Identification of some spanish olive cultivars using image processing techniques

Beyaz, Abdullah; Özkaya, Mücahit Taha; İçen, Duygu

doi:10.1016/j.scienta.2017.06.041

Cited by 29 publications

(17 citation statements)

References 10 publications

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“…Regarding the olive sector, literature focused on the application of machine vision for olive treatment and manufacturing can be consulted. Of special relevance are developments for the classification of fruits according to different characteristics, such as defects on the surface [ 17 ] or the variety [ 18 , 19 ], and fruit detection for feature estimation [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling

Ponce

Aquino

Millán

et al. 2018

Sensors

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This paper presents a new methodology for the estimation of olive-fruit mass and size, characterized by its major and minor axis length, by using image analysis techniques. First, different sets of olives from the varieties Picual and Arbequina were photographed in the laboratory. An original algorithm based on mathematical morphology and statistical thresholding was developed for segmenting the acquired images. The estimation models for the three targeted features, specifically for each variety, were established by linearly correlating the information extracted from the segmentations to objective reference measurement. The performance of the models was evaluated on external validation sets, giving relative errors of 0.86% for the major axis, 0.09% for the minor axis and 0.78% for mass in the case of the Arbequina variety; analogously, relative errors of 0.03%, 0.29% and 2.39% were annotated for Picual. Additionally, global feature estimation models, applicable to both varieties, were also tried, providing comparable or even better performance than the variety-specific ones. Attending to the achieved accuracy, it can be concluded that the proposed method represents a first step in the development of a low-cost, automated and non-invasive system for olive-fruit characterization in industrial processing chains.

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Section: Introductionmentioning

confidence: 99%

Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling

Ponce

Aquino

Millán

et al. 2018

Sensors

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“…In recent years, there are also some product-based classification studies conducted by taking apple, banana, pear, pineapple, grape, melon, watermelon, citrus and avocado fruit into consideration (Zhang and Wu, 2012;Zhang et al, 2014). Variety based studies have been conducted to classify varieties of wheat (Dubey et al, 2006;Marini et al, 2008;Arefi et al, 2011;Pazoki and Pazoki, 2011;Zapotoczny, 2011;Khoshroo et al, 2014;Taner et al, 2015), rice (Liu et al, 2005;Guzman and Perelta, 2008;Silva and Sonnadara, 2013;Pazoki et al, 2014), barley (Zapotoczny, 2012), corn (Chen et al, 2010), bean (Nasirahmadi and Behroozi-Khazaei, 2013), and olive (Beyaz and Öztürk, 2016;Beyaz et al, 2017). Taner et al (2018) conducted both product and variety-based classification and in their study, they conducted with bread wheat, durum wheat, barley, oat and triticale products.…”

Section: Introductionmentioning

confidence: 99%

Chestnut (Castanea sativa Mill.) cultivar classification: an artificial neural network approach

Öztekin

Taner

Duran

2020

Not Bot Horti Agrobo

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The present study investigated the possible use of artificial neural networks (ANN) to classify five chestnut (Castanea sativa Mill.) varieties. For chestnut classification, back-propagation neural networks were framed on the basis of physical and mechanical parameters. Seven physical and mechanical characteristics (geometric mean diameter, sphericity, volume of nut, surface area, shell thickness, shearing force and strength) of chestnut were determined. It was found that these characteristics were statistically different and could be used in the classification of species. In the developed ANN model, the design of the network is 7-(5-6)-1 and it consists of 7 input, 2 hidden and 1 output layers. Tansig transfer functions were used in both hidden layers, while linear transfer functions were used in the output layer. In ANN model, R2 value was obtained as 0.99999 and RMSE value was obtained as 0.000083 for training. For testing, R2 value was found as 0.99999 and RMSE value was found as 0.00031. In the approximation of values obtained with ANN model to the values measured, average error was found as 0.011%. It was found that the results found with ANN model were very compatible with the measured data. It was found that the ANN model obtained can classify chestnut varieties in a fast and reliable way.

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“…Thus, Martínez et al [18] approached the problem by feature extraction from images of olive endocarps, and then using partial least square-discriminant classifiers. Similarly, the proposal by Beyaz et al [19] used captures of fruits and endocarps to identify olive cultivars. Aside from variety classification, research has also been conducted to deal with defective fruit discrimination.…”

Section: Introductionmentioning

confidence: 99%

Automatic Counting and Individual Size and Mass Estimation of Olive-Fruits Through Computer Vision Techniques

et al. 2019

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Fruit grading is an essential post-harvest task in the olive industry, where size-and-mass based fruit classification is especially important when processing high-quality table olives. Within this context, this research presents a new methodology aimed at supporting accurate automatic olive-fruit grading by using computer vision techniques and feature modeling. For its development, a total of 3600 olive-fruits from nine varieties were photographed, stochastically distributing the individuals on the scene, using an ad-hoc designed an imaging chamber. Then, an image analysis algorithm, based on mathematical morphology, was designed to individually segment olives and extract descriptive features to estimate their major and minor axes and their mass. Regarding its accuracy for the individual segmentation of olive-fruits, the algorithm was proven through 117 captures containing 11 606 fruits, producing only six fruit-segmentation mistakes. Furthermore, by linearly correlating the data obtained by image analysis and the corresponding reference measurements, models for estimating the three features were computed. Then, the models were tested on 2700 external validation samples, giving relative errors below 0.80% and 1.05% for the estimation of the major and minor axis length for all varieties, respectively. In the case of estimating olive-fruit mass, the models provided relative errors never exceeding 1.16%. The ability of the developed algorithm to individually segment olives stochastically positioned, along with the low error rates of around 1% reported by the estimation models for the three features, makes the methodology a promising alternative to be integrated into a new generation of improved and non-invasive olive classification machines. The new developed system has been registered in the Spanish Patent and Trademark Office with the number P201930297.INDEX TERMS Computer vision, feature modeling, food industry, fruit grading, image analysis, olive.

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Identification of some spanish olive cultivars using image processing techniques

Cited by 29 publications

References 10 publications

Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling

Olive-Fruit Mass and Size Estimation Using Image Analysis and Feature Modeling

Chestnut (Castanea sativa Mill.) cultivar classification: an artificial neural network approach

Automatic Counting and Individual Size and Mass Estimation of Olive-Fruits Through Computer Vision Techniques

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