Classification and Quality Evaluation of Table Olives

Diaz, Ricardo

doi:10.1016/b978-0-12-802232-0.00014-1

Cited by 5 publications

(2 citation statements)

References 18 publications

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“…Se ha preparado un aplicación en Qt/OpenCV [6,7], que permite analizar el contenido presente en cada cangilón de la cadena (cangilón vacío, aceituna bien posicionada, barcos beatas, trozos de aceituna, múltiples aceitunas), generándose un log cada 1000 aceitunas con los valores de cada clase obtenidos. Asimismo permite seleccionar el ángulo a partir del cual se considera que la aceituna está mal posicionada (barco/beata).…”

Section: Software De Visión Artificialunclassified

Caracterización del porcentaje de aceitunas mal posicionadas en máquinas deshuesadoras, rodajadoras y relleno de aceitunas de mesa (DRR)

Pascual¹,

Luna²,

Lázaro³

et al. 2019

Congreso Ibérico De Agroingeniería

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Section: Software De Visión Artificialunclassified

Caracterización del porcentaje de aceitunas mal posicionadas en máquinas deshuesadoras, rodajadoras y relleno de aceitunas de mesa (DRR)

Pascual¹,

Luna²,

Lázaro³

et al. 2019

Congreso Ibérico De Agroingeniería

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“…For instance, the authors of [19] designed a system to classify olives into four categories, while also analyzing the effectiveness of various algorithms [20]. The fruit quality was evaluated and classified in [21,22] by establishing the external appearance of the skin as a determining factor for fruit quality. Some authors pointed to the use of infrared light for this purpose [23] or to estimate the ripening stage of olive lots [24].…”

Section: Introductionmentioning

confidence: 99%

Use of Artificial Vision during the Lye Treatment of Sevillian-Style Green Olives to Determine the Optimal Time for Terminating the Cooking Process

Gordillo

Luna

et al. 2023

Foods

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This study focuses on characterizing the temporal evolution of the surface affected by industrial treatment with NaOH within the processing tanks during the lye treatment stage of Manzanilla table olives. The lye treatment process is affected by multiple variables, such as ambient temperature, the initial temperature of the olives before lye treatment, the temperature of the NaOH solution, the concentration of the solution, the variety of olives, and their size, which are determinants of the speed of the lye treatment process. Traditionally, an expert, relaying on their subjective judgement, manages the cooking process empirically, leading to variability in the termination timing of the cook. In this study, we introduce a system that, by using an artificial vision system, allows us to know in a deterministic way the percentage of lye treatment achieved at each moment along the cooking process; furthermore, with an interpolator that accumulates values during the lye treatment, it is possible to anticipate the completion of the cooking by indicating the moment when two-thirds, three-fourths, or some other value of the interior surface will be reached with an error of less than 10% relative to the optimal moment. Knowing this moment is crucial for proper processing, as it will affect subsequent stages of the manufacturing process and the quality of the final product.

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