Prediction of Oyster Volume and Weight Using Machine Vision

Damar, Sibel; Student, Y. Yagiz Graduate; Balaban, Murat Ö.; Ural, Saim; Oliveira, Alexandra; Crapo, Charles A.

doi:10.1300/j030v15n04_02

Cited by 19 publications

(17 citation statements)

References 9 publications

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“…(1) the weights of the sea cucumbers in air and in distilled water, and the water density of the distilled water. The water density in this study was considered as 1, because the density of pure water is 0.997 g/cm 3 at room temperature (25 °C) [10]. Fig.…”

Section: Measurement Of Actual Volume and Weightmentioning

confidence: 99%

“…Lee et al (2001) performed three-dimensional measurements and restructured a two-dimensional cross-section photography scan into a three-dimensional surface using charge coupled device (CCD) cameras and a multi-line laser [8][9]. In addition, it is possible to calculate the area of an oyster body using the cubic splines method [10] to estimate weight. However, unlike other marine animals, the sea cucumber (Stichopus japonicas) changes its shape into a football-like [11] or globular structure when it shrinks [12]; moreover, its shape is irregular when it is fully extended.…”

Section: Introductionmentioning

confidence: 99%

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Weight Estimation of the Sea Cucumber (Stichopus japonicas) using Vision-based Volume Measurement

Lee¹,

Kim²,

Park³

et al. 2014

Journal of Electrical Engineering and Technology

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-Growth analysis and selection of sea cucumbers (Stichopus japonicas) is typically performed through length or weight measurements. However, because sea cucumbers continuously change shape depending on the external environment, weight measurement has been the preferred approach. Weight measurements require extensive time and labor, moreover it is often difficult to accurately weigh sea cucumbers because of their wet surface. The present study measured sea cucumber features, including the body length, width, and thickness, by using a vision system and regression analysis to generate R 2 values that were used to develop a weight estimation algorithm. The R 2 value between the actual volume and weight of the sea cucumbers was 0.999, which was relatively high. Evaluation of the performance of this algorithm using cross-validation showed that the root mean square error and worst-case prediction error were 1.434 g and ±5.879 g, respectively. In addition, the present study confirmed that the proposed weight estimation algorithm and single slide rail device for weight measurement can measure weights at approximately 4,500 sea cucumbers per hour.

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Section: Measurement Of Actual Volume and Weightmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Weight Estimation of the Sea Cucumber (Stichopus japonicas) using Vision-based Volume Measurement

Lee¹,

Kim²,

Park³

et al. 2014

Journal of Electrical Engineering and Technology

View full text Add to dashboard Cite

show abstract

“…View area obtained from image analysis has been correlated to weight of shrimp (Luzuriaga et al, 1997), salmon , trout (Gümüş and Balaban, 2010), and oysters (Damar et al, 2006). In the estimation of volume of non-homogeneous foods of irregular shape, such as oysters, a method based on cubic splines was developed (Damar et al, 2006).…”

Section: Introductionmentioning

confidence: 99%

“…In the estimation of volume of non-homogeneous foods of irregular shape, such as oysters, a method based on cubic splines was developed (Damar et al, 2006). Good correlations between predicted and measured volumes were found for oysters from different regions.…”

Section: Introductionmentioning

confidence: 99%

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Determination of Volume of Alaska Pollock (Theragra chalcogramma) by Image Analysis

Balaban

Chombeau²,

Gümüş

et al. 2011

Journal of Aquatic Food Product Technology

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Quality Assessment of Aquatic Foods by Machine Vision, Electronic Nose, and Electronic Tongue

Korel

Balaban

2010

Handbook of Seafood Quality, Safety and Health Applications

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The increase in demand for seafood products has catalyzed the desire for higher standards regarding safety and quality issues. Since seafoods are perishable, freshness is a major quality parameter to be considered [1,2]. There is no unique freshness or spoilage indicator for seafood, therefore combinations of selected indicators need to be used to evaluate freshness [3,4]. An important and widely used method to determine freshness is sensory evaluation [5]. The Quality Index Method (QIM) uses a demerit point scoring system [6] based on the evaluation of the important sensory attributes (odour, texture, and appearance) of fish and other aquatic foods. The sensory quality is expressed by the sum of the demerit points, and a linear correlation between these points and the storage time is used to predict the freshness of the target seafood [5,7,8]. The QIM has been developed for various seafood species and products, such as Atlantic mackerel (Scomber scombrus), horse mackerel (Trachurus trachurus), European sardine (Sardina pilchardus) [9], gilthead seabream (Sparus aurata)[10], farmed Atlantic salmon (Salmo salar) [11,12], and cod (Gadus morhua) [13], etc. Even though QIM is fast and reliable in determining the freshness of seafood, it still requires experts to evaluate the quality attributes. Alternatively, appearance, odour, and taste can be measured by machine vision system (MVS), electronic nose (e-nose), and electronic tongue (e-tongue), respectively.In this chapter, the measurement of visual, odour, and taste quality of seafood using MVS, e-nose, and e-tongue is discussed. A few literature examples are given for all techniques, some of which are given from research conducted in our own laboratories. Visual qualityVisual quality of seafood includes appearance (size, shape, and colour) attributes. These have a direct influence on the seafood's value and acceptance. One of the methods of measuring them is by using a MVS, which consists of a digital camera to acquire images, an illumination system (e.g. a light box with fluorescent bulbs as lighting source), and computer software to analyze the image [14,15]. This is a rapid, objective, repeatable, and non-destructive method, Handbook of Seafood Q uality, Safety and Health ApplicationsEdited by Cesarettin Alasalvar, Fereidoon Shahidi, Kazuo Miyashita and Udaya Wanasundara

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Prediction of Oyster Volume and Weight Using Machine Vision

Cited by 19 publications

References 9 publications

Weight Estimation of the Sea Cucumber (Stichopus japonicas) using Vision-based Volume Measurement

Weight Estimation of the Sea Cucumber (Stichopus japonicas) using Vision-based Volume Measurement

Determination of Volume of Alaska Pollock (Theragra chalcogramma) by Image Analysis

Quality Assessment of Aquatic Foods by Machine Vision, Electronic Nose, and Electronic Tongue

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