Machine learning model identification for forecasting of soya crop yields in Kazakhstan
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Machine learning model identification for forecasting of soya crop yields in Kazakhstan
Original language description
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.
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20704 - Energy and fuels
Result continuities
Project
<a href="/en/project/EF15_003%2F0000456" target="_blank" >EF15_003/0000456: Sustainable Process Integration Laboratory (SPIL)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2021
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2021 6th International Conference on Smart and Sustainable Technologies (SpliTech)
ISBN
9789532901122
ISSN
—
e-ISSN
—
Number of pages
13
Pages from-to
173101-173101
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
neuveden
Event location
Bol and Split
Event date
Sep 8, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—