Publication 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.

English title

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

Type

conference paper

Language

en

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 in English

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

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–to

173101–173101

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",
  month="September",
  pages="173101--173101",
  publisher="Institute of Electrical and Electronics Engineers Inc.",
  isbn="9789532901122"
}