=Paper=
{{Paper
|id=Vol-3040/paper18
|storemode=property
|title=Promising Intelligent Technologies for Agricultural Development
|pdfUrl=https://ceur-ws.org/Vol-3040/paper18.pdf
|volume=Vol-3040
|authors=Aleksandr B. Orishev,Azer A. Mamedov,Irina N. Sycheva,Maksim V. Sherstyuk
}}
==Promising Intelligent Technologies for Agricultural Development==
https://ceur-ws.org/Vol-3040/paper18.pdf
Promising Intelligent Technologies for Agricultural
Development*
Aleksandr B. Orishev[0000-0003-1953-9543]1(*), Azer A. Mamedov[0000-0003-3194-1930]1, Irina N.
Sycheva[0000-0003-3784-0508]1, and Maksim V. Sherstyuk[0000-0001-9630-871X]1
1 Russian State Agrarian University – Moscow Timiryazev Agricultural Academy, Moscow,
Russia
Orishev71@mail.ru, azermamedov@mail.ru, in_sychewa@mail.ru,
maxim99@mail.ru
Abstract. The paper discusses the increasing application of intelligent
technologies in agriculture. The authors analyze potential benefits, and outlines
further development prospects of intelligent technology in Russian agriculture and
beyond. The digitalization of agriculture is based on two main conditions: a)
intelligent mechanisms: receiving, sending, producing, processing data, and b)
connected equipment: communication and interface ensuring the permanent
exchange of information between digital technology and portals. A review of the
up-to-date publications on the development of intelligent technology in
agriculture clearly shows that intelligent technologies are widespread in the
agricultural sector. They are used in such areas as phytopathology, fruit growing,
water resources management, use of soil resources, climate forecasting, and
ethology. The paper provides numerous examples and discusses future prospects.
Keywords: Intelligent technologies, Artificial intelligence, Agriculture,
Agro-industrial complex
1 Introduction
Intelligent technologies are widely used in agriculture: they automate production
processes and modernize the production process and work that is associated with
significant risk to the workers’ health and well-being. For agriculture, the use of such
technologies is of particular importance, since the nature of production labor in this
sector of the economy is associated with numerous complexities, and is subject to the
action of life-threatening factors. New technologies and specialized monitoring are used
to effectively and efficiently manage the cultivation of agricultural plants. Intelligent
technology provides the possibility of introducing specialized sensors in the
*
Copyright © 2021 for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0).
intensification of greenhouses, increasing the efficiency of livestock farms, conducting
environmental research, and addressing problems of nature protection.
There have been major changes in the development dynamics of the agricultural
sector in Russia and beyond [6; 13; 28]. The information revolution leads to the
increasing dependency on information technology. The increasing complexity of the
economic sphere of activity has led to the development of new information technologies.
The information revolution has caused the formation of the information field of
information and telecommunication systems. Information plays a major role in the agro-
industrial complex [10; 26]. The agricultural sector is facing increased demand for
quality products, increased yields and increased productivity. New technologies are
used that reveal the reserves of agro-industrial production and attract investments [15].
Scientific and technical developments facilitate the control and administration of the
enterprise, which increases the efficiency of animal husbandry. Achievements of
organizations and industries are impossible without latest data on market conditions, as
well as advances in production and distribution technologies. The use of digital
technologies increases the profitability of agricultural production in the agro-industrial
complex, as numerous independent sources and calculations suggest [19; 27; 31].
This short paper provides a review of the latest advances in the use of intelligent
technology in agriculture in Russia and beyond, discusses potential benefits, and
outlines further development prospects. The relevance of the study is due to
digitalization processes, which are the key determinant that ensures economic growth
and development. The success of digital transformation implementation determines the
competitiveness of the domestic agro-industrial sector. The agro-industrial complex has
historically been an area less prone to innovation than other sectors of the Russian
economy. Therefore, it is of the highest importance to analyze the current state and
potential benefits of the application of intelligent technologies in agriculture.
2 Application Of Intelligent Technologies In Agriculture: Latest
Advances, Potential Benefits, And Further Prospects
The agro-industrial complex faces the global task of increasing food production due to
population growth. In 2025, it will be necessary to produce 70% more in basic food
products, such as bread, meat, milk, etc. Forecasts are focused on the growth of demand
for food, the emergence of global problems and requirements for increasing production
efficiency in agriculture. The territory of Russia, despite being a fairly large area, is an
agricultural zone of risky agriculture, subject to climatic, soil, biological, and
geographical factors, leading to high production and administrative costs. Consequently,
there is an objective need to increase and maintain competitiveness of Russian
agriculture with all possible means, and the use of information technology in agriculture
promises high benefits. The use of digital methods and models in agriculture ensures
the efficient functioning of the agricultural sector of the economy. The predominance
of inertial methods of management and old technologies in the dynamic economic
conditions of agrarian reforms can lead to the destabilization of functions in agro-
industrial production, imbalances in the dynamics of the agrarian sector, and the
destruction of economic and cooperative ties in agriculture.
The digitalization of agriculture is based on two main conditions: a) intelligent
mechanisms: receiving, sending, producing, processing data, and b) connected
equipment: communication and interface ensuring the permanent exchange of
information between digital technology and portals. The digital agricultural sector has
become a reality in such area as GPS navigation for controlled crop production [14; 18],
specific objects that ensure plant protection throughout the entire production period
through repeated communication [11; 22]. A real future for agricultural production is
the automatic processing of information and the integration of coordinated networks.
However, the implementation of future plans requires targeted efforts of stakeholders.
Our review of the up-to-date publications on the development of information
technology in agriculture clearly shows that intelligent technologies are widespread in
the agricultural sector. They are used in such areas as phytopathology, fruit growing,
water resources management, use of soil resources, climate forecasting, and ethology
[7; 9; 12; 22; 24; 25; 33]. Artificial intelligence technologies carry out the execution of
tasks during work requiring abstract conclusions, identification of samples, actions with
incomplete information. Artificial intelligence technologies in agriculture have common
features and characteristics: technical solutions, software and hardware for forecasting
development depending on climate, soil conditions, precipitation, market prices.
Intelligent technologies are frequently used with robotics. A robot manipulates objects
and tools, and artificial intelligence technologies provide spatial direction, choose tools
for the robot when performing tasks, identify obstacles.
There is an objective need to constantly receive new information anywhere at a
convenient time for farmers to make optimal decisions. It is well-known that irrigation
management works play an important role in the agricultural economy and require high-
tech efforts. Precision farming has gained the possibility of practical application through
the use of computer software, the creation of remote sensors and the automation of
agricultural work. The use of synoptic forecasts makes it possible to effectively use
chemical means of protection in crop production, which reduces the level of
environmental pollution. Evaluation of evapotranspiration is a process of particular
complexity and plays an important role in the administration of crop production, as well
as for modeling irrigation processes. A method for calculating the average
evapotranspiration rate for desert regions using digital technologies has been developed
by a group of authors [4].
For the rational and efficient administration of the cultivation of plant varieties, the
latest technologies and monitoring are widely used. The possibility of using sensors to
improve the efficiency of greenhouses is being studied by a number of researchers.
Climate control systems provide access to Internet resources. It becomes to use sensors
and photo and video equipment controlled remotely using information technology as a
local network. As a result, remote visual control and assessment of plant health are
performed. Another critical problem in agriculture is identifying and killing weeds.
Artificial intelligence technologies detect unwanted plant organisms in crops with high
precision. For instance, a method of hypoelectric visualization has been developed to
identify weed species [30]. This method identifies weeds and allows one to obtain
economic benefits while reducing herbicide sowing treatment. Intelligent systems
contribute to the conservation of livestock, increased productivity, and the use of
progressive methods of keeping livestock and providing them with feed.
Intelligent processing of Earth remote sensing data allows solving many problems
in the field of agriculture, as numerous sources in the literature argue [8; 29; 32]. For
example, various climatic and economic factors in agriculture lead to changes in the
area and structure of arable land used in different regions of the world and our country.
Accordingly, the task of recording, identifying, and evaluating arable land used in
agriculture is extremely relevant for farmers around the world. Among the main tasks
for the study of land use, one can include the determination of the composition of the
soil, the determination of moisture, temperature, soil salinity, as well as the assessment
of the degree of soil degradation and desertification. The use of remote sensing data,
coupled with intelligent technologies, makes it possible to assess the agro-climatic
conditions for the cultivation of agricultural crops and the intensity of the use of soil
resources. Analysis of multi-temporal data allows one to delve deeper into the dynamics
of changes in vegetation cover, land resources, identify problem areas and make long-
term forecasts.
Also, one can increase the effect from intelligent processing of Earth remote sensing
data by applying sensors monitoring the presence of disease or weeds to determine the
health of plants and animals. The sensors use laser radar technology, ultrasonic, and
electromagnetic devices. Remote sensors use infrared wave technology,
spectrophotometers, atomic resonators. On-board performance sensors determine
application rates for fertilizer, water and pesticides. They define the technical
parameters of the technique.
The use of automated systems based on intelligent technology promises high
benefits in labor productivity. Automation is the direction of scientific and technological
progress in the agricultural sector. Automation includes herd management, waste
disposal, egg collection, microclimate maintaining, as well as automation of equipment,
fertilization, milking, feeding [2; 20]. Based on intelligent systems, automation
stabilizes the parameters of the subjects of production, optimizes work processes,
controls production, reduces the amount of manual labor, as well as contributes to the
protection of people, biomaterials, animals from hazardous and emergency modes of
operation [1].
It is possible to reduce the cost of equipment production, maintenance, and control
if one has full knowledge of the equipment and the capabilities of information
technology. The construction of a software and hardware complex guarantees the effect
of the operation of agricultural machinery. There are numerous programs developed for
optimizing animal nutrition, planning the distribution of feed [28–30]. With their help,
groups of agricultural animals are controlled: pigs, poultry, dairy cattle. The purpose of
such programs is to plan the feeding process according to recipes and nutritional
mixtures. There are programs for automation, control, and analytics in animal
husbandry. Such programs monitor the physiological state of the herd, diagnoses
diseases. The introduction of information technologies into agro-industrial complexes
will help increase profitability in a short period of time, depending on the initial
investment and the effectiveness of implementation.
In our opinion, the agrarian sector needs intelligent information systems that
simulate agricultural management and provide an opportunity to receive comments on
activities and consultations from specialists. Increasing the productivity of this system
requires the introduction of regional divisions, and such steps would allow for the full-
scale application of Big Data and Artificial Intelligence in agriculture [17; 21]. The input
data must be provided by the farmers themselves, and they must contain reference data
on the main economic indicators, as well as a description of the technical means and
innovations used by them. Input data should be passed to the analytics department to
validate the submitted data for the relevance and feasibility of development and
application in the relevant region. In the analytical department, a commission with
experts in different areas should be created. After appropriate verification procedures,
the data is transmitted to the database of the information system for open access and
general use. Open information resources have proven to be an important factor in
production. The agro-industrial complex is currently experiencing difficulties in the
distribution of information resources, which affects its evolutionary development. The
use of information systems allows one to create a scientific knowledge base for agro-
industrial complex organizations. Intelligent information systems also provide a
constant access to reliable information resources and allow for cross-scale and cross-
territorial intelligent analysis.
Modern technologies and scientific and technological progress in market conditions
are developing very quickly, outstripping agricultural production relations by several
years. External relations of agricultural enterprises are subject to the greatest dynamism,
which leads to an increase in the volume and speed of information transfer. For effective
management in agriculture, it is highly necessary to reduce the time before making
decisions. Consequently, one should focus on an increase in the speed of information
delivery. At present, information has begun to influence the process of exchange,
consumption, and distribution of created services and goods in agriculture, as well as
the development of the market and economic mechanisms with the establishment of
fairly constant ties between business entities and the entire spectrum of the economic
sector. This allows information to be categorized as a factor of production, and
intelligent systems and tools become of the highest importance in agricultural
production and management.
The introduction of digital systems with the application of intelligent technology in
large industries is profitable. The experience of successful agricultural producers shows
that digital technologies can create conditions for increasing yields and productivity
during the production cycle. Practice shows that the introduction of digital technologies
into the development of agricultural organizations pays off in 2-3 years [23]. The use of
new developments of software systems and information technologies in the agro-
industrial complex intensifies the work of agricultural complexes, automates production
activities and increases productivity.
3 Conclusion
Intelligent technologies are used in agriculture to identify plant diseases, classify plants,
identify weeds, in fruit growing, cadastral registration, and weather forecasts. Further
development and use of such technologies are associated with technical progress and
abilities of agricultural organization to introduce them. Manufacturing, service, and
transactional structures with software are global in nature. The innovative development
of agriculture and the technical modernization of the industry depend on artificial
intelligence technologies and their widespread use.
References
1. Annosi MC, Alberto FB, Nati MF (2019) Is the trend your friend? An analysis of technology
4.0 investment decisions in agricultural SMEs. Computers in Industry 109:59–71
2. Bai T, Chen W-H, Liu Zh et al (2017) Software hazard analysis for nuclear digital protection
system by Colored Petri Net. Annals of Nuclear Energy 110:486–491
3. Bazzi CL (2019) AgDataBox API – Integration of data and software in precision agriculture.
SoftwareX 10:100327
4. Benyam A, Soma T, Fraser E (2021) Digital agricultural technologies for food loss and
waste prevention and reduction: Global trends, adoption opportunities and barriers. Journal
of Cleaner Production 323:129099
5. Bonadiesa S, Gadsden SA (2019) An overview of autonomous crop row navigation
strategies for unmanned ground vehicles. Engineering in Agriculture, Environment and
Food 12(1):24–31
6. Cepeda P, Ponce P, Molina A et al (2013) Towards sustainability of protected agriculture:
automatic control and structural technologies integration of an intelligent greenhouse. IFAC
Proceedings Volumes 46(7):366–371
7. Chen Y, Liu B, Qi K et al (2012) Computer supported control engineering education reform
in agriculture engineering field based on intelligent agriculture (IA) new concept. IERI
Procedia 2:603–608.
8. da Silveira F, Lermen FH, Amaral FG (2021) An overview of agriculture 4.0 development:
Systematic review of descriptions, technologies, barriers, advantages, and disadvantages.
Computers and Electronics in Agriculture 189:106405
9. Gil S (2019) Transformative effects of IoT, Blockchain and Artificial Intelligence on cloud
computing: Evolution, vision, trends and open challenges. Internet of Things 8:100118
10. González-Brionesa A, Mezquita Y, Castellanos-Garzón JA et al (2019) Intelligent multi-
agent system for water reduction in automotive irrigation processes. Procedia Computer
Science 151:971–976
11. Gyung B, Hyun K, Hee GK et al (2013) Reliability modeling of digital component in plant
protection system with various fault-tolerant techniques. Nuclear Engineering and Design
265:1005–1015
12. He X, Zhang D, Yang L et al (2021) Design and experiment of a GPS-based turn
compensation system for improving the seeding uniformity of maize planter. Computers
and Electronics in Agriculture 187:106250
13. Hua W-P, Lina C-B, Yang C-Y et al (2018) A framework of the intelligent plant factory
system. Procedia Computer Science 131:579–584
14. Huanga Y, Chen Zh-X, Yu T et al (2018) Agricultural remote sensing big data: Management
and applications. Journal of Integrative Agriculture 17(9):1915–1931
15. Jha K, Doshi A, Patel P et al (2019) A comprehensive review on automation in agriculture
using artificial intelligence. Artificial Intelligence in Agriculture 2:1–12
16. Kamilaris A, Fonts A, Prenafeta-Boldύ FX (2019) The rise of blockchain technology in
agriculture and food supply chains. Trends in Food Science & Technology 91:640–652
17. Kim BG, Kang HG, Kim HE et al (2013) Reliability modeling of digital component in plant
protection system with various fault-tolerant techniques. Nuclear Engineering and Design
265:1005–1015
18. Kos D, Kloppenburg S (2019) Digital technologies, hyper-transparency and smallholder
farmer inclusion in global value chains. Current Opinion in Environmental Sustainability
41:56–63
19. Mayorova MA, Markin MI (2019) Digital farming in the production and economic activities
of agricultural enterprises. Theoretical Economics 2:67–71
20. McGowan D, Vasilakis C (2019) Reap what you sow: Agricultural technology, urbanization
and structural change. Research Policy 48(9):103794
21. Multsch S (2017) A practical planning software program for desalination in agriculture -
SPARE:WATERopt. Desalination 404:121–131
22. Pan Y (2016) Heading toward Artificial Intelligence 2.0. Engineering 2(4):409–413
23. Phillips PWB, Relf-Eckstein J-A, Wixted GJB (2019) Configuring the new digital
landscape in western Canadian agriculture. NJAS - Wageningen Journal of Life Sciences
90–91:100295
24. Rossetto R, de Filippis G, Triana F et al (2019) Software tools for management of
conjunctive use of surface- and ground-water in the rural environment: Integration of the
Farm Process and the Crop Growth Module in the FREEWAT platform. Agricultural Water
Management 223:105717
25. Sinha BB, Dhanalakshmib R (2020) Recent advancements and challenges of Internet of
Things in smart agriculture: A survey. Future Generation Computer Systems 126:169–184
26. Skvortsov EA, Nabokov VI, Nekrasov KV et al (2019) Application of artificial intelligence
technologies in agriculture. Agrarian Bulletin of the Urals 8(187):91–98
27. Uzun V, Shagaida N, Lerman Z (2019) Russian agriculture: Growth and institutional
challenges. Land Use Policy 83:475–487
28. Veeck G, Veeck A, Yu H (2020) Challenges of agriculture and food systems issues in China
and the United States. Geography and Sustainability 1(2):109–117
29. Wu L (2012) An empirical research on poor rural agricultural information technology
services to adopt. Procedia Engineering 29:1578–1583
30. Xin J, Kaikang C, Jiangtao J et al (2019) Intelligent vibration detection and control system
of agricultural machinery engine. Measurement 145:503–510
31. Yurkova ON, Sleptsova MA (2018) Application of Information Technologies in
Agriculture. Bulletin of Modern Research 12.5(27):299–301
32. Zou S, Yang F, Tang Y et al (2016) Optimized algorithm of sensor node deployment for
intelligent agricultural monitoring. Computers and Electronics in Agriculture 127:76–86
33. Zuo D (2019) Assessment of meteorological and agricultural droughts using in-situ
observations and remote sensing data. Agricultural Water Management 222:125–138