiness management system (short paper)
|pdfUrl=https://ceur-ws.org/Vol-3826/short16.pdf
|volume=Vol-3826
|authors=Bohdan Zhurakovskyi,Vadym Poltorak,Serhii Toliupa,Oleksandr Pliushch,Olena Nesterova
|dblpUrl=https://dblp.org/rec/conf/cpits/ZhurakovskyiPTP24a
}}
==Data protection in the automated agribusiness management system (short paper)==
https://ceur-ws.org/Vol-3826/short16.pdf
Data protection in the automated agribusiness
management system ⋆
Bohdan Zhurakovskyi1,†, Vadym Poltorak1,†, Serhii Toliupa2,*,†, Oleksandr Pliushch2,†
and Olena Nesterova3,4,†
1
National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute”, 37 Peremogy ave., 03056 Kyiv, Ukraine
2
Taras Shevchenko National University of Kyiv, 60 Volodymyrska str., 01601 Kyiv, Ukraine
3
Borys Grinchenko Kyiv Metropolitan University, 18/2 Bulvarno-Kudriavska str., 04053 Kyiv, Ukraine
4
Dragomanov Ukrainian State University, 9 Pyrohova str., 01601 Kyiv, Ukraine
Abstract
The paper discusses the development of components of the agribusiness management system, in particular:
a system for collecting and analyzing data from sensors, task management for foremen, integration of data
on the phases of crop development, and decision-making tools. A thorough analysis and selection of
development technologies that most effectively solve the tasks of agribusiness was carried out. Attention
was paid to the integration of different level components of the system and ensuring their harmonious
operation in real conditions. A data transmission network is selected and configured to ensure stable and
fast communication between system components. Data protection is provided through the use of SSL
certificates. The obtained results can be useful in the automation of similar or similar agricultural
enterprises.
Keywords
agribusiness, management system, data analysis, sensor integration, data protection, encryption,
optimization 1
1. Introduction This study proposes the development of an information
system that will use a variety of sensors to collect data on
In our world, agribusiness is one of the key industries that growing conditions, providing quality monitoring of soil
ensures food security and economic stability in most moisture, temperature, and chemical composition [2]. Based
countries, including Ukraine. However, global challenges on the received data, the system will be able to
such as climate change, geopolitical instability, and regional automatically regulate watering and fertilization, adapting
conflicts are making adjustments to traditional approaches to the current needs of plants and environmental conditions.
to agricultural production. This became especially relevant This will not only contribute to a more rational use of
for Ukraine, where the long devastating war against Russia natural resources but will also increase the yield and quality
caused significant losses of water resources due to the of agricultural products.
destruction of infrastructure, in particular, the terrorist Given the steady growth in demand for food products
attack on the Kakhovskaya HPP. This has led to a critical and the need to adapt to rapidly changing conditions, such
shortage of water, which is necessary for the cultivation of a system becomes relevant, offering a solution that helps
crops, especially in regions dependent on irrigation. Ukrainian farmers not only survive but also successfully
The relevance of the topic is enhanced by the need to compete in the world market.
optimize the use of available resources, reduce costs, and The purpose of this work is to develop an automated
increase the efficiency of agricultural production. The system for managing production processes in agribusiness
integration of advanced information technologies into these to increase the efficiency of resource use and the
processes opens the world to new opportunities for solving productivity of agricultural production.
the above-mentioned problems. In particular, automation The practical significance of the obtained results lies in
allows us to implement advanced methods of monitoring the possibility of implementing the developed automated
the condition of crops and optimizing the use of water and management system in agribusiness, which will
fertilizers, which is especially important in the context of significantly increase the efficiency of resource use, reduce
limited resources and the need to adapt to changing climatic costs, and increase the yield of crops.
conditions [1].
CPITS-II 2024: Workshop on Cybersecurity Providing in Information 0000-0003-3990-5205 (B. Zhurakovskyi);
and Telecommunication Systems II, October 26, 2024, Kyiv, Ukraine 0000-0001-9231-9411 (V. Poltorak);
∗
Corresponding author. 0000-0002-1919-9174 (S. Toliupa);
†
These authors contributed equally. 0000-0001-5310-0660 (O. Pliushch);
zhurakovskybiyu@tk.kpi.ua (B. Zhurakovskyi); 0000-0002-0402-0370 (O. Nesterova)
andr.vadym.2012@gmail.com (V. Poltorak); © 2024 Copyright for this paper by its authors. Use permitted under
Creative Commons License Attribution 4.0 International (CC BY 4.0).
tolupa@i.ua (S. Toliupa);
opliusch@yahoo.com (O. Pliushch);
o.nesterova@kubg.edu.ua (O. Nesterova)
CEUR
Workshop
ceur-ws.org
ISSN 1613-0073
267
Proceedings
To fulfill the set goals, it was necessary to solve the plants, the ability to react in real-time to changes in growing
following tasks: develop the system project, determine the conditions, and to provide timely adjustments to crop care.
components, subsystems, and methods of their interaction; One of the main advantages of automated systems is the
analyze existing solutions to justify the expediency and ability to centrally manage all aspects of production,
uniqueness of the development; create information blocks including monitoring soil conditions and controlling
for notifications and decision-making; to develop data moisture levels, temperature, and nutrient levels such as
processing and analysis algorithms [3]; ensure data potassium and nitrogen. Thanks to this, agronomists can
protection through authentication and authorization [4]; quickly make the necessary changes in agrotechnical
conduct system testing to identify and eliminate errors, measures, increasing the efficiency of resource use [13].
check reliability of data transmission [5–7], software The system must be user-friendly, reliable, and
stability and usability [8]. functionally complete to ensure easy implementation and
use in the field.
2. Description of the subject area
2.2.2. Development goals and objectives
2.1. Description of the process of
The main objectives are to develop a data acquisition
agribusiness activity subsystem, which is the source of input information, and a
Agribusiness involves complex and varied activities control system, which allows users to control important
oriented around seasonal cycles that determine the dates of parameters of the production process, such as irrigation and
sowing, tending, harvesting, and processing. These cycles fertilization. Agribusiness is a complex system that depends
vary depending on geographical location, type of crops, and on many factors such as seasonal cycles, climatic conditions,
climatic conditions. For example, spring is usually sowing, and market fluctuations. Effective management of these
summer requires intensive care and watering, autumn—is factors is critical to ensure sustainable development and
harvesting, and winter—planning for the next season, and increase productivity.
maintenance of equipment. The formulation of the task and the determination of the
The management structure in agribusiness includes purpose of the system showed the need for the introduction
several levels: the highest is focused on strategic planning; of automated systems to optimize production processes,
the middle level is responsible for coordination between reduce costs, and increase the efficiency of resource use.
departments, and the lower level ensures the The main goal is to create a tool that will allow you to
implementation of operational tasks in the fields and centrally manage all aspects of production, providing
factories [9]. Agribusiness faces many challenges, such as monitoring of soil conditions, and control of humidity,
climate change that introduces unpredictability to temperature, and nutrients. This will allow agronomists to
production cycles, pests and diseases that can spread make the necessary adjustments on time, which will
quickly, fluctuating market prices that require flexibility in increase the yield and quality of products.
financial planning, various political conflicts, wars, and Defining the goals and objectives of the development
other irresistible forces. All these factors require effective emphasized the importance of creating a data collection
management and implementation of the latest technologies subsystem and control system, developing data processing
to ensure sustainable development and reduce costs [10]. algorithms, and creating an adaptive user interface.
The implementation of automated systems can help to Completion of these tasks will ensure effective
optimize production processes, reduce resource losses, and implementation of the system in agribusiness, which will
increase the overall productivity of agriculture. Such allow farmers to make informed decisions based on up-to-
systems allow collecting and analyzing data on the date data and increase their competitiveness in the market.
condition of the fields, weather conditions, and the level of Analysis of ready-made solutions on the market showed
moisture and nutrients in the soil. This helps to make more that there are several advanced agribusiness management
informed decisions about crop care, irrigation, and systems, such as AgroTop [14] and AgriChain [15], which
fertilization, which ultimately increases yield and product provide a wide range of functionality for large
quality [11, 12]. agribusinesses. AgroTop focuses on task automation,
performance monitoring, and data visualization, while
2.2. Setting the problem AgriChain offers an end-to-end solution that includes land
bank management, agro-production, warehouse logistics,
2.2.1. Purpose of the system and crop monitoring.
Automated agribusiness management systems are aimed at AGRIUNO has certain differences and advantages,
solving many serious problems that have traditionally including its focus on smaller farms. It offers the separation
complicated agricultural production. The main goal of the of functionality into separate roles, which ensures ease of
system is to improve production management processes, use without overloading users with unnecessary
such as improving the facilitation of communication information. In addition, our system allows agronomists to
between employees of different levels, reducing costs, and manage the phases of crop development, set threshold
more efficiently distributing resources. values for sensors, and monitor average sensor values
The system is being developed as a tool that allows you through graphs. An important advantage is also the
to significantly simplify and optimize processes due to automation of data collection and analysis, which allows
automation and detailed control of key indicators. This farmers to make informed decisions based on up-to-date
includes the use of sensors to collect data on the state of data.
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Table 1 submission, editing, and monitoring of tasks, as well as
Comparison with existing solutions ensure the storage and updating of data in the database in
Functionality AgroTop Agri Chain AGRIUNO real-time.
Planning and Prospects for development include expanding the
analysis of crop Yes Yes No functionality, adding new functions, such as tracking the
rotation condition of the equipment, and integrating with mobile
Automation of
setting tasks and applications for fieldwork. It is also possible to use the
Yes Yes Yes system in other countries, adapting to local conditions and
control of
execution requirements, as well as constantly improving the interface
Visualization and to ensure greater convenience and accessibility for users.
Yes Yes Yes
monitoring
Performance
analysis
Yes Yes Yes 3.2. Requirements for functional
Separation of
No No Yes characteristics
functionality
Field List of functions, tasks, or sets of tasks to be automated:
No Yes Yes
management
Tasks and Automation of the process of user login to the
No Yes Yes
execution control
Monitoring and
system (agronomists, foremen, managers) with
No Yes Yes identity verification.
notifications
Data collection No Yes Yes Ensuring authentication of users when entering
the system, and supporting roles.
Thanks to this, our system provides effective Storage of data on fields, cultures, and phases of
management of production processes, facilitates decision- their development.
making, and increases the productivity of farms. It is Setting limit values for sensors according to the
affordable, easy to use, and requires no additional training, phases of crop development.
making it attractive to smaller agribusinesses seeking to Monitoring the average values of the sensors
implement modern management technologies without through graphs [16].
incurring significant costs. Notification of the deviation of sensor data from
the set limit values [17].
3. Formation of system requirements Creation and editing of tasks for foremen.
Monitoring the status of tasks, assigning tasks to
3.1. Requirements for the system as a foremen monitoring their execution, and receiving
whole notifications about urgent tasks and important
The AGRIUNO system consists of several main subsystems, messages.
each of which performs specific functions necessary for Data collection from humidity and temperature
effective agribusiness management. The authentication sensors. Manual data entry for potassium and
subsystem provides secure user access to the system, nitrogen sensors. Analysis and visualization of
supporting the roles of agronomist, foreman, and manager. collected data.
The field management subsystem allows agronomists to The system should ensure fast execution of
manage information about fields and crops, and store data requests and data processing. Any operation
about fields, crops, and their development phases. It also should not take more than a few seconds.
supports setting limit values for sensors and monitoring The system must be fault-tolerant and provide
field status through graphs and alerts. data backup. It should be possible to quickly
The task management subsystem provides the ability to restore data in the event of a crash.
create and edit tasks for foremen, monitor the status of task The user interface should be easy to use and
execution, assign tasks to foremen, and control their understandable even for inexperienced users. All
execution. functions should be easily accessible and
The monitoring and data collection subsystem provides understandable.
data collection from humidity and temperature sensors, as
well as manual data entry for potassium and nitrogen These requirements ensure that the AGRIUNO system will
sensors. It includes notifying users about indicators efficiently perform all the necessary functions, ensuring the
exceeding the set limit values. accuracy, reliability, and speed of data processing, which are
The administrative subsystem coordinates and manages critical for agribusiness management.
production processes, providing monitoring of tasks and the
status of fields, review, and analysis of used resources, as 3.3. Requirements for types of security
well as management of users and their roles.
3.3.1. Information support
The system must be available to users 24/7 without
interruption, with the ability to easily expand to support a Information support includes data structures, methods of
growing number of users and data. It should provide a high storage, processing, and management.
level of user data protection, provide the possibility of
registration and login to their accounts, support the
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Database tables to store data on users, fields, 4. Development of an information
cultures, development phases, tasks, sensors, and
resources.
system
Using a relational database (MongoDB) to ensure 4.1. System structure
data integrity and consistency [18].
The AGRIUNO system consists of several main components,
Storing data in the form of JSON documents
each of which performs specific functions to ensure
ensures flexibility of the data structure and ease of
effective agribusiness management. The main components
scaling [19].
of the system include a client part, a server part, an
Data filtering and sorting methods for efficient
administrative interface, management devices, end devices,
search and processing of information about fields,
and security modules.
crops, and tasks [20].
The client part includes a web interface designed for
Using MongoDB queries to interact with the user interaction with the system through a web browser.
database, provide fast access, and process large The web interface is implemented based on the Vue.js
volumes of data [21]. framework [31], which ensures a dynamic and interactive
user experience. It provides access to system functionality
3.3.2. Software
for agronomists, foremen, and managers. The server part
Software includes software components that ensure the consists of a web server that processes requests from the
functioning of the system. client part and interacts with the database. The web server
is implemented based on the Express.js framework [32],
Web server for processing user requests and which runs on the Node.js platform [33] and includes an API
providing access to the database [22]. for data exchange between the client part and the server,
Web application based on Vue.js for the providing task processing logic, field status monitoring,
interaction of users with the system, including user management, and other functions. The database uses
agronomists, foremen, and managers [23]. MongoDB [34] to store data in the form of JSON documents
Interfaces for field monitoring, task management, [35], which provides flexibility in data structure and ease of
and resource utilization analysis. scaling. The administrative interface is represented by the
Admin panel to monitor and manage tasks, fields, manager panel, which is designed to monitor and manage
and users. all aspects of the system. It is integrated with a web server
Tools for viewing and analyzing data. and database to provide access to up-to-date information
Software tools for authentication and and provides tools for viewing and analyzing data,
authorization of users, ensuring confidentiality monitoring tasks and field status, and managing users and
and protection of information [24]. their roles.
Use of encryption protocols to protect data during Control devices include pumps, dispensers, and valves
used for automated irrigation and fertilizer management.
transmission between the client and the server
These devices are controlled through a gateway that
[25].
receives commands from the backend [36]. End devices
3.3.3. Technical support include sensors that collect data on moisture, temperature,
nitrogen, and potassium in the soil, as well as a device for
Technical support includes the hardware and infrastructure collecting and processing information that transmits the
necessary for the functioning of the system. collected data to the server part via the Internet.
Security and data protection modules ensure
Servers to ensure high performance and reliability confidentiality and protection of information with the help
of system operation [26]. of software tools for authentication and authorization of
Data storage systems for storing large amounts of users, using encryption protocols to protect data during
information and providing quick access to it [27]. transmission between the client and the server [37, 38].
High-speed network connections ensure fast The encryption process is based on the use of SSL
access to the system and the processing of requests certificates. These are electronic documents that certify that
in real-time [28]. the website owner is a valid organization. When installing
Backup communication channels to ensure an SSL certificate, the owner’s identity is verified by a
uninterrupted operation of the system in case of trusted third party—the Certificate Authority (CA). This
failure of the main channel [29]. process ensures that the data you send to the website will
Computers, laptops, and mobile devices for user be securely protected from unwanted intrusions or other
access to the system ensure ease of use at any stage digital threats.
of the production process [30]. Data encryption process. Stages:
Thus, the AGRIUNO system will be equipped with all 1. Connection initialization: the website URL is
the necessary software and technical support, which will entered, and the browser initiates a connection to
allow it to effectively perform all the necessary functions for the web server.
agribusiness management, ensuring high productivity, 2. Sending the public key: The web server sends the
reliability, and data security. public key from its SSL certificate to your browser.
270
3. Certificate Verification: The browser checks the Actors and functions
web server’s SSL certificate to ensure that it is Manager:
valid and appears to be a trusted third party.
4. Generation of a shared secret key: The browser Authorization: login to the system.
generates a shared secret key that will be used for Ability to add and remove fields.
further data encryption. View information about the status of the fields.
5. Symmetric Cipher Encryption: Using the web View comments from an agronomist.
server’s public key, the browser encrypts the View information about tasks and their status.
shared secret key that it sends back to the server.
6. Decrypting the secret key: The web server uses its Agronomist:
private key to decrypt the shared secret key that
was sent by the browser. Authorization: login to the system.
7. Secure data transmission: Now that the browser Management of crop development: Planning and
and web server share a secret key, all data control of crop development phases.
transmitted between them is encrypted with a Sensor settings: Setting limit values for sensors
symmetric cipher using that key. according to the phases of crop development.
Monitoring indicators: Viewing the average values
Encryption with SSL has many advantages. Among
of the sensors with the help of graphs.
them:
Field Status Review: Assessment of current crop
Confidentiality. The data you transmit over the status and development phases in each field.
Internet remains confidential and unintelligible to Assignment of tasks: Distribution of tasks between
unwanted persons. foremen.
Data integrity. SSL protection ensures that data Commenting: Adding comments on the status of
during transmission will not be changed by fields and providing recommendations.
attackers. Receiving notifications: Notifications about the
Web server authentication. You can be sure that deviation of sensor indicators from the established
you are interacting with exactly the website you norms.
intended to visit. View information about tasks and their status.
The system architecture provides scalability that allows Brigadier:
the system to be easily expanded to support a growing
number of users and data, reliability that includes high Authorization: login to the system.
performance and system stability with the ability to backup Data entry: Daily data entry from sensors.
and quickly restore data in the event of a crash, and ease of View information about tasks and their status.
use thanks to an intuitive interface that makes it easier for Execution of tasks: Implementation of tasks set by
both beginners and experienced users. the agronomist.
Notifications: Receive urgent tasks and important
4.2. Functional model of the system messages.
To ensure effective agribusiness management, the
AGRIUNO system includes different user roles, each of 4.3. Database model
which has its functional responsibilities. These roles or The database model of the AGRIUNO system defines the
actors interact with the system to perform specific tasks, structure of the data and the relationships between the
manage processes, and monitor the status of fields. Below various data elements in the system. Below are the main
are the functional responsibilities and functions of each tables (collections) and their attributes that provide system
actor. functionality.
The following is a description of the collections in the
database:
Users collection:
_id (ObjectId) is a unique user identifier
UserID—numeric user ID
Name—user name
Role—user role (agronomist, foreman, manager)
login—login to enter the system
password—password for logging into the system
Fields collection:
_id (ObjectId) is a unique field identifier
FieldID—numeric identifier of the field
Name—field name
Figure 1: Detailed structural diagram of the system
ForemanID—identifier of the foreman responsible for the
field
271
PhaseID is the identifier of the current phase of culture fields and tasks: A one-to-many relationship where one
development field can have many tasks.
fields and sensors: A one-to-many relationship where
Table 2
one field can have many sensors.
Description of database tables
fields and phases: A one-to-one relationship where one
Collection Appointment field can have one current phase.
This collection stores information about
sensors and measures: A one-to-many relationship
system users. It includes data for managing
users where one sensor can have many measurements.
access to the system, authentication, and
authorization.
This collection stores information about fields,
fields including their names, foreman IDs, and
current crop development phases.
This collection stores information on the
development phases of crops, including their
phases
descriptions and threshold values for various
parameters.
This collection stores information about tasks,
tasks including their descriptions, statuses, and
creation dates.
This collection stores information about
sensors sensors installed in fields, including their types
and locations.
This collection stores information
measures about measurements, including the value,
time, and date of the measurement.
Phases collection:
_id (ObjectId)—unique identifier of the phase
PhaseID—numeric identifier of the phase
Name—the name of the development phase
Description—phase description
HumidityMin—the minimum level of humidity
HumidityMax—the maximum level of humidity
TemperatureMin—the minimum temperature
TemperatureMax is the maximum temperature
PotassiumMin—the minimum level of potassium
PotassiumMax—the maximum level of potassium
NitrogenMin—the minimum level of nitrogen
NitrogenMax—the maximum level of nitrogen
Tasks collection:
_id (ObjectId) is the unique identifier of the task
TaskID—numeric identifier of the task
Description—task description Figure 2: ER diagram of the database
FieldID—identifier of the field to which the task belongs
This database model provides efficient storage,
Status—task status (new, in progress, completed)
management, and processing of data necessary for the
CreationDate—date and time of task creation
operation of the AGRIUNO system, allowing monitoring,
Sensors collection:
analysis, and management of production processes in
_id (ObjectId) is the unique identifier of the sensor
agribusiness.
SensorID—numerical identifier of the sensor
Type—sensor type (temperature, humidity, potassium,
nitrogen)
4.4. Data transmission and processing
FieldID—identifier of the field where the sensor is The agribusiness management system provides storage,
installed processing, and presentation of various data necessary for
Measures collection: the optimization of production processes and decision-
_id (ObjectId)—unique identifier of the dimension making. Below is a list of input data required for the system
MeasureID—numeric identifier of the measurement to function:
SensorID—the identifier of the sensor to which the
measurement is linked Sensor data: Sensors are placed in fields to monitor
Value—value of measurement various parameters such as soil moisture,
MeasureDate—date and time of measurement temperature, nitrogen, and other nutrients. This
Relationships between tables: data is critical for assessing the current condition
users and fields: A one-to-one relationship where one of the fields and making decisions about irrigation,
user (foreman) can be responsible for one field. fertilization, and other agrotechnical measures.
272
Field Information: Includes details about each field, graphs based on the collected data, taking into account the
such as location, name, foreman’s name, and other irregularity of the data. The main solution methods can be:
characteristics. This information makes it possible
to better plan work in the fields and monitor their Interpolation: Used to fill gaps between irregular
condition during the season. data.
Phase information: Data on the different phases of Calculation of average values: To evaluate the
plant growth, including sowing time, periods of current state of the fields.
active growth, flowering, ripening, and harvest. Graphing: To visualize changes in indicators over
The data includes limit values for sensors. This time and make decisions about crop care.
allows you to coordinate agrotechnical measures
at the optimal time to achieve the maximum yield. 5.4. Description of the solution method
Information for authorization: Data for 5.4.1. Processing irregular data
registration, authentication, and authorization of
system users. Include user logins, passwords, and To process irregular data from sensors, the interpolation
roles, providing access control and data security. method can be used to fill the gaps between the received
Information about users: Includes personal data data and ensure the continuity of the analysis [40].
about users, their contact information, and roles. Interpolation:
This allows you to manage users and ensure the
– For each sensor and parameter, we determine time
appropriate level of access to various system
intervals where there is no data.
functions.
– We use linear interpolation to fill these gaps.
Tasks to foremen: Data about tasks assigned to
𝑀 (𝑡 ) − 𝑀 (𝑡 )
foremen, including task descriptions, deadlines, 𝑀 (𝑡) = 𝑀 (𝑡 ) + × (𝑡 − 𝑡 )
𝑡 −𝑡
and other necessary details. This helps to organize
where 𝑡 and 𝑡 are the times between which the
work in the fields and ensures timely
interpolation is carried out, 𝑀 (𝑡 ) and
implementation of agrotechnical measures.
𝑀 (𝑡 ) are the value of the sensor at these
The output of the system includes the results of sensor moments [41].
data analysis, which are presented in the form of reports and
graphs, allowing agronomists to assess the condition of the 5.4.2. Calculation of average values of
fields in real-time. The system also provides task indicators
management tools to foremen, including creating, After interpolation of the data, it is possible to calculate the
assigning, and monitoring task completion. average values of indicators for a certain period [42]. Input
data: 𝑀 (𝑡) is the measurement value from the sensor 𝐷
5. Mathematical support on the field 𝑃 at a moment in time 𝑡. 𝑇 is the period over
5.1. Meaningful formulation of the problem which the average value is calculated (for example, a week).
The formula for calculating the average value [43]:
The agribusiness management information system is aimed 1
at optimizing the use of resources, monitoring the condition 𝑋 (𝑃 ) = 𝑀 (𝑡)
|𝑇|
of fields, managing the phases of crop development, and ∈
providing recommendations for crop care. The goal of the where 𝑋 (𝑃 ) is the average value of the indicator 𝑋 on the
system is to increase production efficiency, reduce costs, field 𝑃 ,|𝑇| is the number of measurements per period T.
and improve crop quality. An example of calculating the average humidity value.
Suppose there is a field 𝑃 with three humidity sensors
5.2. Mathematical formulation of the 𝐷 , 𝐷 , 𝐷 , and we have the measurements for the last
problem week. The input may look like this:
𝑀 , (𝑡) = 70%
The mathematical model of the agribusiness management 𝑀 , (𝑡) = 75%
system may include the following components [39]:
𝑀 , (𝑡) = 72%
Set of fields: P = {P , P , . . . , P }, where 𝑃 is a separate
The average value of humidity in the field 𝑃 for the last
field.
week:
Set of sensors: D = {D , D , . . . , D }, where 𝐷 is the 1 1
separate sensor. 𝐻 (𝑃 ) = (𝑀 , (𝑡) + 𝑀 . (𝑡) + 𝑀 . (𝑡)) = (70 + 75 + 72) = 72.33%
3 3
A set of measured parameters: X = {T, H, K, N}, where T
is temperature, H is humidity, K is potassium, N is nitrogen. 5.4.3. Formation of graphs
Data requests 𝑀 are the measurements from the Graphs of indicators are created based on the collected data
sensor 𝐷 on the field 𝑃 at a moment in time t. to visualize changes in indicators over time.
Input data: A set of measurements 𝑀 (𝑡) for each
5.3. Justification of the solution method sensor 𝐷 on the field 𝑃 for a certain period.
To solve the task of monitoring and analyzing the condition The process of building a schedule:
of the fields, it is necessary to develop a method of
calculating the average values of the indicators and forming Collected data are grouped by time.
273
For each point in time, the average values of indicators. These methods allow for continuous data
indicators for each field are calculated. analysis, which helps to accurately monitor the condition of
Data is plotted on a graph where the x-axis the fields.
represents time and the y-axis represents metric The process of constructing graphs for visualization of
values. changes in indicators over time is described, which allows
agronomists to quickly assess the dynamics of changes in
An example of graph construction: temperature, humidity, and levels of potassium and
Suppose we have a temperature measurement in the nitrogen in the fields. Data visualization on graphs is an
𝑃 field in a week: important tool for making informed decisions about crop
To construct a graph, the data is entered as points on care.
the graph and connected by a line to show the trend of The proposed methods and approaches to data
temperature changes for a week (Fig. 3). processing allow the system to effectively perform the
functions of monitoring and managing agrarian processes.
Table 3 This helps to optimize the use of resources, and increase
Example data productivity and product quality, which ultimately ensures
Time Temperature (℃) the sustainable development of agribusiness.
01.06.2024 25 Special attention was paid to the development of
02.06.2024 26 algorithms for analyzing sensor data and making decisions
03.06.2024 27 about crop care. The most effective methods of analysis
04.06.2024 24
05.06.2024 26 were selected and implemented, which ensure high
06.06.2024 25 accuracy of forecasting and optimization of agronomic
07.06.2024 27 processes. This included the use of modern technologies for
data collection, processing of large volumes of information,
and machine learning.
Further research and development can be aimed at
expanding the functionality of the system, including
support for additional types of sensors, integration with
other control systems, and the use of the latest technologies
for data analysis. This will further increase the efficiency of
agribusiness and ensure the sustainable development of this
important industry.
References
Figure 3: An example of a schedule [1] O. Kopiika, P. Skladannyi, Use of Service-Oriented
Information Technology to Solve Problems of
The graph will help agronomists quickly assess the Sustainable Environmental Management. Information
dynamics of temperature changes in the field and make Technology and Mathematical Modeling for
appropriate decisions about crop care. Environmental Safety 3021 (2021) 66–75.
[2] B. Zhurakovskiy, N. Tsopa, Assessment Technique
6. Conclusions and Selection of Interconnecting Line of Information
Networks, 3rd International Conference on Advanced
The development of an automated agribusiness Information and Communications Technologies
management system is a complex and multifaceted task that (2019) 71–75. doi: 10.1109/AIACT.2019.8847726.
requires deep knowledge in the fields of information [3] B. Zhurakovskyi, et al., Processing and Analyzing
technology, agronomy, and data management. The main Images based on a Neural Network, in: Cybersecurity
goal of the project was to create an integrated system that Providing in Information and Telecommunication
provides effective management of fields, monitoring of soil, Systems, vol. 3654 (2024) 125–136.
plant, and resource conditions, as well as optimization of [4] H. Jaasko, Search Engine Optimization When
production processes. Entering New a Market, Business Information
The developed agribusiness management system makes TechnologyOulu University of Applied Sciences
it possible to significantly increase the efficiency of (2018) 1–45
production processes, ensuring accurate monitoring of the [5] C. Berrou, A. Glavieux, Near Optimum Error
state of the fields and optimal use of resources. The system Correcting Coding and Decoding: Turbo-Codes, IEEE
provides users with the opportunity to respond to changes Trans. On Comm. 44(10) (1996) 1261–1271.
in conditions on time and make informed decisions [6] P. Jung, J. Plechinger, Performance of Rate
regarding the care of crops. It also provides a convenient Compatible Punctured Turbo-Codes for Mobile Radio
interface for interacting with the system, which facilitates Applications, Electronics Lettes, 33(25) (1997) 2102–
its use and increases user satisfaction. 2103.
Key aspects of irregular data processing are considered, [7] S. J. Lin, W. H. Chung, Y. S. Han, Novel Polynomial
including the use of interpolation methods to fill gaps Basis and its Application to Reed-Solomon Erasure
between measurements and calculate average values of Codes, in: IEEE 55th Annual Symposium on
274
Foundations of Computer Science (FOCS) (2014) 316– Providing in Information and Telecommunication
325. Systems, vol. 3421 (2023) 67–76.
[8] J. Bergstra, Y. Bengio, Random Search for Hyper- [26] B. Zhurakovskyi, et al., Comparative Analysis of
Parameter Optimization, J. Machine Learning Res. 13 Modern Formats of Lossy Audio Compression, in:
(2012) 281–305. Cyber Hygiene, vol, 2654 (2020) 315–327.
[9] Agricultural Business. ВУЕ. URL: https://vue.gov.ua/ [27] N. Fedorova, et al., Software System for Processing
[10] K. P. Broadbent, Agribusiness, Commonwealth and Visualization of Big Data Arrays, Advances in
Bureau of Agricultural Economics (1974). Computer Science for Engineering and Education,
[11] A. Volovyk, et al., Fault Identification in Linear LNDECT, 134 (2022) 324–336. doi: 10.1007/978-3-031-
Dynamic Systems by the Method of Locally Optimal 04812-8_28.
Separate Estimation, TCSET 2022: Emerging [28] V. Druzhynin, et al., Features of Processing Signals
Networking in the Digital Transformation Age, LNEE, from Stationary Radiation Sources in Multi-Position
965 (2023) 634–651. doi: 10.1007/978-3-031-24963- Radio Monitoring Systems, Cybersecurity Providing
1_37. in Information and Telecommunication Systems, vol.
[12] B. Zhurakovskyi, et al., Traffic Control System Based 2746 (2020) 46–65.
on Neural Network, Digital Ecosystems: [29] B. Zhurakovskyi, et al., Modifications of the
Interconnecting Advanced Networks with AI Correlation Method of Face Detection in Biometric
Applications, LNEE, 1198 (2024) 522–542. doi: Identification Systems, Cybersecurity Providing in
10.1007/978-3-031-61221-3_25. Information and Telecommunication Systems, vol.
[13] Automated Systems. URL: https://www.freedomgpt. 3288 (2022) 55–63.
com/wiki/automated-systems [30] B. Zhurakovskyi, et al., Smart House Management
[14] AgroTop. URL: https://fieldbi.io/agrotop System, TCSET 2022: Emerging Networking in the
[15] Agrichain. Agribusiness Management System. URL: Digital Transformation Age, LNEE, 965 (2023) 268–
https://agronews.ua/news/agrichain-iedyna-systema- 283. doi: 10.1007/978-3-031-24963-1_15.
upravlinnia-ahrobiznesom/ [31] Vue.js. The Progressive JavaScript Framework. URL:
[16] N. Sabharwal, S. G. Edward, Practical MongoDB: https://vuejs.org/
Architecting, Developing, and Administering [32] A. Mardan, Express.js Deep API Reference, Apress
MongoDB, Apress (2015). (2014).
[17] V. Sokolov, et al., Method for Increasing the Various [33] Node.js. URL: https://www.jetbrains.com/help/
Sources Data Consistency for IoT Sensors, in: IEEE 9th webstorm/developing-node-js-applications.html
International Conference on Problems of [34] Mongo. URL: https://docs.nestjs.com/techniques/
Infocommunications, Science and Technology mongodb
(PICST) (2023) 522–526. doi: 10.1109/PICST57299. [35] A. Vickler, Javascript: Javascript Back End
2022.10238518. Programming, Independently Published (2021).
[18] N. Dovzhenko, et al., Method of Sensor Network [36] PLC+WiFiRedefining All-in-One Smart Home
Functioning under the Redistribution Condition of Connectivity. URL: https://www.hisilicon.com/en/
Requests between Nodes, in: Cybersecurity Providing techtalk/all-in-one-smart-home
in Information and Telecommunication Systems vol. [37] I. Liminovych, et al., Protection System for Analysis of
3421 (2023) 278–283. External Link Placing, Cybersecurity Providing in
[19] DevDocs – JavaScript Documentation, DevDocs API Information and Telecommunication Systems, vol.
Documentation. URL: https://devdocs.io/javascript/ 3654 (2024) 179–188.
[20] A. Vickler, Javascript: Javascript Back End [38] V. Poltorak, et al., Remote Object Confidential Control
Programming, Independently Published (2021). Technology based on Elliptic Cryptography,
[21] Wikipedia, MongoDB. URL: https://en.wikipedia.org/ Cybersecurity Providing in Information and
wiki/MongoDB Telecommunication Systems II, vol. 3550 (2023) 121–
[22] K. Tkachenko, et al., Ontological Approach in Modern 130.
Educational Processes, in: Workshop on [39] N. Jacob, Pseudodifferential Operators and Markov
Cybersecurity Providing in Information and processes, Volume 3 Markov Processes and
Telecommunication Systems, CPITS, vol. 3654 (2024) Applications (2005). doi: 10.1142/p395.
88–97. [40] F. Nicola, L. Rodino, Global Pseudodifferential
[23] Vue.js. Vue.js – The Progressive JavaScript Calculus on Euclidean Spaces, Basel: Birkhäuser
Framework|Vue.js. URL: https://vuejs.org/guide/ (2010).
introduction.html [41] V. A. Mikhailets, A. A. Murach, Hormander Spaces,
[24] B. Zhurakovskyi, I. Averichev, I. Shakhmatov, Using Interpolation, and Elliptic Problems, Berlin, Boston:
the Latest Methods of Cluster Analysis to Identify De Gruyter (2014).
Similar Profiles in Leading Social Networks, in: [42] V. A. Mikhailets, A. A. Murach, Interpolation Hilbert
Information Technology and Implementation, vol. Spaces Between Sobolev Spaces, Results Math. 67(1)
3646 (2023) 116–126. (2015) 135–152.
[25] B. Zhurakovskyi, et al., Secured Remote Update [43] C. Foiaş, J.-L. Lions, Sur certains théorèmes
Protocol in IoT Data Exchange System, Cybersecurity d'interpolation, Acta Sci. Math. (Szeged) 22(3–4)
(1961) 269–282.
275