=Paper=
{{Paper
|id=Vol-3006/01_short_paper
|storemode=property
|title=Problems and tasks of using digital technologies in agriculture
|pdfUrl=https://ceur-ws.org/Vol-3006/01_short_paper.pdf
|volume=Vol-3006
|authors=Viktor V. Alt,Maxim S. Chekusov,Elena A. Balushkina,Svetlana P. Isakova
}}
==Problems and tasks of using digital technologies in agriculture==
https://ceur-ws.org/Vol-3006/01_short_paper.pdf
Problems and tasks of using digital technologies
in agriculture
Viktor V. Alt1,2 , Maxim S. Chekusov3 , Elena A. Balushkina1 and Svetlana P. Isakova1
1
Siberian Federal Scientific Centre of Agro-Biotechnologies of the Russian Academy of Sciences, Novosibirsk, Russia
2
Novosibirsk State Technical University, Novosibirsk, Russia
3
Omsk Agrarian Scientific Center, Omsk, Russia
Abstract
Digitalization of agricultural production is an existing reality. About 50% of farms to some extent use
elements of digitalization in the implementation of technological and managerial tasks. A system analysis
to the objects of agricultural production, as to the information environment when describing them in the
information space, is applied to the study. The main problems of digitalization of agriculture associated
with large amounts of data and the required accuracy of the resulting solutions are shown. The main
tasks in the use of digitalization by agricultural producers are identified and justified. The general
requirements for the volume of information for solving problems of agriculture, crop production and
means of processing this information are revealed. The amount of information when using digital
methods of managing technological processes in agriculture is determined from the necessary resolution
when managing technological processes and the volume of arable land in the subject of Russia. The
analysis of positive trends allowed us to formulate main messages in the application of digital technologies
in agricultural production at the present stage of its development.
Keywords
Agricultural factors, digital information, resolution, volume of information, labor productivity.
1. Introduction
“Strategy without tactics is the slowest path to victory. Tactics without strategy are just a fuss before defeat”.
Sun Tzu, Chinese strategist and thinker who lived in the VI century BC,
author of the famous treatise on military strategy.
Digital technologies in the management of agricultural production are a set of software
methods for processing natural production information using technical means and computers [1,
2]. There is no consensus on what should be considered a priority, and what can‘t be solved
taking into account the realities in specific conditions. It is also not decided what difficulties
await those who want to benefit from the use of digital technologies and what opportunities
open up when using these technologies. It offers databases and knowledge, platform solutions,
big data systems, artificial intelligence systems, etc. The use of digital technologies today
requires the formulation of approaches to their application. They should be based on the
SDM-2021: All-russian conference, August 24–27, 2021, Novosibirsk, Russia
" altviktor@ngs.ru (V. V. Alt); chekusov@anc55.ru (M. S. Chekusov)
© 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
http://ceur-ws.org
ISSN 1613-0073 CEUR Workshop Proceedings (CEUR-WS.org)
1
Viktor V. Alt et al. CEUR Workshop Proceedings 1–7
analysis of the types of information, its volumes and the change in the paradigm of information
support for agricultural production.
2. Discussion
To formulate the main messages in the application of digital technologies in agricultural produc-
tion, it is planned to solve the following tasks using a system analysis of hidden relationships
and implicit functional dependencies:
— determination of structural relations of agricultural production objects;
— estimation of the amount of information that characterizes the multidimensional space in
the aggregate of all objects as a whole and each separately;
— formation of the paradigm of information support and management of technological pro-
cesses of agricultural production using expert systems and artificial intelligence systems.
The analysis of trends in the digitalization of agricultural production showed an increase
in production efficiency while maintaining soil fertility, a decrease in social tension in rural
areas, and an increase in the influx of young people to rural areas [3, 4, 5]. In the course of
our research, we applied a systematic analysis of the commonality of relationships. These
connections make up agricultural production as a system consisting of independent subsystems.
They have stable connections in the higher-level system and have the property of changing
independently as subsystems, and changing as a whole within the system. The method of
limiting transition of states of subsystems is applied to establish mutual influence on the system
as a whole and its subsystems. This approach to the systematic representation of agricultural
(grain) production is legitimate. The proof of this was the creation of a number of information
models in our research team over the years using the above-mentioned concept (machine-
tractor unit, agricultural machine, transmission, engine, plant, phytophages, agrophytocenosis,
etc.). The use of information models provided the creation of more than 20 databases and
knowledge [3, 4, 5, 6, 7]. Subject-oriented search algorithms of a tree-like search system are
applied.
What is the reliable knowledge of the digitalization of agriculture? Agriculture is characterized
by features that dictate the use of modern capabilities for processing and analyzing digital
information:
— multi-dimensionality of factors that characterize production and technological processes;
— agroclimatic distribution of farms (more than 100 million hectares);
— diversity of societies;
— the multiplicity of species, breeds, varieties of animals and plants.
The inevitability of incomplete information when making management decisions exists in
agricultural production. This is due to the multifunctional nature of management facilities
in agriculture. These include: the environment, land, plants, animals, machines and society
(rural people with their environment: agro-climatic conditions, social conditions, financial and
monetary structure of income and expenses, transport infrastructure, production logistics, etc.)
2
Viktor V. Alt et al. CEUR Workshop Proceedings 1–7
These features determine usage need of innovative digital technologies: artificial intelligence,
robotics, Internet of things, unmanned aerial vehicles, as well as the introduction of a number
of new hardware, software, mobile applications, sensor technologies and big data processing
systems [8, 9, 10].
The areas and directions in agriculture affected by digitalization are presented below:
— detection of diseases, pests and weeds by processing images obtained by drones using
artificial intelligence;
— forecast weather conditions by receiving, processing and analyzing data from local
weather stations and statistical data for previous periods;
— precision farming using GPS navigation, drones, digital mapping and soil assessment;
— application of fertilizers and protective equipment using the analysis of data obtained by
aerial photography, crop monitoring and spot application of funds using GPS navigation;
— monitoring plant growth using drone flyovers, calculating vegetation indices, tracking
product quality and forecasting yields;
— control of the production process using information systems that provide tools for eco-
nomic and other calculations, field history and document management;
— monitoring of agricultural machinery using systems for tracking its movement, fuel
consumption and remote monitoring of the state of equipment;
— purchase and sale of equipment using Internet portals, mobile applications, etc.
Agricultural production is characterized by a combination of processes with a periodicity
of several microseconds (in the task of ensuring the operability of tractors, combines and
other equipment) to three years (in the task of updating the herd of animals). These examples
demonstrate the polar requirements for speed, volume of information, and size of information
messages. It is necessary to formulate approaches to the formation of tools and software at the
current stage of development of both agricultural production and digital technologies.
Each of the subjects of agricultural production management is associated with the multifunc-
tional nature of the interrelationships of management objects in the decision-making process.
We can systematize them in a generalized form as the environment, the earth, plants, animals,
machines, and society. Society is a rural person with his social and living conditions, financial
and monetary system of income and expenses, transport infrastructure, production logistics, etc.
All this can be represented as 8 and sometimes 12 types of resources. They interact in a common
set and are represented by an n-dimensional space, the size of which is determined by the
number of resources under consideration [11]. A person lives in a 4-dimensional space (width,
length, height, and time). When the dimensionality of the space increases (the first derivative
of any of the metrics of the four-dimensional space can be represented as the fifth coordinate),
one feels a certain discomfort. The analysis of the flow of processes in 8, 12-dimensional space
for him is a task of insurmountable complexity.
To solve this problem, a person assumes the immateriality or limitlessness of some resources
in order to reduce the dimensionality of space. Thus, he makes private decisions. As a result,
incomplete knowledge of the processes in agricultural production is formed [12].
As Friedrich von Hayek (an economist and philosopher of the twentieth century) argued, every
person has only a particle of knowledge, so in the process of managerial and entrepreneurial
3
Viktor V. Alt et al. CEUR Workshop Proceedings 1–7
Table 1
Grain production volumes in the Novosibirsk region
Indicator, average value 1988–1992 2016–2019 +Growth, %
Crop area, thousands of ha 1999 1492.9 −25.6
Gross collection, thousands of tons 2668 2522.7 −0.05
Yield, tons/ha 1.33 1.69 27
activity, mistakes of actions or decisions are objectively inevitable. This is the first and one of
the main messages of the use of digital technologies in agricultural production.
The desire to increase the productivity of fields and farms is always associated with the need
for a deeper knowledge and understanding of the processes that determine the functioning of all
components in agriculture and animal husbandry. Fields, varieties, breeds, conditions for their
growth and development, processing into products of human life support and the environment
of its existence and habitat are associated with a quantitative and qualitative description of the
entire diversity of the universe. The more deeply we know the world, the more parameters,
characteristics, and descriptions we use to create articles, books, databases, and knowledge [7].
The need to use large databases and their analysis for making management decisions dictates
the timeliness of changing the paradigm of information support for digitalized agricultural
production [12, 13]. We proposed a paradigm of information support for digitalized agricultural
production.
The efficiency of grain production is largely determined by the choice of varieties and tech-
nologies in all their diversity in accordance with the agro-climatic (soil diversity, temperature,
moisture) and agro-landscape features of the farm location. This information can only be
obtained by remote sensing of fields with a resolution of up to 0.01–0.003 m. Such accuracy
entails the need to process large amounts of data.
We consider the Novosibirsk region and its acreage — 1900 thousand hectares as an example
to determine the possible necessary amounts of information. This territory is characterized the
entire spectrum of types and varieties of agricultural crops, growing and harvesting technologies.
Solving the problems of digital management of them involves the use of a digital platform with
processed volumes of about 2 petaflops. Invariant solutions to this problem are possible only
on the basis of digital information technologies using big-date, digital platforms, databases,
knowledge bases, expert systems and artificial intelligence systems. This is next of the main
messages of the use of digital technologies in agro-industrial production.
The agro-climatic potential of Siberia is 4–6 times lower than the same indicator in Europe and
the United States. In Western Siberia it is 58–63% of the potential of Russia, and in Transbaikalia
is 38–43% (according to Academician of the Russian Academy of Sciences Goncharov P.L.).
In Siberia with the expansion of the use of high-intensity, low-energy and resource-saving
technologies the production of competitive products is ensured (Table 1) [4, 7].
Arable land in the Novosibirsk region has decreased by 25% in 30 years. At the same time the
gross grain harvest has decreased by 0.05%, and the yield has increased by 27%. The results of
the competitive test showed a yield of 4.5 to 7.6 tone on ha of zoned wheat varieties according
to Academician of the Russian Academy of Sciences A.N. Vlasenko, which indicates their
4
Viktor V. Alt et al. CEUR Workshop Proceedings 1–7
advantage over non-zoned varieties. Varieties of Siberian selection occupy 95–99% of the grain
wedge in the region.
The share of the rural population of Russia decreased by 2 times over the period 1959–2018,
according to Rosstat, and by 2019 reached 25.3% of the total population [13]. In 2001–2010 the
number of employees in agricultural organizations decreased by about 5 times [14]. The negative
trend in terms of providing the village with specialists continues. For the sake of objectivity, it
should be noted that large commodity producers and agricultural holdings don‘t experience an
acute shortage in personnel due to high productivity, intensification and remuneration. The
analysis of positive and negative trends in rural areas in the field of changes in the human
resource allows us to conclude that there is a chronic shortage of highly qualified specialists
among workers, people with higher and secondary specialized education who seek to use digital
technologies in production.
If we consider the six types of resources (“land”, “plants”, “environment”, “machines”, “animals”
and “society”) in unity and in the system of grain production, it becomes obvious that we in
Siberia objectively have the possibility of doubling grain production [15].
Most authors believe that the factors that ensure the growth of grain production are:
— varieties of intensive grain cereals;
— plant nutrition (its replenishment is provided by organic and mineral fertilizers);
— protection of plants from diseases, pests and weeds;
— increase the productivity of agricultural machines (by increasing their working width,
power, automation, etc.).
This is certainly true. However, the task of leveling the causes that hinder the growth of
grain production is to formalize the limiting factors and find ways to overcome them.
For example, take the Trans-Baikal Territory. There are years when there is no moisture in
the meter layer of the soil, and even poplars dry up. But in August, September, rains come,
leading to flooding. Can this moisture be used? Yes, you can, and this technological technique is
known. This is a deep loosening. It ensures the accumulation of moisture in the meter layer of
the soil and its use in the next growing season. The use of deep loosening allowed us to obtain
3.5 ton on ha of spring wheat Novosibirsk 29 in the SP Sterlikov V.V. Karymsky district of the
Trans-Baikal Territory.
The search for techniques and technologies that eliminate limiting factors is multidimensional.
Their composition and ways to overcome them can be determined only by analyzing digital
simulation models of agrobiocenosis for a specific territory. This is the next message in the
development and development of digital information technologies in agricultural production.
We have already discussed the unifying role of “society” for all five resources. But not
everything is so simple with a more detailed analysis of this role. It should be taken into account
that a person as a subject of business processes always tries to maintain their advantages over
other business participants, otherwise they will be like everyone else. In such a situation,
the principle works: “Do I need this?”. Overcoming this situation is possible through science,
education and management. Of course, this requires time and financial resources. Yes, it is
possible that not all fundamental research is involved in this process, but applied research is
definitely a priority. This is the use of the development of information and digital technologies
in a particular agricultural enterprise.
5
Viktor V. Alt et al. CEUR Workshop Proceedings 1–7
The number of combinations in solving the problems of organizing and implementing agri-
cultural production in a particular farm with 3 technologies of tillage (dump-classic, non-dump,
no-tile), 3 levels of intensification (extensive, normal, intensive) and 6 messages of information
digital technologies is equal to 247520 options.
3. Conclusion
The principles are based on the formulated messages of digital information technologies, which
allow them to be used in more than 247520 different combinations and to be applied to the
entire variety of farms, depending on their capabilities and desires. The solution to the problems
of the transition of agriculture to a highly productive type of production lies in the way of
step-by-step resolution of the choice of technologies and ranking this set by the importance for
each particular farm of a particular message to eliminate (reduce) the negative impact on the
life of the producer of agricultural products.
References
[1] Dos Santos U.J.L., Pessin G., da Costa C.A., da Rosa Righi R. AgriPrediction: A proactive
internet of things model to anticipate problems and improve production in agricultural
crops // Computers and Electronics in Agriculture. 2019. Vol. 161. P. 202–213.
[2] Jones J.W., Antle J.M., Basso B. et al. Toward a new generation of agricultural system data,
models, and knowledge products: State of agricultural systems science // Agricultural
Systems. 2017. Vol. 155. P. 269–288.
[3] Fedorenko V.F., Mishurov N.P., Buklagin D.S., Goltyapin V.Ya., Golubev I.G. Digital agricul-
ture: state and prospects of development: Scientific Ed. Moscow: FSBI “Rosinformagrotech”,
2019. 316 p.
[4] Fedorenko V.F., Chernoivanov V.I., Goltyapin V.Ya., Fedorenko I.V. World trends in the intel-
lectualization of agriculture: Nauch. analyte. review. Moscow: FSBI “Rosinformagrotech”,
2018. 232 p.
[5] Gostev A.V., Pykhtin A.I. The structure of the normative reference database of the support
system for agricultural producers on the rational choice of highly profitable adaptive
technologies for the cultivation of grain crops // Modern High-tech Technologies. 2018.
Vol. 2. P. 37–41.
[6] Tkachenko V.V. Methodology of multi-criteria complex assessment and selection of tech-
nology for cultivating agricultural crops // Scientific Journal of KubGAU. 2016. Vol. 123.
No. 09. P. 1–19.
[7] Results of the study of the intra-field heterogeneity of the soil cover: Report of scien-
tific research work No. 05-2017. Novokubansk branch of FGBNU “Rosinformagrotech”
(KubNIITiM), 2017. 116 p.
[8] Phillips P.W.B., Relf-Eckstein J.-A., Jobe G., Wixted B. Configuring the new digital landscape
in western Canadian agriculture // Wageningen Journal of Life Sciences. 2019. Vol. 90.
Art. no. 100295.
[9] Talaviya, Shah D., Patel N., Yagnik H., Shah M. Implementation of artificial intelligence in
6
Viktor V. Alt et al. CEUR Workshop Proceedings 1–7
agriculture for optimisation of irrigation and application of pesticides and herbicides //
Artificial Intelligence in Agriculture. 2020. Vol. 4. P. 58–73.
[10] Jha K., Doshi A., Patel P., Shah M. A comprehensive review on automation in agriculture
using artificial intelligence // Artificial Intelligence in Agriculture. 2019. Vol. 2. P. 1–12.
[11] Alt V., Isakova S., Balushkina E. Digitalization: Problems of its development in modern
agricultural production // 8th Innovative Technologies in Science and Education, ITSE 2020.
2020. Vol. 210. Art. no. 10001.
[12] Paptsov A.G., Altukhov A.I., Kashevarov N.I., Pershukevich P.M., Denisov A.S., Rudoy E.V.
et al. Forecast of scientific and technological development of the crop production industry,
including seed production and organic farming in Russia, in the period up to 2030 //
Novosibirsk State Agrarian University, Sib. Feder. Center for Agrobiotechnologies of the
RAS, Institute of Cytology and Genetics SB RAS, VNC VNIIEKH. Novosibirsk: Publ. House
of NGAU “Zolotoy kolos”, 2019. 100 p.
[13] Regions of Russia. Socio-economic indicators. 2020: Stat. Sat. / Rosstat. Moscow, 2020.
1242 p.
[14] Alt V.V., Bobrova T.N., Kolpakova L.A., et al. Methodological provisions on the information
support of machine agricultural technologies for the production of spring wheat grain at
the level of an agricultural enterprise. Novosibirsk, 2017. 56 p.
[15] Donchenko A.S., Kalichkin V.K., Mityakova R.P. et al. Interregional scheme of placement
and specialization of agricultural production in the subjects of the Russian Federation of
the Siberian Federal District: Recommendations. Novosibirsk: SB RAS, 2016. 255 p.
7