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Estimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions

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dc.contributor.authorSabzi, Sajad
dc.contributor.authorPourdarbani, Razieh
dc.contributor.authorRohban, Mohammad H.
dc.contributor.authorGarcía Mateos, Ginés
dc.contributor.authorArribas, J. I.
dc.contributor.departmentInformática y Sistemas
dc.contributor.otherFacultades de la UMU::Facultad de Informática
dc.date.accessioned2026-01-18T16:15:28Z
dc.date.available2026-01-18T16:15:28Z
dc.date.copyright© 2021 The Authors
dc.date.issued2021-10-15
dc.description.abstractIn recent years, farmers have often mistakenly resorted to overuse of chemical fertilizers to increase crop yield. However, excessive consumption of fertilizers might lead to severe food poisoning. If nutritional deficiencies are detected early, it can help farmers to design better fertigation practices before the problem becomes unsolvable. The aim of this study is to predict the amount of nitrogen (N) content in cucumber (Cucumis sativus L., var. Super Arshiya-F1) plant leaves using hyperspectral imaging (HSI) techniques and three different regression methods: a hybrid artificial neural networks-particle swarm optimization (ANN-PSO); partial least squares regression (PLSR); and unidimensional deep learning convolutional neural networks (CNN). Cucumber plant seeds were planted in 20 different pots. After growing the plants, pots were categorized and three levels of nitrogen overdose were applied to each category: 30%, 60% and 90% excesses, called N30%, N60%, N90%, respectively. HSI images of plant leaves were captured before and after the application of nitrogen excess. A prediction regression model was developed for each individual category. Results showed that mean regression coefficients (R) for ANN-PSO were inside 0.937–0.965, PLSR 0.975–0.997, and CNN 0.965–0.985 ranges, test set. We conclude that regression models have a remarkable ability to accurately predict the amount of nitrogen content in cucumber plants from hyperspectral leaf images in a non-destructive way, being PLSR slightly ahead of CNN and ANN-PSO methods.
dc.formatapplication/pdf
dc.format.extent14
dc.identifier.citationChemometrics and Intelligent Laboratory Systems, 2021, Vol. 217 : 104404
dc.identifier.doihttps://doi.org/10.1016/j.chemolab.2021.104404
dc.identifier.eissn1873-3239
dc.identifier.issn0169-7439
dc.identifier.urihttp://hdl.handle.net/10201/187909
dc.languagespa
dc.publisherElsevier
dc.relationThis research was funded in part by the Spanish Ministry for Science, Innovation and Universities (MICINN), Agencia Estatal de Investigación (AEI), as well as by the Fondo Europeo de Desarrollo Regional funds (FEDER, EU), under grant numbers RTI2018-098958-B-I00 (J.I. Arribas) and RTI2018-098156-B-C53 (G. Garcia-Mateos).
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S0169743921001726
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectRegression
dc.subjectPrediction
dc.subjectHyperspectral imaging
dc.subjectImage processing
dc.subjectLeaf
dc.subjectMachine learning
dc.subjectNitrogen
dc.subjectOptimization
dc.subjectCucumber
dc.subjectPlant
dc.subject.odsObjetivo 3: Salud
dc.subject.odsObjetivo 12: Producción y consumo sostenibles
dc.subject.odsObjetivo 2: Hambre y seguridad alimentaria
dc.titleEstimation of nitrogen content in cucumber plant (Cucumis sativus L.) leaves using hyperspectral imaging data with neural network and partial least squares regressions
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/publishedVersion
dspace.entity.typePublicationes
relation.isAuthorOfPublicationbb68ac7e-f852-4bc6-a7b4-e9a37a5617c2
relation.isAuthorOfPublication.latestForDiscoverybb68ac7e-f852-4bc6-a7b4-e9a37a5617c2
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