An efficient shape analysis method for shrimp quality evaluation

Lee, Dah-Jye; Xiong, Guangming; Lane, Robert M.; Zhang, Dong

doi:10.1109/icarcv.2012.6485271

Cited by 8 publications

(4 citation statements)

References 14 publications

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“…Shrimp incompleteness is often regarded as a defect in shrimp species. Previous studies (Lee et al 2012;Zhang et al 2014) regarding shrimp completeness mainly depended on highly intelligent image processing technologies. These methods can automatically depict and represent the shape of shrimp and produce sound accuracy for recognition.…”

Section: Introductionmentioning

confidence: 99%

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Identification of Impurities in Fresh Shrimp Using Improved Majority Scheme-Based Classifier

2016

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The efficient removal of impurities from postharvest raw shrimp is beneficial to improve the quality of shrimp products. Single feature combined with single classifier results in poor classification rate. Moreover, accuracy of combined classifiers remains unsatisfactory, especially when sound shrimp is mixed with various defective shrimp and impurities. In this study, an improved majority rule (IMAJ) classifier combination scheme was proposed to address this problem. The accuracy of IMAJ (91.53 %) was compared with six other kinds of classifier combination schemes proposed by Kittler. The schemes include Sum (89.93 %), Product (65.99 %), Max (89.76 %), Min (80 %), Median (88.18 %), and Majority (89.2 %). Comparison results indicate that the combination classifier based on IMAJ rule is superior.

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Section: Introductionmentioning

confidence: 99%

“…Among the criteria for impurity identification (Lee et al 2012;USDC 2011), the image features, such as texture, shape, and invariant moment, are important classification indices. The use of these features to classify a single impurity class via image analysis is a relatively easy task but performs poorly when many more classes are added.…”

Section: Introductionmentioning

confidence: 99%

Identification of Impurities in Fresh Shrimp Using Improved Majority Scheme-Based Classifier

2016

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“…The area of the Shrimp is projected by the camera was used to calculate the weight after calibration. Dah-Jye-Lee at el [12] analyzes two grading criteria that are used for the finding the shrimp quality based on the occurrence of black spots, which is measured as a percentage of total body surface and quality of Shape showing to whole shrimp as well as broken pieces. The indication of the aging shrimp is based o black spots and are measured to be the defects that must be removed.…”

Section: International Journal For Research In Applied Science and Engimentioning

confidence: 99%

Computer Aided Shrimp Disease Diagnosis in Aquaculture

Rao¹

2017

IJRASET

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Abstract-Shrimp aquaculture mainly constitutes major economic processes in the world. Automation of some processes in aquaculture is becoming important. There could be great benefit from automation. Shrimp disease diagnosis is a problem of social and economic interest. This paper presents the latest advancements like the expert system applied for shrimp disease diagnosis and also shows the advances towards the effective tools of diagnosis. It gives the better solution for increasing the degree of automation of shrimp disease diagnosis. This research paper suggests solution that is possible for increasing the automation for the diagnosis of the shrimp disease.

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“…Disfigurement and foreign objects can also be automatically recognized (Poonnoy, Yodkeaw, Sriwai, Umongkol, & Intamoon, 2014). Color in shrimps is highly correlated with abnormalities such as dehydration (Mohebbi, Akbarzadeh-T, Shahidi, Moussavi, & Ghoddusi, 2009), spoilage, or black spots (Lee, Xiong, Lane, & Zhang, 2012). Monitoring changes in color can help identify undesired conditions of shrimps during processing (Namwong, 2002).…”

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confidence: 99%

Automated discrimination of deveined shrimps based on grayscale image parameters

Thanasarn

Chaiprapat

Waiyakan

et al. 2019

J Food Process Engineering

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The “Vein” in a shrimp is its digestive tract filled with grit, sand, and sediments stretching along the back of the abdomen. In most shrimp market forms, presence of vein is highly restricted and limited according to the U.S. standard for imports. This research aims to develop an image‐based approach for detection of improperly deveined shrimps. Two hundred shrimp images were subjected to a sequence of image processing techniques before extracting significant parameters from grayscale images. These parameters include shape measurements and pixel value measurements drawn from an image histogram. In this research, disqualified shrimps were identified by two classification techniques: linear discriminant analysis and support vector machine (SVM). Better than 98% classification accuracy was obtained with the SVM using a polynomial kernel function. The success of this research has filled a void left by past studies to facilitate fully automated shrimp quality inspection. Practical applications Rising wages and labor scarcity are among critical problems to seafood industries, along with low productivity due to ergonomics limitations. Such problems will be even worse in the near future and automated machines are becoming a popular alternative to tackle them. These machines must be driven by an intelligent processing unit capable of handling unavoidable variability naturally found in agricultural products. In most shrimp market forms, presence of veins is highly restricted and limited by the U.S. standards for imports. Deveining always leaves remnants of uncertain length. Employing statistical learning techniques, the approach developed in this study can accurately and automatically discriminate shrimps by acceptability based on the vein. Findings of this research contribute to the development of a fully automated shrimp processing machine, supporting sustainability of the industry by reducing reliance on labor policies and workforce availability.

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An efficient shape analysis method for shrimp quality evaluation

Cited by 8 publications

References 14 publications

Identification of Impurities in Fresh Shrimp Using Improved Majority Scheme-Based Classifier

Identification of Impurities in Fresh Shrimp Using Improved Majority Scheme-Based Classifier

Computer Aided Shrimp Disease Diagnosis in Aquaculture

Automated discrimination of deveined shrimps based on grayscale image parameters

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