dc.contributor.author | Noguera, Miguel | |
dc.contributor.author | Millán Prior, Borja | |
dc.contributor.author | Andújar Márquez, José Manuel | |
dc.date.accessioned | 2023-07-07T10:51:17Z | |
dc.date.available | 2023-07-07T10:51:17Z | |
dc.date.issued | 2022-12 | |
dc.identifier.citation | Noguera, 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/agriculture13010004 | es_ES |
dc.identifier.issn | 2077-0472 (electrónico) | |
dc.identifier.uri | https://hdl.handle.net/10272/22282 | |
dc.description.abstract | The 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.sponsorship | This 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.iso | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.relation.isversionof | Publisher’s version | |
dc.rights | Atribución-NoComercial-SinDerivadas 3.0 España | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ | * |
dc.subject.other | Sensor | es_ES |
dc.subject.other | Multispectral | es_ES |
dc.subject.other | Precision farming | es_ES |
dc.subject.other | Machine learning | es_ES |
dc.subject.other | Artificial neural network | es_ES |
dc.subject.other | AS7265x | es_ES |
dc.title | New, Low-Cost, Hand-Held Multispectral Device for In-Field Fruit-Ripening Assessment | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.identifier.doi | 10.3390/agriculture13010004 | |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.subject.unesco | 33 Ciencias Tecnológicas | es_ES |