2013
DOI: 10.1007/s13197-013-1123-7
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Determination of the olive maturity index of intact fruits using image analysis

Abstract: In this work, the maturity index of different samples of olives was objectively assessed by image analysis obtained through machine vision, in which algorithms of color-based segmentation and operators to detect edges were used. This method allows a fast, automatic and objective prediction of olive maturity index. This prediction value was compared to maturity index (MI), generally used by olive oil industry, based on the subjective visual determination of color of fruit skin and flesh. Machine vision was also… Show more

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Cited by 75 publications
(54 citation statements)
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“…To evaluate the antioxidant activity and phenolic content during ripening, the olives of CdN cultivar were collected from four of the eight Maturity Index (MI) classification groups (0–7) of olives described by Guzman et al [16] (Figure 1). Green olives, characterized by green peel and light green pulp (MI group 0, indicated as Stage 0);Partially green olives with slightly pigmented peel and green pulp (MI group 2, indicated as Stage 2);Olives with purple peel and yellow pulp (MI group 4, indicated as Stage 4);Black coloration of the peel and pulp identified (MI Group 7, indicated as Stage 7)…”
Section: Methodsmentioning
confidence: 99%
“…To evaluate the antioxidant activity and phenolic content during ripening, the olives of CdN cultivar were collected from four of the eight Maturity Index (MI) classification groups (0–7) of olives described by Guzman et al [16] (Figure 1). Green olives, characterized by green peel and light green pulp (MI group 0, indicated as Stage 0);Partially green olives with slightly pigmented peel and green pulp (MI group 2, indicated as Stage 2);Olives with purple peel and yellow pulp (MI group 4, indicated as Stage 4);Black coloration of the peel and pulp identified (MI Group 7, indicated as Stage 7)…”
Section: Methodsmentioning
confidence: 99%
“…The ripening index predicted is in close agreement with the one visually estimated by experts. The evaluation of weight of fruit also provided good results (R 2 = 0.91) 77 Estimation the percentage of dirt (leaves, branches and stones) on a sample of olives for oil production using visible machine vision…”
Section: Computer Visionmentioning
confidence: 94%
“…In 2015, Guzman et al predicted by image analysis the maturity index of different samples of olives, obtaining good performance in the classification of the fruit in batches. They also presented a methodology to calculate the ripening index of each individual olive.…”
Section: Computer Visionmentioning
confidence: 99%
“…The detachment index (DI) was calculated as: DI = DF/FW. Ripening index (RI) was determined according to the method based on color changes of peel and pulp …”
Section: Methodsmentioning
confidence: 99%