Publication result detail

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.

Original Title

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

English Title

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

Type

Paper in proceedings (conference paper)

Original Abstract

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.

English abstract

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.

Keywords

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

Key words in English

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

Authors

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

RIV year

2022

Released

08.09.2021

Publisher

Institute of Electrical and Electronics Engineers Inc.

ISBN

9789532901122

Book

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

Pages from

173101

Pages to

173101

Pages count

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"
}