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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU142155" target="_blank" >RIV/00216305:26210/21:PU142155 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.23919/SpliTech52315.2021.9566376" target="_blank" >http://dx.doi.org/10.23919/SpliTech52315.2021.9566376</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.23919/SpliTech52315.2021.9566376" target="_blank" >10.23919/SpliTech52315.2021.9566376</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20704 - Energy and fuels

Projekt

<a href="/cs/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Laboratoř integrace procesů pro trvalou udržitelnost</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2021

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

ISBN

9789532901122

ISSN

—

e-ISSN

—

Počet stran výsledku

13

Strana od-do

173101-173101

Název nakladatele

Institute of Electrical and Electronics Engineers Inc.

Místo vydání

neuveden

Místo konání akce

Bol and Split

Datum konání akce

8. 9. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—