Detail publikace

Machine learning model identification for forecasting of soya crop yields in Kazakhstan

Beisekenov, N.A., Anuarbekov, T.B., Sadenova, M.A., Varbanov, P.S., Klemeš, J.J., Wang, J.

Originální název

Machine learning model identification for forecasting of soya crop yields in Kazakhstan

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

In this article, using the example of soybean production in Kazakhstan, the features of using a new neuroprogramming method for analyzing data from field experiments and predicting yield are considered. It is shown that using historical statistics over several years, the program can create a trained model that is useful for predicting future values (profitability charts, anomalies, efficiency). The average error of the created neural yield model is 0.00894. The correlation coefficient of the developed neuromodel is 0.9602; determination coefficient - 0.9887. Based on the results of the work, a forecast of the yield of agricultural crops was obtained and recommendations were formulated to increase the yield of soybeans. © 2021 University of Split, FESB.

Klíčová slova

Machine learning; Neural networks; Time-series rhythm; Vegetation index; Yield forecast

Autoři

Beisekenov, N.A., Anuarbekov, T.B., Sadenova, M.A., Varbanov, P.S., Klemeš, J.J., Wang, J.

Vydáno

8. 9. 2021

Nakladatel

Institute of Electrical and Electronics Engineers Inc.

ISBN

9789532901122

Kniha

2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)

Strany od

173101

Strany do

173101

Strany počet

13

BibTex

@inproceedings{BUT173228,
  author="Petar Sabev {Varbanov} and Jiří {Klemeš} and Jin {Wang}",
  title="Machine learning model identification for forecasting of soya crop yields in Kazakhstan",
  booktitle="2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)",
  year="2021",
  pages="173101--173101",
  publisher="Institute of Electrical and Electronics Engineers Inc.",
  doi="10.23919/SpliTech52315.2021.9566376",
  isbn="9789532901122"
}