Crops reclamation management based on hybrid neuro-fuzzy systems

Токарев, К Е; Orlova, Yulia; Rogachev, Aleksey F.; Kuzmin, V A; Tokareva, Yu M

doi:10.1088/1755-1315/421/4/042015

Cited by 10 publications

(2 citation statements)

References 4 publications

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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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“…The use of the cognitive approach based on the construction, structural-parametric identification, and research of fuzzy cognitive maps (FCM) is limited by methodological difficulties of modeling in real time [2,7,12]. Based on a systematic approach, a review of foreign and domestic publications has revealed promising areas of time series data Mining, which combines statistical, fuzzy-multiple, hybrid neural network and cognitive technologies for analyzing multidimensional time series [2,12,15]. Cognitive methods allow us to analyze indistinctly defined data and predict not only the direction of changes in the MVR, but also to generate brief explanations of the evolution of such series in a linguistic form [12].…”

Section: Introductionmentioning

confidence: 99%

Cognitive Modeling of Socio-Economic Systems with Functional Consideration of the Time Factor

Rogachev

Melikhova

2021

SHS Web Conf.

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The research analyzes scientific and methodological approaches for structural and parametric identification of fuzzy cognitive models based on time-dependent functional dependencies. In the process of constructing and identifying fuzzy cognitive models, the stages of structural identification with the definition of a set of concepts and unclear relationships over this set, parametric identification, which implements the transition to fuzzy identification with the definition of the intensity of influence between factors, are implemented. A specialized software system was developed for the purpose of computer support for modeling and studying the influence of the time factor set by changes in the strength of connections between concepts. A representation of each of the elements of the FCM adjacency matrix is proposed in the form of an additive expression controlled by the alpha parameter, which includes a constant component determined by the expert method and a control function that depends on real or dimensionless model time. To correct the elements of the FCM adjacency matrix, it is proposed to use a 3-parameter parabolic or modified 4-parameter exponential dependence as a function that depends on real or dimensionless model time. The developed modified method for solving the problem of time factor accounting using functional dependencies for the strength of mutual influence of concepts provides for expanding the capabilities of fuzzy cognitive models by taking into account the time factor.

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Learning of Convolutional Neural Networks in the Tasks of Land Use Digitization Based on CUDA Technologies

Tereliansky

Rogachev²,

Melikhova

2021

Studies in Systems, Decision and Control

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Crops reclamation management based on hybrid neuro-fuzzy systems

Cited by 10 publications

References 4 publications

Geoinformation monitoring of crop productivity using intelligent analysis of vegetation indices

Geoinformation monitoring of crop productivity using intelligent analysis of vegetation indices

Cognitive Modeling of Socio-Economic Systems with Functional Consideration of the Time Factor

Learning of Convolutional Neural Networks in the Tasks of Land Use Digitization Based on CUDA Technologies

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