Show simple item record

dc.contributor.authorNoguera, Miguel
dc.contributor.authorMillán Prior, Borja 
dc.contributor.authorAndújar Márquez, José Manuel
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.
dc.identifier.issn2077-0472 (electrónico)
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.relation.isversionofPublisher’s version
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.subject.otherPrecision farminges_ES
dc.subject.otherMachine learninges_ES
dc.subject.otherArtificial neural networkes_ES
dc.titleNew, Low-Cost, Hand-Held Multispectral Device for In-Field Fruit-Ripening Assessmentes_ES
dc.subject.unesco33 Ciencias Tecnológicases_ES

Files in this item

This item appears in the following Collection(s)

Show simple item record

Atribución-NoComercial-SinDerivadas 3.0 España
Except where otherwise noted, this item's license is described as Atribución-NoComercial-SinDerivadas 3.0 España

Copyright © 2008-2010. ARIAS MONTANO. Repositorio Institucional de la Universidad de Huelva
Contact Us | Send Feedback |