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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F21%3APU143074" target="_blank" >RIV/00216305:26210/21:PU143074 - isvavai.cz</a>

Výsledek na webu

<a href="http://www.cetjournal.it/cet/21/88/203.pdf" target="_blank" >http://www.cetjournal.it/cet/21/88/203.pdf</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3303/CET2188203" target="_blank" >10.3303/CET2188203</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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. © 2021, AIDIC Servizi S.r.l.

Název v anglickém jazyce

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

Popis výsledku anglicky

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. © 2021, AIDIC Servizi S.r.l.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

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 periodika

Chemical Engineering Transactions

ISSN

2283-9216

e-ISSN

—

Svazek periodika

neuveden

Číslo periodika v rámci svazku

88

Stát vydavatele periodika

IT - Italská republika

Počet stran výsledku

6

Strana od-do

1219-1224

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85122427930