Simple item page
 

Prediction of Potato (Solanum tuberosum L.) Yield Based on Machine Learning Methods

cris.virtual.author-orcid0000-0003-3721-6473
cris.virtual.author-orcid0000-0002-2222-6866
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtual.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid3fe42726-36c4-478a-818f-a10f72d4a6ef
cris.virtualsource.author-orcid09c81af2-c2b2-424c-b2ec-4cda600883bf
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
cris.virtualsource.author-orcid#PLACEHOLDER_PARENT_METADATA_VALUE#
dc.abstract.enThis research delves into the application of machine learning methods for predicting the yield of potato varieties used for French fries in Poland. By integrating a comprehensive dataset comprising agronomical, climatic, soil, and satellite-based vegetation data from 36 commercial potato fields over five growing seasons (2018–2022), we developed three distinct models: non-satellite, satellite, and hybrid. The non-satellite model, relying on 85 features, excludes vegetation indices, whereas the satellite model includes these indices within its 128 features. The hybrid model, combining all available features, encompasses a total of 165 features, presenting the most-comprehensive approach. Our findings revealed that the hybrid model, particularly when enhanced with SVM outlier detection, exhibited superior performance with the lowest Mean Absolute Percentage Error (MAPE) of 5.85%, underscoring the effectiveness of integrating diverse data sources into agricultural yield prediction. In contrast, the non-satellite and satellite models displayed higher MAPE values, indicating less accuracy compared to the hybrid model. Advanced data-processing techniques such as PCA and outlier detection methods (LOF and One-Class SVM) played a pivotal role in model performance, optimising feature selection and dataset refinement. The study concluded that machine learning methods, particularly when leveraging a multifaceted approach involving a wide array of data sources and advanced processing techniques, can significantly enhance the accuracy of agricultural yield predictions. These insights pave the way for more-efficient and -informed agricultural practices, emphasising the potential of machine learning in revolutionising yield prediction and crop management.
dc.affiliationWydział Inżynierii Środowiska i Inżynierii Mechanicznej
dc.affiliation.instituteKatedra Inżynierii Biosystemów
dc.contributor.authorKurek, Jarosław
dc.contributor.authorNiedbała, Gniewko
dc.contributor.authorWojciechowski, Tomasz
dc.contributor.authorŚwiderski, Bartosz
dc.contributor.authorAntoniuk, Izabella
dc.contributor.authorPiekutowska, Magdalena
dc.contributor.authorKruk, Michał
dc.contributor.authorBobran, Krzysztof
dc.date.access2025-05-27
dc.date.accessioned2025-10-27T12:14:55Z
dc.date.available2025-10-27T12:14:55Z
dc.date.copyright2023-12-11
dc.date.issued2023
dc.description.abstract<jats:p>This research delves into the application of machine learning methods for predicting the yield of potato varieties used for French fries in Poland. By integrating a comprehensive dataset comprising agronomical, climatic, soil, and satellite-based vegetation data from 36 commercial potato fields over five growing seasons (2018–2022), we developed three distinct models: non-satellite, satellite, and hybrid. The non-satellite model, relying on 85 features, excludes vegetation indices, whereas the satellite model includes these indices within its 128 features. The hybrid model, combining all available features, encompasses a total of 165 features, presenting the most-comprehensive approach. Our findings revealed that the hybrid model, particularly when enhanced with SVM outlier detection, exhibited superior performance with the lowest Mean Absolute Percentage Error (MAPE) of 5.85%, underscoring the effectiveness of integrating diverse data sources into agricultural yield prediction. In contrast, the non-satellite and satellite models displayed higher MAPE values, indicating less accuracy compared to the hybrid model. Advanced data-processing techniques such as PCA and outlier detection methods (LOF and One-Class SVM) played a pivotal role in model performance, optimising feature selection and dataset refinement. The study concluded that machine learning methods, particularly when leveraging a multifaceted approach involving a wide array of data sources and advanced processing techniques, can significantly enhance the accuracy of agricultural yield predictions. These insights pave the way for more-efficient and -informed agricultural practices, emphasising the potential of machine learning in revolutionising yield prediction and crop management.</jats:p>
dc.description.accesstimeat_publication
dc.description.bibliographyil., bibliogr.
dc.description.financepublication_nocost
dc.description.financecost0,00
dc.description.if3,3
dc.description.number12
dc.description.points140
dc.description.versionfinal_published
dc.description.volume13
dc.identifier.doi10.3390/agriculture13122259
dc.identifier.issn2077-0472
dc.identifier.urihttps://sciencerep.up.poznan.pl/handle/item/5487
dc.identifier.weblinkhttp://www.mdpi.com/2077-0472/13/12/2259
dc.languageen
dc.relation.ispartofAgriculture (Switzerland)
dc.relation.pagesart. 2259
dc.rightsCC-BY
dc.sciencecloudnosend
dc.share.typeOPEN_JOURNAL
dc.subject.enmachine learning
dc.subject.enyield prediction
dc.subject.enpotato
dc.titlePrediction of Potato (Solanum tuberosum L.) Yield Based on Machine Learning Methods
dc.title.volumeSpecial Issue Digital Innovations in Agriculture-Series II
dc.typeJournalArticle
dspace.entity.typePublication
oaire.citation.issue12
oaire.citation.volume13