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dc.contributor.authorNoguera, Miguel
dc.contributor.authorMillán Prior, Borja 
dc.contributor.authorAndújar Márquez, José Manuel 
dc.date.accessioned2023-07-07T10:51:17Z
dc.date.available2023-07-07T10:51:17Z
dc.date.issued2022-12
dc.identifier.citationNoguera, M., Millan, B., & Andújar, J. M. (2022). New, Low-Cost, Hand-Held Multispectral Device for In-Field Fruit-Ripening Assessment. In Agriculture (Vol. 13, Issue 1, p. 4). MDPI AG. https://doi.org/10.3390/agriculture13010004es_ES
dc.identifier.issn2077-0472 (electrónico)
dc.identifier.urihttps://hdl.handle.net/10272/22282
dc.description.abstractThe state of ripeness at harvest is a key piece of information for growers as it determines the market price of the yield. This has been traditionally assessed by destructive chemical methods, which lead to low-spatiotemporal resolution in the monitorization of crop development and poor responsiveness for growers. These limitations have shifted the focus to remote-sensing, spectroscopy-based approaches. However, most of the research focusing on these approaches has been accomplished with expensive equipment, which is exorbitant for most users. To combat this issue, this work presents a low-cost, hand-held, multispectral device with original hardware specially designed to face the complexity related to in-field use. The proposed device is based on a development board (AS7265x, AMS AG) that has three sensor chips with a spectral response of eighteen channels in a range from 410 to 940 nm. The proposed device was evaluated in a red-grape field experiment. Briefly, it was used to acquire the spectral signature of eighty red-grape samples in the vineyard. Subsequently, the grape samples were analysed using standard chemical methods to generate ground-truth values of ripening status indicators (soluble solid content (SSC) and titratable acidity (TA)). The eighteen pre-process reflectance measurements were used as input for training artificial neural network models to estimate the two target parameters (SSC and TA). The developed estimation models were evaluated through a leave-one-out cross-validation approach obtaining promising results (R2 = 0.70, RMSE = 1.21 for SSC; and R2 = 0.67, RMSE = 0.91 for TA).es_ES
dc.description.sponsorshipThis work was supported by grant PID2020-119217RA-I00, funded byMCIN/AEI/10.13039/501100011033; grant IJC2019-040114-I, funded by MCIN/AEI/10.13039/501100011033; and grant 0766_OLIVAIS_5_E, funded by the Interreg Cooperation Program V-A SPAIN-PORTUGAL (POCTEP) 2014-2020, and co-financed with ERDF.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relation.isversionofPublisher’s version
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subject.otherSensores_ES
dc.subject.otherMultispectrales_ES
dc.subject.otherPrecision farminges_ES
dc.subject.otherMachine learninges_ES
dc.subject.otherArtificial neural networkes_ES
dc.subject.otherAS7265xes_ES
dc.titleNew, Low-Cost, Hand-Held Multispectral Device for In-Field Fruit-Ripening Assessmentes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.3390/agriculture13010004
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.subject.unesco33 Ciencias Tecnológicases_ES


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