Olive classification according to external damage using image analysis

Riquelme, M.T.; Elorza, Pilar Barreiro; Ruiz-Altisent, Margarita; Valero, Constantino

doi:10.1016/j.jfoodeng.2007.12.018

Cited by 106 publications

(48 citation statements)

References 18 publications

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“…We also give the identification results of the width-length measurements of the olives in Table 4. Riquelme et al (2008) stressed that according to the results of color defects and morphological characteristics, they achieved a 75%-97% success rate in their study. Mendoza et al (2006) worked on sRGB, HSV, and L * a * b * color space and they also stress that L * a * b * color space is the best color space for this kind of evaluation.…”

Section: Resultsmentioning

confidence: 93%

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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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Section: Resultsmentioning

confidence: 93%

“…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. Riquelme et al (2008) studied color defects and morphological characteristics of olive cultivars.…”

Section: Introductionmentioning

confidence: 99%

Identification of olive cultivars using image processing techniques

Beyaz¹,

Öztürk²

2016

Turk J Agric For

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“…(Narendra, et al, 2010) have presented the recent development and application of image analysis and computer vision systems in sorting and grading of products in the field of agricultural and food. (Riquelme, et al, 2008) have developed 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.…”

Section: Fig 1: Image Patterns Of Different Agriculture Producementioning

confidence: 99%

Recognition and Classification of Normal and Affected Agriculture Produce using Reduced Color and Texture Features

Pujari¹,

Yakkundimath²,

Byadgi³

2014

IJCA

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In this paper, we present a reduced feature set based approach for recognition and classification of normal and affected agriculture produce types. Color and texture features are extracted from normal and affected image samples of agriculture produce. The color features are reduced from eighteen to eight and texture features are reduced from thirty to five. A classifier based on Back Propagation Neural Network (BPNN) is developed which uses reduced color and texture features to recognize and classify the different normal and affected agricultural produce. A feedback from classifier performance is used in reducing the features. The average classification accuracies using reduced color features are 78.08% and 75.17% for normal and affected agriculture produce type respectively. The average classification accuracies using reduced texture features are 85.53% and 77.43% for normal and affected agriculture produce type respectively. The average classification accuracies have increased to 88.28% and 83.80% for normal and affected agriculture produce type respectively, when the reduced color and texture features are combined. The work finds application in developing a machine vision system in agriculture fields in the area of recognition and classification of agriculture produce.

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“…The external appearance of the fresh fruit harvested along with its size and variety are key determinants in the quality and price received by farmers. Therefore, the presence of defects, such as bruising of the olive skin, is inadmissible in the Spanish processing industry because it could provoke rejection by the consumers (Rejano, 1999;Riquelme et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

“…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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Olive classification according to external damage using image analysis

Cited by 106 publications

References 18 publications

Identification of olive cultivars using image processing techniques

Identification of olive cultivars using image processing techniques

Recognition and Classification of Normal and Affected Agriculture Produce using Reduced Color and Texture Features

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

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