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dc.contributor.authorFariñas, María Dolores
dc.contributor.authorJimenez-Carretero, Daniel 
dc.contributor.authorSancho-Knapik, Domingo
dc.contributor.authorPeguero-Pina, José Javier
dc.contributor.authorGil-Pelegrín, Eustaquio
dc.contributor.authorGómez Álvarez-Arenas, Tomás
dc.identifier.citationPlant Methods. 2019; 15:128es_ES
dc.description.abstractBackground: Non-contact resonant ultrasound spectroscopy (NC-RUS) has been proven as a reliable technique for the dynamic determination of leaf water status. It has been already tested in more than 50 plant species. In parallel, relative water content (RWC) is highly used in the ecophysiological field to describe the degree of water saturation in plant leaves. Obtaining RWC implies a cumbersome and destructive process that can introduce artefacts and cannot be determined instantaneously. Results: Here, we present a method for the estimation of RWC in plant leaves from non-contact resonant ultrasound spectroscopy (NC-RUS) data. This technique enables to collect transmission coefficient in a [0.15-1.6] MHz frequency range from plant leaves in a non-invasive, non-destructive and rapid way. Two different approaches for the proposed method are evaluated: convolutional neural networks (CNN) and random forest (RF). While CNN takes the entire ultrasonic spectra acquired from the leaves, RF only uses four relevant parameters resulted from the transmission coefficient data. Both methods were tested successfully in Viburnum tinus leaf samples with Pearson's correlations between 0.92 and 0.84. Conclusions: This study showed that the combination of NC-RUS technique with deep learning algorithms is a robust tool for the instantaneous, accurate and non-destructive determination of RWC in plant leaves.es_ES
dc.description.sponsorshipThis research was supported by Grant (DPI2016-78876-R-AEI/FEDER, UE) from the Spanish State Research Agency (AEI) and the European Regional Development Fund (ERDF/FEDER). It was also partially funded by Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) Grant number RTA2015-00054-C02-01 and by Gobierno de Aragón H09_17R research group (DOC INIA-CCAA (ESF)). Funding was provided by Conselleria d’Educació, Investigació, Cultura i Esport (APOSTD/2018/203 (ESF))es_ES
dc.publisherBioMed Central (BMC) es_ES
dc.subjectMachine learninges_ES
dc.subjectPlant leaveses_ES
dc.titleInstantaneous and non-destructive relative water content estimation from deep learning applied to resonant ultrasonic spectra of plant leaveses_ES
dc.typejournal articlees_ES
dc.rights.licenseAtribución 4.0 Internacional*
dc.contributor.funderMinisterio de Ciencia, Innovación y Universidades (España) 
dc.contributor.funderUnión Europea. Fondo Europeo de Desarrollo Regional (FEDER/ERDF) 
dc.contributor.funderInstituto Nacional de Investigación y Tecnología Agraria y Alimentaria (España) 
dc.contributor.funderGobierno de Aragón (España) 
dc.identifier.journalPlant methodses_ES
dc.repisalud.orgCNICCNIC::Unidades técnicas::Celómicaes_ES
dc.rights.accessRightsopen accesses_ES

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