Modelling of Alfalfa Yield Forecasting Based on Earth Remote Sensing (ERS) Data and Remote Sensing Methods
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F22%3APU145941" target="_blank" >RIV/00216305:26210/22:PU145941 - isvavai.cz</a>
Result on the web
<a href="http://www.cetjournal.it/cet/22/94/116.pdf" target="_blank" >http://www.cetjournal.it/cet/22/94/116.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3303/CET2294116" target="_blank" >10.3303/CET2294116</a>
Alternative languages
Result language
angličtina
Original language name
Modelling of Alfalfa Yield Forecasting Based on Earth Remote Sensing (ERS) Data and Remote Sensing Methods
Original language description
This study aims to develop a method for modelling early forecasting of alfalfa yield on a farm scale located in East Kazakhstan. The authors evaluated the correlation coefficient between forage crop yield and different data sets, including weather data, climate indices, spectral indices from drones and satellite observations. An ensemble machine learning model was developed by combining three commonly used basic training modules: random forest (RF), support vector method (SVM), and multiple linear regression (MLR). It is found that the best yield prediction algorithm in this study is the Random Forest (RF) algorithm, which predicts yields with R2 = 0.94 and RMSE = 0.25 t/ha. The results of this study showed that combining remote sensing drought indices with climatic and weather variables from UAV and satellite imagery using machine learning is a promising approach for alfalfa yield prediction.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
CEP classification
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OECD FORD branch
20704 - Energy and fuels
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Chemical Engineering Transactions
ISSN
2283-9216
e-ISSN
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Volume of the periodical
neuveden
Issue of the periodical within the volume
94
Country of publishing house
IT - ITALY
Number of pages
6
Pages from-to
697-702
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85139245353