Publication detail

Modelling of Alfalfa Yield Forecasting Based on Earth Remote Sensing (ERS) Data and Remote Sensing Methods

Sadenova, M.A., Beisekenov, N.A., Apshikur, B., Khrapov, S.S., Kapasov, A.K., Mamysheva, A.M., Klemeš, J.J.

Original Title

Modelling of Alfalfa Yield Forecasting Based on Earth Remote Sensing (ERS) Data and Remote Sensing Methods

Type

journal article in Scopus

Language

English

Original Abstract

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.

Keywords

Modelling; Alfalfa Yield; Forecasting; Based on; Earth; Remote; Sensing; ERS; Data; Methods

Authors

Sadenova, M.A., Beisekenov, N.A., Apshikur, B., Khrapov, S.S., Kapasov, A.K., Mamysheva, A.M., Klemeš, J.J.

Released

1. 9. 2022

Publisher

Italian Association of Chemical Engineering - AIDIC

ISBN

2283-9216

Periodical

Chemical Engineering Transactions

Number

94

State

Republic of Italy

Pages from

697

Pages to

702

Pages count

6

URL

http://www.cetjournal.it/cet/22/94/116.pdf

BibTex

@article{BUT179625,
  author="Jiří {Klemeš}",
  title="Modelling of Alfalfa Yield Forecasting Based on Earth Remote Sensing (ERS) Data and Remote Sensing Methods",
  journal="Chemical Engineering Transactions",
  year="2022",
  number="94",
  pages="697--702",
  doi="10.3303/CET2294116",
  issn="2283-9216",
  url="http://www.cetjournal.it/cet/22/94/116.pdf"
}