Comparison of three algorithms in the classification of table olives by means of computer vision

Diaz, Ricardo; Gil, Libertad Sánchez; Serrano, C.; Blasco, Miguel Lorente; Moltó, Enrique; Blasco, José

doi:10.1016/s0260-8774(03)00191-2

Cited by 96 publications

(49 citation statements)

References 11 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%

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%

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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“…Bari et al (2003) worked on olive characteristics, and they also stress that these features are 90% accurate in identifying olives. Diaz et al (2004) worked on olive classification, and they state that, according to the results, it is possible to classify olives at a rate of 90% based on artificial neural networks. In our research, the results showed that all observed olive cultivars were identified at a P < 0.05 significance level using first analysis of variance, and after that Duncan's test.…”

Section: Resultsmentioning

confidence: 99%

“…Bari et al (2003) concentrated on identifying the characteristics of olives, and they stated that morphological characteristics are important factors for the identification of olives. Diaz et al (2004) worked on the classification of olives based on fruit surface defects in different quality categories. Mendoza et al (2006) examined sRGB, HSV, and L * a * b * color space and they stated that standard colors of fruits and vegetables as measured by a computerized imaging system can be used for the determination of product status.…”

Section: Introductionmentioning

confidence: 99%

Identification of olive cultivars using image processing techniques

Beyaz¹,

Öztürk²

2016

Turk J Agric For

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As a country in an olive production zone, olive (Olea europaea L.) production is of great economic importance for Turkey. Turkey has 88 domestic and 28 foreign cultivars of olive, all of which are located in the Kemalpaşa Production and Research Garden (Olive Gene Bank) at the Olive Research Station in İzmir (38°25′34.7628″N, 27°25′22.9872″E). The aim of this study was the identification of olive cultivars using image processing techniques. For this aim, images of olives, taken at 2896 × 1944 pixel and 300 dpi resolution, were captured using a DSLR camera, and evaluations of pixels were used for considering the pixel distribution and dimension measurements. For this aim, MATLAB v2012 and Image j software were used. In the light of the obtained data, analysis of variance and Duncan's test were used to characterize the olive cultivars. As a result, all observed olive cultivars were identified at P < 0.05 significance level. Sarı ulak, Gemlik, Edincik su, Memecik, Eşek zeytini, Ayvalık, Kilis yağlık, Uslu, Çilli, and Domat olive cultivars were identified utilizing the validation process employing image processing and analysis techniques. Only the Erkence cultivar was not identified. Moreover, different classification techniques were applied to the olive stone color value data by the help of SPSS v22 and Clementine v12, which is a data mining software package from SPSS. In addition to the first results the Erkence cultivar was identified 69% with artificial neural networks.

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“…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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Comparison of three algorithms in the classification of table olives by means of computer vision

Cited by 96 publications

References 11 publications

Multispectral images of peach related to firmness and maturity at harvest

Multispectral images of peach related to firmness and maturity at harvest

Identification of olive cultivars using image processing techniques

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

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