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Sensors 2018, 18(9), 2930;

Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling

University of Huelva, Department of Electronic Engineering, Computer Systems and Automation, La Rábida, Palos de la Frontera, 21819 Huelva, Spain
Author to whom correspondence should be addressed.
Received: 30 July 2018 / Revised: 27 August 2018 / Accepted: 31 August 2018 / Published: 3 September 2018
(This article belongs to the Special Issue Sensors in Agriculture 2018)
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This paper presents a new methodology for the estimation of olive-fruit mass and size, 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 developed for 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 the segmentations to objective reference measurement. The performance of the models was evaluated on external validation sets, giving relative errors of 0.86% for the major axis, 0.09% for the minor axis and 0.78% for mass in the case of the Arbequina variety; analogously, relative errors of 0.03%, 0.29% and 2.39% were annotated for Picual. Additionally, global feature estimation models, applicable to both varieties, were also tried, providing comparable or even better performance than the variety-specific ones. Attending to the achieved accuracy, it can be concluded that the proposed method represents a first step in the development of a low-cost, automated and non-invasive system for olive-fruit characterization in industrial processing chains. View Full-Text
Keywords: olive; food industry; fruit grading; image analysis; segmentation olive; food industry; fruit grading; image analysis; segmentation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Ponce, J.M.; Aquino, A.; Millán, B.; Andújar, J.M. Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling. Sensors 2018, 18, 2930.

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