Application of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstan

Simple item page
dc.contributor.authorSadenova, Marzhancs
dc.contributor.authorBeisekenov, Nailcs
dc.contributor.authorVarbanov, Petar Sabevcs
dc.contributor.authorPan, Tingcs
dc.coverage.issue6cs
dc.coverage.volume13cs
dc.date.accessioned2024-02-16T07:45:41Z
dc.date.available2024-02-16T07:45:41Z
dc.date.issued2023-06-01cs
dc.description.abstractThe article provides an overview of the accuracy of various yield forecasting algorithms and offers a detailed explanation of the models and machine learning algorithms that are required for crop yield forecasting. A unified crop yield forecasting methodology is developed, which can be adjusted by adding new indicators and extensions. The proposed methodology is based on remote sensing data taken from free sources. Experiments were carried out on crops of cereals, legumes, oilseeds and forage crops in eastern Kazakhstan. Data on agricultural lands of the experimental farms were obtained using processed images from Sentinel-2 and Landsat-8 satellites (EO Browser) for the period of 2017-2022. In total, a dataset of 1600 indicators was collected with NDVI and MSAVI indices recorded at a frequency of once a week. Based on the results of this work, it is found that yields can be predicted from NDVI vegetation index data and meteorological data on average temperature, surface soil moisture and wind speed. A machine learning programming language can calculate the relationship between these indicators and build a neural network that predicts yield. The neural network produces predictions based on the constructed data weights, which are corrected using activation function algorithms. As a result of the research, the functions with the highest prediction accuracy during vegetative development for all crops presented in this paper are multi-layer perceptron, with a prediction accuracy of 66% to 99% (85% on average), and polynomial regression, with a prediction accuracy of 63% to 98% (82% on average). Thus, it is shown that the use of machine learning and neural networks for crop yield prediction has advantages over other mathematical modelling techniques. The use of machine learning (neural network) technologies makes it possible to predict crop yields on the basis of relevant data. The individual approach of machine learning to each crop allows for the determination of the optimal learning algorithms to obtain accurate predictions.en
dc.formattextcs
dc.format.extent1-27cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationAgriculture. 2023, vol. 13, issue 6, p. 1-27.en
dc.identifier.doi10.3390/agriculture13061195cs
dc.identifier.issn2077-0472cs
dc.identifier.orcid0000-0001-5261-1645cs
dc.identifier.other187361cs
dc.identifier.researcheridB-8954-2009cs
dc.identifier.scopus6603469420cs
dc.identifier.urihttps://hdl.handle.net/11012/244980
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofAgriculturecs
dc.relation.urihttps://www.mdpi.com/2077-0472/13/6/1195cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2077-0472/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectcerealsen
dc.subjectforage cropsen
dc.subjectgrain legumesen
dc.subjectmachine learningen
dc.subjectoilseedsen
dc.subjectremote sensingen
dc.subjectsustainable farming practicesen
dc.subjectyield forecastingen
dc.titleApplication of Machine Learning and Neural Networks to Predict the Yield of Cereals, Legumes, Oilseeds and Forage Crops in Kazakhstanen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-187361en
sync.item.dbtypeVAVen
sync.item.insts2024.02.16 08:45:40en
sync.item.modts2024.02.16 08:13:14en
thesis.grantorVysoké učení technické v Brně. Fakulta strojního inženýrství. Laboratoř integrace procesůcs
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
agriculture1301195v3.pdf
Size:
4.97 MB
Format:
Adobe Portable Document Format
Description:
file agriculture1301195v3.pdf
Download
Collections
Laboratoř integrace procesů