RT Journal Article
T1 Methodology for Olive Fruit Quality Assessment by Means of a Low-Cost Multispectral Device
A1 Noguera Manzano, Miguel
A1 Millán Prior, Borja
A1 Aquino Martín, Arturo
A1 Andújar Márquez, José Manuel
AB The standard methods for determining the quality of olives involve chemical methods thatare time-consuming and expensive. These limitations lead growers to homogeneous harvesting basedon subjective criteria such as intuition and visual decisions. In recent times, precision agriculturetechniques for fruit quality assessment, such as spectroscopy, have been introduced. However, theyrequire expensive equipment, which limit their use to olive mills. This work presents a completemethodology based on a new low-cost multispectral sensor for assessing quality parameters ofintact olive fruits. A set of 507 olive samples were analyzed with the proposed device. After datapre-processing, artificial neural network (ANN) models were trained using the 18 reflectance signalsacquired by the sensor as input and three olive quality indicators (moisture, acidity, and fat content)as targets. The responses of the ANN models were promising, reaching coefficient-of-determinationvalues of 0.78, 0.86, and 0.62 for fruit moisture, acidity, and fat content, respectively. These resultsshow the suitability of the proposed device for assessing the quality status of intact olive fruits. Itsperformance, along with its low cost and ease of use, paves the way for the implementation of anolive fruit quality appraisal system that is more affordable for olive growers
PB MDPI
SN 2073-4395 (electrónico)
YR 2022
FD 2022
LK http://hdl.handle.net/10272/20883
UL http://hdl.handle.net/10272/20883
LA eng
NO Noguera, M., Millan, B., Aquino, A., & Andújar, J. M. (2022). Methodology for Olive Fruit Quality Assessment by Means of a Low-Cost Multispectral Device. In Agronomy (Vol. 12, Issue 5, p. 979). MDPI AG. https://doi.org/10.3390/agronomy12050979
NO This work was supported by grant PID2020-119217RA-I00 funded by MCIN/AEI/ 10.13039/501100011033, and grant IJC2019-040114-I funded by MCIN/AEI/ 10.13039/501100011033, and alsoby project TIColiVA with grant P18-RTJ-4539 funded by the Regional Government of Andalusiathrough the “PAIDI, Plan Andaluz de Investigación, Desarrollo e Innovación”.The authors acknowledge Francisco Dominguez Calvo, the Nuestra señora de laoliva manager, for providing the olive samples and reference data on which the study was conducted,as well as Diego Tejada, for his support in the device design
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026