The application of a fast algorithm for the classification of olives by machine vision

Diaz, Ricardo; Faus, G; Blasco, Miguel Lorente; Blasco, José; Moltó, Enrique

doi:10.1016/s0963-9969(00)00041-7

Cited by 57 publications

(28 citation statements)

References 9 publications

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“…Multispectral imaging can be used to address external features such as ripening (Lu, 2004) and external defects (Diaz et al, 2000(Diaz et al, ,2004Leemans and Destain, 2004;Kleynen et al, 2003;Mehl et al, 2004;Tao and Wen, 1999;Singh and Delwiche, 1994;Unay and Gosselin, 2006) with higher sensitivity in comparison to the ordinary RGB imaging (Gomez-Sanchis et al, 2008;Aleixos et al, 2002Aleixos et al, , 2007Leemans et al, 2002;Kleynen et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

“…Du and Sun (2006) presented the recent advances in learning techniques for food quality evaluation based on computer vision. In the case of fruits, the statistics learning is employed for segmentation, feature extraction and defects classification: Bayesian classification process, Bayesian and linear discriminant analysis together with Mahalanobis distance classifiers (Kleynen et al, 2003;Diaz et al, 2000Diaz et al, , 2004.…”

Section: Introductionmentioning

confidence: 99%

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Multispectral images of peach related to firmness and maturity at harvest

Lleó

Elorza

Ruiz-Altisent

et al. 2009

Journal of Food Engineering

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Keywords:Fruit Multispectral Vision Ripeness Firmness Maturity Non-destructive Quality Visible spectra Chlorophyll Peach Two multispectral maturity classifications for red soft-flesh peaches ('Kingcrest', 'Rubyrich' and 'Richlady' n = 260) are proposed and compared based on R (red) and R/IR (red divided by infrared) images obtained with a three CCD camera (800 nm, 675 nm and 450 nm). R/IR histograms were able to correct the effect of 3D shape on light reflectance and thus more Gaussian histograms were produced than R images. As fruits ripened, the R/IR histograms showed increasing levels of intensity. Reference measurements such as firmness and visible spectra also varied significantly as the fruit ripens, firmness decreased while reflectance at 680 nm increased (chlorophyll absorption peak).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multispectral images of peach related to firmness and maturity at harvest

Lleó

Elorza

Ruiz-Altisent

et al. 2009

Journal of Food Engineering

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“…A good fit (R 2 = 0.952) was obtained between the values of bruise damage obtained in laboratory by visual estimation and the bruise index obtained in field conditions by image analysis. Moreover, as suggested by other authors (Tao et al, 1995;Díaz et al, 2000), image analysis is faster, more consistent, efficient, cheaper, and accurate and presents fewer errors than visual assessment, which depends largely on the human factor. Fig.…”

Section: Resultsmentioning

confidence: 82%

“…However, table olive studies have been more focused on industry conditions than on the stages of harvesting and transportation to the industry. These studies have focused primarily on the detection of defects presented in olive fruits and their classification into categories using machine vision systems (Díaz et al, 2000(Díaz et al, , 2004Riquelme et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Isolation of table olive damage causes and bruise time evolution during fruit detachment with trunk shaker

Jiménez-Jiménez¹,

García²,

Blanco-Roldán³

et al. 2013

Span. j. agric. res.

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The high sensitivity of table olives to mechanical damage limits mechanical harvesting with trunk shakers. The objective of this study was the identification, evaluation and temporal evolution assessment of the sources of damage caused to the fruits. To do this, digital image analysis was used for the objective determination of damage produced to table olives. Harvesting tests were performed in an intensive olive orchard with trees of the ‘Manzanilla’ variety in Seville, Spain. Mechanical harvesting with trunk shakers and subsequent detachment of the fruits to the ground produced a level of bruise 12 times greater than the levels obtained from manual harvesting. Fruit-fruit and fruit-branch impacts and friction from the movement of the fruit in the tree canopy during vibration and detachment were the main causes of damage to the fruits. These causes represented a mean value of 60% of the damage produced to the fruits from mechanical harvesting. In addition, most bruising from mechanical damage occurred in the first hour after harvesting and followed an exponential tendency. The information obtained about table olive damage causes and bruise time evolution during fruit detachment with trunk shaker can be used by the producers to determine how to reduce and prevent bruising during harvesting operations.

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“…Only recently have some research institutes and manufactures been looking for techniques allowing for automatic classification. Such research has worked towards the development of prototype devices for fruit classification based either on artificial vision (Diaz et al, 2000(Diaz et al, , 2004Mateos et al, 2005) or on sorting techniques like fruit rebound (Barreiro et al, 2003).…”

Section: Introductionmentioning

confidence: 99%

Olive classification according to external damage using image analysis

Riquelme

Elorza

Ruiz-Altisent

et al. 2008

Journal of Food Engineering

106

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The external appearance of an olive's skin is the most decisive factor in determining its quality as a fruit. This work tries to establish a hierarchical model based on the features extracted from images of olives reflecting their external defects. Seven commercial categories of olives, established by product experts, were used: undamaged olives, mussel-scale or 'serpeta', hail-damaged or 'granizo', mill or 'rehus', wrinkled olive or 'agostado', purple olive and undefined-damage or 'molestado'. The original images were processed using segmentation, colour parameters and morphological features of the defects and the whole fruits. The application of three consecutive discriminant analyses resulted in the correct classification of 97% and 75% of olives during calibration and validation, respectively. However the correct classification percentages vary greatly depending on the categories, ranging 80-100% during calibration and 38-100% during validation.

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The application of a fast algorithm for the classification of olives by machine vision

Cited by 57 publications

References 9 publications

Multispectral images of peach related to firmness and maturity at harvest

Multispectral images of peach related to firmness and maturity at harvest

Isolation of table olive damage causes and bruise time evolution during fruit detachment with trunk shaker

Olive classification according to external damage using image analysis

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