2023, Vol. 10, No. 1. - go to content...

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DOI: 10.15862/03NZOR123 (https://doi.org/10.15862/03NZOR123)

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Ovchinnikov A.Yu., Afanasyeva Yu.S. «Precision farming» as a concept for big data management in agriculture. Russian journal of resources, conservation and recycling. 2023; 10(1). Available at: https://resources.today/PDF/03NZOR123.pdf (in Russian). DOI: 10.15862/03NZOR123

«Precision farming» as a concept for big data management in agriculture

Ovchinnikov Aleksey Yurievich
Federal Scientific Agroengineering Center VIM, Moscow, Russia
E-mail: aleksovchinn@gmail.com
ORCID: https://orcid.org/0000-0002-2188-1527
RSCI: https://www.elibrary.ru/author_profile.asp?id=1116441

Afanasyeva Yulia Stanislavovna
S.Y. Witte Moscow University
Ryazan branch, Ryazan, Russia
E-mail: kalab-yuliya@yandex.ru
ORCID: https://orcid.org/0000-0002-0678-1990
RSCI: https://www.elibrary.ru/author_profile.asp?id=957839

Abstract. The article describes precision farming technologies (FT) for use in agriculture. Precision farming is a new system that is designed to increase crop yields through the use of various aspects, such as technology, management and information, in order to increase productivity, improve crop quality, protect the environment and save energy. Information related to agricultural land is difficult to store due to large amounts of data and annual fluctuations in the distribution of agricultural land. One of the main and responsible components of the FT consists of several sets of satellites. These sets of satellites send radio signals in the form of waves to a receiver installed on earth, the receivers, having received the signals, process them and find out the exact topographic position. Precision farming consists of remote sensing techniques using smart sensors, which play a vital role in monitoring, obtaining and providing processed crop data to farmers. Precision farming is positioned as a new agricultural practice used by farmers to increase crop productivity through the use of modern technologies such as IoT (Internet of Things), AI (artificial intelligence), ML (machine learning) and cloud computing. To date, most studies conducted in the field of FT show that FT-based agriculture has significantly affected the productivity and sustainability of crops. Thus, FT is an attractive concept that naturally meets the farmer’s expectations for more efficient use of agricultural resources.

Keywords: agriculture; precision farming; machine learning; artificial intelligence

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