scholarly journals DIGITALIZATION TRENDS OF AGRICULTURAL ENTERPRISES IN UKRAINE

2021 ◽  
Vol 139 (5) ◽  
pp. 72-85
Author(s):  
TARASYUK Anton ◽  
GAMALIY Volodymyr
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Information And Communication Technologies ◽  
Intelligent Systems ◽  
Information Technologies ◽  
System Analysis ◽  
Agricultural Sector ◽  
Agricultural Enterprises ◽  
Current State ◽  
And Control

Background. Agriculture is a leader in the export of our country,butthere is no comprehensive systemic approach in Ukraine and in the world to the development of enterprises in this industry based on the use of information technology in terms of the concept of the Fourth Industrial Revolution. An analysis of recent research and publicationshas shown that there are some scientific achievements, but an important scientific and practical problem of a comprehensive strategy for digitalization of agricultural enterprises remains unresolved. The aim of the article is to study the current state of implementation of information technologies in Ukrainian agricultural enterprises, to identify unresolved problems of agricultural enterprises digitalization. Materials and methods. Methods of system analysis and synthesis, marketing researches, statistical and comparison were used in the paper. Results. Scientific hypotheses have been put forward regarding the need to create and implement a comprehensive concept of digitalization of agriculture – "Smart agricultural", which is a set of software and hardware that provides automated collection and transmission for processing all necessary data for management decisions in the agricultural sector. Based on the results of this study, the theoretical foundations for the development and application of intelligent systems in the agricultural sector and the use of automated workplaces in control systems have been developed. The main groups of hardware and software used for industry automation are considered. At detailed consideration of application of the specified technological directions, there are non-systematic application, absence of software for systematic fixing and control of parameters for the further analysis. Conclusion. The results of the development ways analysis of the "Agriculture 4.0" ("SmartFarm") concept for its application at the Ukrainian agricultural enterprises allowed to allocate four technological directions: aerospace technologies; Internet of Things (IoT); information and communication technologies; Big Data and Machine Learning. The main achievements in each technological direction, available developments and ways of their application are considered. We found out that technological and technical means are used to ensure the quality development of the agricultural sector, but most technologies are used for operational processes and control of the enterprise current state. The study demonstrates that the big data technology and machine learning, which are the most important for the creation of automated jobs are not developed completely. Keywords: management system, intelligent systems, machine learning, digitalization of agricultural sector.

scholarly journals Mechanisms for introducing innovative technologies into the production of the agricultural sector

2020 ◽  
pp. 158-164
Author(s):  
Mykola Kravchenko
Keyword(s):  
Agricultural Sector ◽  
Animal Husbandry ◽  
Industrial Complex ◽  
Investment Environment ◽  
Innovative Technologies ◽  
Agricultural Enterprises ◽  
Current State ◽  
General Scientific ◽  
State And Local ◽  
Methodological Principles

Purpose. The aim of the article is substantiation of theoretical and methodological principles and development of practical recommendations for the formation and implementation of innovative technologies in the production of agricultural enterprises. Methodology of research. General-scientific and special research methods are used in the process of research, in particular methods: dialectics and scientific abstraction – in determining the essence of the innovative model of development of the agricultural sector of the economy; economic and statistical – when analysing the current state of implementation of innovative technologies in the agricultural sector of Ukraine; monographic – used in presenting the results of the study. Findings. Theoretical bases of formation of innovative mechanisms and their introduction in agrarian sector are covered. Theoretical and methodological approaches to the management of innovative technologies in the production of agricultural enterprises have been formed. Organizational and economic measures for the introduction of innovative technologies in the production of agricultural enterprises are substantiated. Originality. The mechanism of introduction of innovative technologies in agricultural production in the conditions of unfavourable investment environment in Ukraine is improved, which in contrast to the existing mechanisms provides integration of state instruments of support and regulation of the industry and implementation of state and regional programs at the expense of state and local budgets. In the paper it is offered to allocate production-technological, organizational-administrative, selection genetic, economic and social-ecological mechanisms of integration of innovation in various subsystems of agricultural sector. The production and technological mechanism is a priority in providing state support for the development of animal husbandry and processing of agricultural products. Practical value. Scientific developments will allow to form in Ukraine an effectively functioning agro-industrial complex with optimal financing based on the introduction of innovative technologies in the production of agricultural enterprises. Key words: innovation, methodical approaches, agricultural sector, advantages, technologies, production.

Deep Learning Applications in Agriculture

2021 ◽  
pp. 325-345
Author(s):  
Hari Kishan Kondaveeti ◽  
Gonugunta Priyatham Brahma ◽  
Dandhibhotla Vijaya Sahithi
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Rapid Growth ◽  
Agricultural Sector ◽  
The Other ◽  
Learning Methods ◽  
Agriculture Sector ◽  
Learning Techniques ◽  
Recent Trends

Deep learning (DL), a part of machine learning (ML), comprises a contemporary technique for processing the images and analyzing the big data with promising outcomes. Deep learning methods are successfully being used in various sectors to gain better results. Agriculture sector is one of the sectors that could be benefitted from the deep learning techniques since the current agriculture techniques cannot keep up with the rapid growth in population. In this chapter, the recent trends in the applications of deep learning techniques in the agricultural sector and the survey of the research efforts that employ deep learning techniques are going to be discussed. Also, the models that are implemented are going to be analyzed and compared with the other existing models.

Four Questions on the Digital Economy

2019 ◽  
Vol 8 (1) ◽  
pp. 5-14
Author(s):  
Василий Свистунов ◽  
Vasiliy Svistunov ◽  
Виталий Лобачев ◽  
Vitaliy Lobachyev
Keyword(s):  
Information And Communication Technologies ◽  
Information Technologies ◽  
Practical Importance ◽  
Communication Technologies ◽  
Digital Economy ◽  
Current State ◽  
Information And Communication ◽  
State Of Affairs ◽  
National Economies ◽  
The Impact

The article is devoted to the analysis of the main modern trends of digitalization of the economies of the leading world powers. Particular attention is paid to the state of Affairs with the practice of information and communication technologies in the Russian Federation. The analysis of trends in the participation of the digital economy in the formation of GDP of a number of countries, including Russia. The impact of digitalization processes on the current state and further development of various spheres of management is assessed. The practice of development of strategic programs for the development of national economies, which determine the targets for the development and implementation of modern information technologies in various industries and activities to improve the efficiency of national socio-economic systems. The author’s position in determining the main features of the current state of the digital economy of Russia is based on the generalization of the results of studies conducted by a number of international companies, and is of practical importance in the study of the problem of the ongoing transformation of social and labor relations in the context of digitalization.

The systems of corporate management in the fi eld of Agro-Industrial complex with the use of information technologies and their modernization

2020 ◽  
pp. 51-63
Author(s):  
I. Kovalyev ◽  
A. Takun ◽  
S. Takun ◽  
M. Kostomakhin
Keyword(s):  
Information And Communication Technologies ◽  
High Speed ◽  
Information Technologies ◽  
Agricultural Sector ◽  
Management Systems ◽  
Gradual Transition ◽  
Corporate Management ◽  
Industrial Complex ◽  
The Republic ◽  
Legal Forms

In the creation of new eff ective systems of corporate management in the agricultural sector of the Republic of Belarus at the moment, the latest advanced information and communication technologies come to the fore. Through the active use of these technologies today it is already becoming possible to perceive and subsequently process huge amounts of information, clear structuring and formalization of the received data, providing switching, routing and high-speed exchange of information and data between the control units (objects) and the control system itself. In connection with the global trend of deep digital transformation of all spheres of the economy and clearly defi ned directions of today’s state economic policy and prospects for the gradual transition to full-format digitalization of domestic agriculture, there is a real need to create modern «digital» management systems in agriculture, both at the enterprise level, and at the district (regional) and Republican levels in order to improve the eff ectiveness of enterprises in the industry. To date, one of the promising tasks in the further improvement of the functioning of agricultural organizations is the development of domestic methods for choosing the best options for digitalization of management of agricultural production and processing enterprises of diff erent organizational and legal forms. The general principles of informatization, digitalization of management processes in agricultural enterprises and the principles of building diff erent basic models of information and analytical systems in order to modernize existing management systems in the fi eld of agriculture have been discussed in the article.

Digital Epidemiology of Innovation (Preprint)

2021 ◽  
Author(s):  
Ivan Triana ◽  
LUIS PINO ◽  
Dennise Rubio
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Management ◽  
Medical Practice ◽  
Data Science ◽  
Medical Literature ◽  
The Future ◽  
Digital Epidemiology ◽  
Definition Of ◽  
And Control

UNSTRUCTURED Bio and infotech revolution including data management are global tendencies that have a relevant impact on healthcare. Concepts such as Big Data, Data Science and Machine Learning are now topics of interest within medical literature. All of them are encompassed in what recently is named as digital epidemiology. The purpose of this article is to propose our definition of digital epidemiology with the inclusion of a further aspect: Innovation. It means Digital Epidemiology of Innovation (DEI) and show the importance of this new branch of epidemiology for the management and control of diseases. In this sense, we will describe all characteristics concerning to the topic, current uses within medical practice, application for the future and applicability of DEI as conclusion.

scholarly journals Grappling with the Future Use of Big Data for Translational Medicine and Clinical Care

2017 ◽  
Vol 26 (01) ◽  
pp. 96-102 ◽  
Author(s):  
S. Murphy ◽  
V. Castro ◽  
K. Mandl
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Clinical Care ◽  
Healthcare Providers ◽  
Machine Learning Algorithms ◽  
Fully Integrated ◽  
Innovative Methods ◽  
Current State ◽  
Electronic Medical Record Systems ◽  
Genomic Variant

Summary Objectives: Although patients may have a wealth of imaging, genomic, monitoring, and personal device data, it has yet to be fully integrated into clinical care. Methods: We identify three reasons for the lack of integration. The first is that “Big Data” is poorly managed by most Electronic Medical Record Systems (EMRS). The data is mostly available on “cloud-native” platforms that are outside the scope of most EMRs, and even checking if such data is available on a patient often must be done outside the EMRS. The second reason is that extracting features from the Big Data that are relevant to healthcare often requires complex machine learning algorithms, such as determining if a genomic variant is protein-altering. The third reason is that applications that present Big Data need to be modified constantly to reflect the current state of knowledge, such as instructing when to order a new set of genomic tests. In some cases, applications need to be updated nightly. Results: A new architecture for EMRS is evolving which could unite Big Data, machine learning, and clinical care through a microservice-based architecture which can host applications focused on quite specific aspects of clinical care, such as managing cancer immunotherapy. Conclusion: Informatics innovation, medical research, and clinical care go hand in hand as we look to infuse science-based practice into healthcare. Innovative methods will lead to a new ecosystem of applications (Apps) interacting with healthcare providers to fulfill a promise that is still to be determined.

scholarly journals Surveillance Farm: Towards a Research Agenda on Big Data Agriculture

2018 ◽  
Vol 16 (3) ◽  
pp. 370-378 ◽  
Author(s):  
Francisco Klauser
Keyword(s):  
Big Data ◽  
Urban Space ◽  
Research Agenda ◽  
Agricultural Sector ◽  
Smart Cities ◽  
Data Gathering ◽  
Power And Control ◽  
Smart Farming ◽  
Smart Technologies ◽  
And Control

Farming today relies on ever-increasing forms of data gathering, transfer, and analysis. Think of autonomous tractors and weeding robots, chip-implanted animals and underground infrastructures with inbuilt sensors, and drones or satellites offering image analysis from the air. Despite this evolution, however, the social sciences have almost completely overlooked the resulting problematics of power and control. This piece offers an initial review of the main surveillance issues surrounding the problematic of smart farming, with a view to outlining a broader research agenda into the making, functioning, and acting of Big Data in the agricultural sector. For surveillance studies, the objective is also to move beyond the predominant focus on urban space that characterises critical contemporary engagements with Big Data. Smart technologies shape the rural just as much as the urban, and “smart farms” are just as fashionable as “smart cities.”

scholarly journals FINANCIAL SUPPORT OF AGRICULTURE SECTOR ENTERPRISES

2021 ◽  
Keyword(s):  
Financial Support ◽  
Agricultural Sector ◽  
Animal Husbandry ◽  
Industrial Complex ◽  
Agricultural Producers ◽  
Agricultural Enterprises ◽  
Agriculture Sector ◽  
Industrial Enterprises ◽  
Current State ◽  
Holding Back

The development of the agricultural sector, like any other sector of the economy, depends significantly on funding. Insufficient funding is holding back a significant increase in the efficiency of agro-industrial enterprises. Information on the importance of agricultural enterprises in the development of Ukraine's economy is given. The article analyzes the results of the budget program PCECC 1201150 "Financial support of agricultural producers" in 2020, the current state and trends in lending to agricultural enterprises. Analyzing the attraction of soft loans by businesses in the agro-industrial complex in 2020, it was found that most agricultural enterprises need funds to replenish working capital, and long-term loans are in demand due to the need to upgrade fixed assets and construction and reconstruction of production facilities. Some programs confirm that the priority areas of state support for agricultural producers in 2021, as in previous years, are the development of animal husbandry, horticulture, farming, as well as cheaper loans and purchase of agricultural machinery. The results of the state program "Affordable loans 5-7-9%" are presented, according to which agricultural enterprises received almost half of the total amount of loans issued. The specific features of the agro-industrial complex presented in the article are the factors holding back their lending, so we believe that it would be appropriate to consider the creation of a specialized agricultural bank that would lend to small and medium-sized agricultural enterprises. The services of commercial banks that they offer for enterprises in the agricultural sector are analyzed. The variety of services and programs is due to the desire of banks to attract as large a range of enterprises in the agricultural sector to provide them with funds for further development.

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