RT Journal Article
T1 In-Field Assessment of Olive Fruit Quality Using a Low-Cost Multispectral Sensor and ANN Models
A1 Noguera Manzano, Miguel
A1 Millán Prior, Borja
A1 Aquino Martín, Arturo
A1 Andújar Márquez, José Manuel
AB Optimizing harvest time and oil production requires accurate olive fruit quality characterization. Traditional chemical methods are costly and tedious, leading to poor monitoring resolution and reliance on subjective visual assessments. While spectroscopy offers a non-destructive alternative, standard equipment remains complex and prohibitively expensive for smallholder farmers. To address this, we propose a methodology using a custom-made, low-cost multispectral device. Built upon the AS7265x board, the system acquires 18 spectral bands in the visible and near-infrared range (410–940 nm). We used these spectral data to feed artificial neural network (ANN) models for estimating the quality of intact olives. During a two-season field experiment, we monitored ripening to acquire spectral signatures and ground-truth values for oil content per fresh weight (OCFW), oil content per dry matter (OCDM), moisture (M), and titratable acidity (TA). External validation showed high accuracy for OCFW (R2p = 0.86), OCDM (R2p = 0.86), and M (R2p = 0.89), proving the system’s reliability. However, TA estimation showed lower performance (R2p = 0.21), indicating limited spectral correlation. These findings pave the way for affordable, real-time smart farming tools for olive quality monitoring.
PB MDPI
SN 2073-4395 (electrónico)
YR 2026
FD 2026
LK https://hdl.handle.net/10272/28666
UL https://hdl.handle.net/10272/28666
LA eng
NO Noguera, M., Millán, B., Aquino, A., & Andújar, J. M. (2026). In-Field Assessment of Olive Fruit Quality Using a Low-Cost Multispectral Sensor and ANN Models. Agronomy, 16(12), 1198. https://doi.org/10.3390/agronomy16121198
NO This research was funded by Research Project PID2020-119217RA-I00 funded by MCIN/AEI/10.13039/501100011033; grant IJC2019-040114-I fundedbyMCIN/AEI/10.13039/501100011033; grant CNS2022_136137 funded by MICIU/AEI/10.13039/501100011033 and by “European Union NextGenerationEU/PRTR”; the Interreg Cooperation Program VI-A SPAIN-PORTUGAL (POCTEP) 2021-27 and co-financed with ERDF, grant number 0067_OLIVARIA_5_E, within the scope of the OlivarIA Project.
DS Repositorio Institucional de la Universidad de Huelva
RD 13 jul 2026