Agrarian
Bulletin
of the Urals

Russian Journal of Agricultural Research

The publication is registered by the Ministry of the Russian Federation
for Affairs of the Press, Television and Radio Broadcasting and Mass Communication Media.
Registration certificate: PI number 77-12831 on May 31, 2002
Subscription index in catalog «Russian Press» - 16356
ISSN 1997 - 4868 (Print)

The Journal is included in the list of the leading peer-reviewed scientific journals and publications, which should be published by the main results of theses for the degree of doctor and Ph.D.
The Journal is included in the Russian Science Citation Index.
Journal is included in the list of VAK (from 25.09.2017), No. 291

ISSN 2307-0005 (Online)
Key title: Agrarnyj vestnik Urala (Online)
Abbreviated key title: Agrar. vestn. Urala (Online)

Аграрный вестник Урала № 08 (187) 2019

Экономика

Некрасов К. В. кандидат экономических наук, доцент Уральский государственный аграрный университет

Набоков В. И. доктор экономических наук, профессор Уральский государственный аграрный университет

Кротов М.И. кандидат экономических наук, доцент Уральский государственный аграрный университет

Скворцов Е. А. старший преподаватель Уральский государственный аграрный университет

Скворцова Е. Г. аспирант Уральский государственный аграрный университет

УДК:637.112

Application of technologies of artificial intelligence in agriculture

 The technologies of artificial intelligence (AI) are used in various sectors of the national economy, including agriculture. The purpose of the study is to identify the essence and summarize the directions of application of AI technologies in agriculture. These technologies are used in various fields of agriculture: the detection of plant diseases, the classification and identification of weeds, the determination and counting of fruits, the management of water resources and soil, the prediction of weather (climate), and the determination of animal behavior. AI technologies used in agriculture have a number of significant features. First of all, it is a software and hardware tool. AI technologies perform an intellectual function when performing work in agriculture, which consists in the ability to perform abstract reasoning, recognize images, act in conditions of incomplete information, show creativity, and ability to learn. The strengths of the application of AI technologies include increased labor productivity in agricultural sectors, increased efficiency in managerial decision-making processes, as well as increased access to information, increased human capabilities in the workplace, and the emergence of new professions. The main features are connected with various technical breakthroughs, in particular, machine learning, neural networks, big data, etc. This will create additional jobs in high-tech sectors, including programming. AI technologies will optimize food production worldwide and reduce the problem of global hunger. One of the threats is the outlined lag of the Russian Federation in the development of these technologies for agriculture from the advanced countries. The results of the study can be used by the executive authorities in the development of programs for innovative agricultural development and technical modernization of the industry.


Keywords:

artificial intelligence, deep learning (DL), intellectual technologies, digital farming


References:

1. Nabokov V. I., Nekrasov K. V., Zueva O. N., Donskova L. A. Otraslevyye osobennosti kak faktor formirovaniya i razvitiya logisticheskikh sistem v APK [Industry features as a factor of formation and development of logistics systems in the agroindustrial complex] // Agrarian Bulletin of the Urals. 2016. No. 12 (154). Pp. 102–104. (In Russian.)

2. Nabokov V. I., Ishniyazova A. R., Nekrasov K. V. Sovershenstvovaniye innovatsionnoy deyatel’nosti sovremennykh organizatsiy: monografiya [Improvement of innovative activities of modern organizations: monograph]. Izhevsk: LLC „Print-2“, 2017. 140 p. (In Russian.)

3. Nabokov V. I., Skvortsov E. A., Nekrasov K. V. Kadrovaya problema i vnedreniye robototekhniki v sel’skom khozyaystve [Personnel problem and introduction of robotics in agriculture] // Financial economy. 2018. No. 7. Pp. 89–91. (In Russian.)

4. Nabokov V. I., Skvortsov E. A., Nekrasov K. V. Vnedreniye robototekhniki v organizatsiyakh sel’skogo khozyaystva [Introduction of robotics in agricultural organizations] // VIESH institute’ herald. 2018. No. 4 (33). Pp. 126–131. (In Russian.)

5. Nekrasov K. V. Organizatsionno-ekonomicheskiy mekhanizm innovatsionnogo razvitiya pererabatyvayushchikh organizatsiy molochno-produktovogo podkompleksa regiona: avtoreferat dis. ... kand. ekon. nauk [Organizational and economic mechanism of innovative development of the organizations of processing a dairy-grocery subcomplex of region: abstract of dissertation ... candidate of economic sciences]. Ekaterinburg: Ural State Agricultural Academy, 2013. 26 p. (In Russian.)

6. Fedorenko V. F., Chernoivanov V. I., Gol’tyapin V. Ya., Fedorenko I. V. Mirovyye tendentsii intellektualizatsii sel’skogo khozyaystva: nauchnyy analiticheskiy obzor [International trend of intellectualization of agriculture: scientific analytical review]. Moscow: Rosinformagrotekh, 2018. 232 p. (In Russian.)

7. Amatya S., Karkee M., Gongal A., Zhang Q., Whiting M. D. Detection of cherry tree branches with full foliage in planar architecture for automated sweet-cherry harvesting // Biosystems Engineering. 2015. No. 146. Pp. 3–15

8. Dutta R., Smith D., Rawnsley R., Bishop-Hurle, G., Hills J., Timms G., Henry D. Dynamic cattle behavioural classification using supervised ensemble classifiers // Computers and Electronics in Agriculture. 2015. No. 111. Pp. 18–28.

9. Mehdizadeh S., Behmanesh J., Khalili K. Using MARS, SVM, GEP and empirical equations for estimation of monthly mean reference evapotranspiration // Computers and Electronics in Agriculture. 2017. No. 139. Pp. 103–114.

10. Mouatadid S., Raj N., Deo R. C., Adarnowski J. F. Input selection and data-driven model performance optimization to predict the Standardized Precipitation and Evaporation Index in a drought-prone region // Atmospheric research. 2018. Т. 212. Pp. 130–149.

11. Prasad R., Deo R. C., Li Y., Maraseni T. Soil moisture forecasting by a hybrid machine learning technique: ELM integrated with ensemble empirical mode decomposition // Geoderma. 2018. Т. 330. Pp. 136–161.

12. Pantazi X. E., Moshou D., Oberti R., West J., Mouazen A. M., Bochtis D. Detection of biotic and abiotic stresses in crops by using hierarchical self organizing classifiers // Precision Agriculture. 2017. No. 18. Pp. 383–393.

13. Sengupta S., Lee W. S. Identification and determination of the number of immature green citrus fruit in a canopy under different ambient light conditions // Biosystems Engineering. 2015. No. 117. Pp. 51–61.


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