Computer Simulation and Optimization of Parameters of Configuration of the Contour of Moistening Under Drip Irrigation of Agricultures

Melikhova, Elena; Rogachev, Aleksey F.

doi:10.1007/978-3-030-13397-9_122

Cited by 13 publications

(6 citation statements)

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“…You should take into account the fact that the values of the received vegetation indices depend on the sensor characteristics (spatial and spectral resolution), illumination, and shooting conditions, so they cannot E3S Web of Conferences 203, 02013 (2020) EBWFF-2020 https://doi.org/10.1051/e3sconf/202020302013 provide absolute quantitative indicators of the studied vegetation characteristics. To accurately interpret them, it is necessary to use data from field measurements [6][7][8][10][11][12]. Fig.…”

Section: Resultsmentioning

confidence: 99%

Geoinformation monitoring of crop productivity using intelligent analysis of vegetation indices

et al. 2020

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One of the most promising areas of development of the system for managing programmed agricultural production and increasing its productivity at present in Russia and abroad is the use of remote monitoring and sensing systems for subsequent interpretation of the data obtained by calculating and visualizing vegetation indices using intelligent systems. Using geomonitoring systems allows you to monitor the state of crops, make timely decisions on the implementation of appropriate agricultural measures. The development of the remote monitoring system, due to the increase in the number of satellites and the formation of available space survey data, suggests the possibility of creating an effective system for monitoring agricultural land and assessing crop yields.

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Section: Resultsmentioning

confidence: 99%

Geoinformation monitoring of crop productivity using intelligent analysis of vegetation indices

et al. 2020

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“…As part of their research, they proposed approaches to the binary classification of the agricultural land condition based on the analysis of highresolution aerial photographs, programming the development of the agricultural production systems based on retrospective data on the yield of the agricultural crops as well as subsequent image analysis and vegetation indices` calculation. [13] V.V. Borodychev considers the possibilities of the agricultural production management using modern GIS technologies for monitoring the operation of the technical means in real time, involving the formation of a specialized list of the interactive interaction segments as well as the use of multifunctional software systems for data analysis and intelligent decision support.…”

Section: Discussionmentioning

confidence: 99%

Agriculture 4.0: the theoretical concept and its practical implementation

Polyakov

2021

E3S Web Conf.

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The paper discusses the main aspects of the formation and development of the “Agriculture 4.0”; its basic directions of the implementation are the active use of various digital technologies to be founded for the introduction of qualitatively new approaches to organizing the activities of the agricultural enterprises. Moreover, the formation of the digital platforms is considered as one of such approaches improving economic organizational and managerial relations that take place in the economic activity process of the agricultural enterprises. The conducted analysis made it possible to give the practical implementation of the ideas developed in the “Agriculture 4.0” concept. It is capable of both significantly improving efficiency of the agricultural production and reducing the dependence of the agro ecosystems` functioning from natural factors as well as contribution to the ecologization of the agricultural production processes.

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“…The complexity of practical processing of field experiment materials by the MAV method is due to the cumbersome calculations [5,9], the lack of available specialized software, or the need to adapt universal statistical data processing packages (Statistica, Statgrafics, etc.) to the features (experience scheme, repetition) of multi-factor field studies.…”

Section: Methodsmentioning

confidence: 99%

“…to the features (experience scheme, repetition) of multi-factor field studies. In this regard, the results of field experiments were processed by the MAV method using it in the MathCad 15 environment using a computer program developed at the Department of mathematical modeling and computer science of the Volgograd state UNIVERSITY [9,10]. The original x yield matrix is set in the MS EXCEL table processor format.…”

Section: Methodsmentioning

confidence: 99%

Regularities of beet root crop yield formation based on retrospective data

Melikhova

2020

E3S Web Conf.

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The article presents a statistical model of the impact of agromeliorative factors, including methods and modes of irrigation on the productivity of beet root crops in the combination of drip irrigation and fine sprinkling (MAV). The experiments were carried out according to a three – factor scheme providing for the regulation of the phytoclimate (factor A): A1 - drip irrigation; A2-drip irrigation together with the management of the phytoclimate by MAV. Hydrothermal regulation of the phytoclimate was carried out using additional equipment with an interval of 1 hour during the entire vegetation period, provided that the air temperature was higher than the biologically optimal 26°C. the parameters of controlling the lowest humidity of HB (factor B) were taken: B1 – 70 %; B2 – 80 %. On the basis of the dispersion statistical analysis of the results of field studies, the following statistically significant shares of their participation in the formation of the crop were established: factor A – 23%, factor B – 29%, factor C – 44%. The revealed joint influence of factors A and C on the variability of the crop of root crops, the share of which was two percent, exceeds the value of the influence of other pair interactions.

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Computer Simulation and Optimization of Parameters of Configuration of the Contour of Moistening Under Drip Irrigation of Agricultures

Cited by 13 publications

References 1 publication

Geoinformation monitoring of crop productivity using intelligent analysis of vegetation indices

Geoinformation monitoring of crop productivity using intelligent analysis of vegetation indices

Agriculture 4.0: the theoretical concept and its practical implementation

Regularities of beet root crop yield formation based on retrospective data

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