Mathematical Optimization as A Tool for the Development of "Smart" Agriculture in Kazakhstan

This article uses methods for predicting plant performance indicators in Kazakhstan. In the work, using deep learning, visualization of predicted indicators (indicators and others), statistics from predicted values ??and identified changes, time series have been developed. Sentinel satellite data an...

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Autores principales:	Nail Alikuly Beisekenov, Marzhan Anuarbekovna Sadenova, Petar Sabev Varbanov
Formato:	article
Lenguaje:	EN
Publicado:	AIDIC Servizi S.r.l. 2021
Materias:	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
Acceso en línea:	https://doaj.org/article/b5670bcb974f4752afa4785df0919b1d
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spelling	oai:doaj.org-article:b5670bcb974f4752afa4785df0919b1d2021-11-15T21:46:58ZMathematical Optimization as A Tool for the Development of "Smart" Agriculture in Kazakhstan10.3303/CET21882032283-9216https://doaj.org/article/b5670bcb974f4752afa4785df0919b1d2021-11-01T00:00:00Zhttps://www.cetjournal.it/index.php/cet/article/view/11996https://doaj.org/toc/2283-9216This article uses methods for predicting plant performance indicators in Kazakhstan. In the work, using deep learning, visualization of predicted indicators (indicators and others), statistics from predicted values ??and identified changes, time series have been developed. Sentinel satellite data and statistical indicators for the last few years for the agricultural territories of Kazakhstan are used as primary data. It is found that the upward trend in wheat quality, however, increases the size of fertilizers, variables based on the NDVI also significantly contribute to the forecasting model. It has been shown that the amount of applied fertilizer has stabilized in the past few years due to economic and environmental constraints, so NDVI-based models will become increasingly important for enhancing forecasting models. Four machine learning algorithms have been evaluated and compared, namely boosted regression trees (BRT) and support vector machine (SVM), to map and predict the field yield of the Experimental Oil Farm in East Kazakhstan using readily available additional data. Based on the results of the work, a forecast of crop yields and general statistical recommendations for increasing yields were obtained.Nail Alikuly BeisekenovMarzhan Anuarbekovna SadenovaPetar Sabev VarbanovAIDIC Servizi S.r.l.articleChemical engineeringTP155-156Computer engineering. Computer hardwareTK7885-7895ENChemical Engineering Transactions, Vol 88 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895
spellingShingle	Chemical engineering TP155-156 Computer engineering. Computer hardware TK7885-7895 Nail Alikuly Beisekenov Marzhan Anuarbekovna Sadenova Petar Sabev Varbanov Mathematical Optimization as A Tool for the Development of "Smart" Agriculture in Kazakhstan
description	This article uses methods for predicting plant performance indicators in Kazakhstan. In the work, using deep learning, visualization of predicted indicators (indicators and others), statistics from predicted values ??and identified changes, time series have been developed. Sentinel satellite data and statistical indicators for the last few years for the agricultural territories of Kazakhstan are used as primary data. It is found that the upward trend in wheat quality, however, increases the size of fertilizers, variables based on the NDVI also significantly contribute to the forecasting model. It has been shown that the amount of applied fertilizer has stabilized in the past few years due to economic and environmental constraints, so NDVI-based models will become increasingly important for enhancing forecasting models. Four machine learning algorithms have been evaluated and compared, namely boosted regression trees (BRT) and support vector machine (SVM), to map and predict the field yield of the Experimental Oil Farm in East Kazakhstan using readily available additional data. Based on the results of the work, a forecast of crop yields and general statistical recommendations for increasing yields were obtained.
format	article
author	Nail Alikuly Beisekenov Marzhan Anuarbekovna Sadenova Petar Sabev Varbanov
author_facet	Nail Alikuly Beisekenov Marzhan Anuarbekovna Sadenova Petar Sabev Varbanov
author_sort	Nail Alikuly Beisekenov
title	Mathematical Optimization as A Tool for the Development of "Smart" Agriculture in Kazakhstan
title_short	Mathematical Optimization as A Tool for the Development of "Smart" Agriculture in Kazakhstan
title_full	Mathematical Optimization as A Tool for the Development of "Smart" Agriculture in Kazakhstan
title_fullStr	Mathematical Optimization as A Tool for the Development of "Smart" Agriculture in Kazakhstan
title_full_unstemmed	Mathematical Optimization as A Tool for the Development of "Smart" Agriculture in Kazakhstan
title_sort	mathematical optimization as a tool for the development of "smart" agriculture in kazakhstan
publisher	AIDIC Servizi S.r.l.
publishDate	2021
url	https://doaj.org/article/b5670bcb974f4752afa4785df0919b1d
work_keys_str_mv	AT nailalikulybeisekenov mathematicaloptimizationasatoolforthedevelopmentofsmartagricultureinkazakhstan AT marzhananuarbekovnasadenova mathematicaloptimizationasatoolforthedevelopmentofsmartagricultureinkazakhstan AT petarsabevvarbanov mathematicaloptimizationasatoolforthedevelopmentofsmartagricultureinkazakhstan
_version_	1718426866668273664

Mathematical Optimization as A Tool for the Development of "Smart" Agriculture in Kazakhstan

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