Development of a methodology for evaluating the efficiency level of monitoring agroecosystems using big data technologies

Aleksey Petrov,Sergey Grigorishin,Leonid Andreev,N.A Shyurova,O.V Tkachenko

doi:10.1051/bioconf/20224303012

Aleksey Petrov, Sergey Grigorishin + Show 3 more

Open Access

https://doi.org/10.1051/bioconf/20224303012

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Abstract

The article discusses the theory of monitoring agroecosystems for the effectiveness of using Big Data technologies. The relationship between the agricultural areas of the Tyumen region, the Big Data sources available in them, and the technologies for working with Big Data obtained from sources are described. The article also developed a methodology that makes it possible to assess the level of effectiveness of monitoring agroecosystems using Big Data technologies, based on the result of which a strategy for development of the region as a whole and its agroecosystems, in particular, is formed in terms of equipment with information technologies. The methodology presented in the article is formed on the basis of an engineering ontology, which in the future is able to lower the degree of the human factor in global and local monitoring of agroecosystems for the effectiveness of using Big Data technologies.

Highlights

The monitoring and assessment of natural ecosystems primarily implies development and operation of such observation systems, on the basis of which it is possible to assess their condition
This implies that if ten sensors of soil acidity are located on some hypothetical agricultural field, using Big Data technologies will be ineffective, since the data obtained can be processed in simple Microsoft Excel and there is no point in creating software for analyzing such data based on Python or another programming language
Optical sensors; Information posted in the database of government agencies; Digital web libraries; Information from private banks; Survey data; Neural networks; Geospatial modeling; Statistical modeling; Image processing; Big Data Benchmarking; Web services; Mobile applications; Cloud computing with a distributed computing model; Geospatial modeling; Computer vision; Artificial intelligence; Statistical modeling; Predictive analytics; Cloud technologies

Summary

Introduction

The monitoring and assessment of natural ecosystems primarily implies development and operation of such observation systems, on the basis of which it is possible to assess their condition. The assessment, in turn, is primarily the result of an analysis of the quantitative characteristics of ecosystems, which are dynamic, since they have the property of variability, relative to the impact of external factors (the name of the factors depends on the type of ecosystems). Agricultural ecosystems are commonly referred to as agroecosystems. Such monitoring of agroecosystems [1], on the one hand, is of great economic importance for the region [2], since, based on its result, the agroecosystem manager can develop a strategy and take measures (crop conservation, pest control, and etc.), on the other hand, it is of great ecological importance, as agroecosystems are part of larger natural ecosystems

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