RT Journal Article
T1 Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling
A1 Ponce Real, Juan Manuel
A1 Aquino Martín, Arturo
A1 Millán Prior, Borja
A1 Andújar Márquez, José Manuel
AB This paper presents a new methodology for the estimation of olive-fruit mass andsize, characterized by its major and minor axis length, by using image analysis techniques. First,different sets of olives from the varieties Picual and Arbequina were photographed in the laboratory.An original algorithm based on mathematical morphology and statistical thresholding was developedfor segmenting the acquired images. The estimation models for the three targeted features,specifically for each variety, were established by linearly correlating the information extracted from thesegmentations to objective reference measurement. The performance of the models was evaluated onexternal validation sets, giving relative errors of 0.86% for the major axis, 0.09% for the minor axis and0.78% for mass in the case of the Arbequina variety; analogously, relative errors of 0.03%, 0.29% and2.39% were annotated for Picual. Additionally, global feature estimation models, applicable to bothvarieties, were also tried, providing comparable or even better performance than the variety-specificones. Attending to the achieved accuracy, it can be concluded that the proposed method representsa first step in the development of a low-cost, automated and non-invasive system for olive-fruitcharacterization in industrial processing chains.
PB MDPI
SN 1424-8220
YR 2018
FD 2018-09
LK http://hdl.handle.net/10272/16219
UL http://hdl.handle.net/10272/16219
LA eng
NO Ponce Real, J. M., Aquino Martín, A., Millán Prior, B., Andújar Márquez, J. M.  Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling. Sensors, 18(9), 2930. (2018). DOI: https://doi.org/10.3390/s18092930
NO The research and APC were funded by the INTERREG Cooperation Program V-A SPAIN-PORTUGAL(POCTEP) 2014–2020, and co-financed with ERDF funds, grant number 0155_TECNOLIVO_6_E, within the scopeof the TecnOlivo Project.
DS Repositorio Institucional de la Universidad de Huelva
RD 1 jun 2026