Classification of Boiled Shrimp's Shape Using Image Analysis and Artificial Neural Network Model

Poonnoy, Poonpat; Yodkeaw, Panupong; Sriwai, Akkarin; Umongkol, Pongpol; Intamoon, Saowanit

doi:10.1111/jfpe.12081

Cited by 17 publications

(9 citation statements)

References 10 publications

(14 reference statements)

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“…However, the human-based vision methods are becoming less attractive due to their high costs, low speeds, requiring experienced staffs for grading of the product and low accuracies. In recent years, the application of advanced techniques based on CV for grading different agricultural products due to its high accuracy, low cost and high speed has become more widespread (Du and Sun 2004;Cakmak and Boyaci 2011;Kumar-Patel et al 2012;Poonnoy et al 2014).…”

Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network‐Based Method to Identify Five Classes of Almond According to Visual Features

Teimouri

Omid

Mollazade

et al. 2015

J Food Process Engineering

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The quality evaluation is one of the key factors that have a major impact on the final price of agricultural products. Nowadays, image processing-based techniques are becoming as an acceptable and widespread in quality evaluation procedures. In this study, we develop a robust method based on image processing and computational intelligence for quality grading and classification of almonds. The images of five classes of almond including normal almond (NA), broken almond (BA), double almond (DA), wrinkled almond (WA) and shell of almond (SA) were acquired by a scanner. For segmentation of images, both H component in HSI color space and Otsu's thresholding method were applied. In the next step, the feature vector, which includes 8 shape features, 45 color features and 162 texture features, was composed. For choosing correlated and superior features among all the 215 extracted features, sensitivity analysis was applied. Principal component analysis method was also used to reduce the dimension of the feature vector. The classification of almonds into different classes was carried out by artificial neural networks (ANNs). Among different ANN structures, the 18-7-7-5 topology was the most optimum classifier. The accuracy of ANN classifier for each class was 98.92% for NA, 99.46% for BA, 98.38% for DA, 98.92% for WA and 100% for SA. The technique can readily be extended for online sorting machines. PRACTICAL APPLICATIONSOne of the applications of this method is in the design and fabrication of realtime grading and sorting machines. The biggest advantage of the presented algorithm is its high precision. The developed classifier is able to detect and eject defected almonds (broken, double, wrinkled and shell of almonds) out of a stream of almonds in the sorting process line. Therefore, if the processing time of the method is improved further, it can readily be used in an online sorting machine.

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Section: Introductionmentioning

confidence: 99%

An Artificial Neural Network‐Based Method to Identify Five Classes of Almond According to Visual Features

Teimouri

Omid

Mollazade

et al. 2015

J Food Process Engineering

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“…Currently, ANN is a valid way to fit the mathematical relationships, especially for multiple-input and multiple-output systems (Bhadeshia, 1999), which are different from the conventional material-behavior-evaluation techniques (Mandal et al, 2009; Das et al, 2013). Furthermore, many quantitative mathematical models are established using the combination of both image analysis and ANN, with which the problems presented in the industrial field have been successfully resolved (Buessler et al, 2014; Poonnoy et al, 2014; Samtas, 2014).…”

Section: Methodsmentioning

confidence: 99%

Automatic Detection of Pearlite Spheroidization Grade of Steel Using Optical Metallography

Chen

Ren

et al. 2016

Microsc Microanal

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To eliminate the effect of subjective factors during manually determining the pearlite spheroidization grade of steel by analysis of optical metallography images, a novel method combining image mining and artificial neural networks (ANN) is proposed. The four co-occurrence matrices of angular second moment, contrast, correlation, and entropy are adopted to objectively characterize the images. ANN is employed to establish a mathematical model between the four co-occurrence matrices and the corresponding spheroidization grade. Three materials used in coal-fired power plants (ASTM A315-B steel, ASTM A335-P12 steel, and ASTM A355-P11 steel) were selected as the samples to test the validity of our proposed method. The results indicate that the accuracies of the calculated spheroidization grades reach 99.05, 95.46, and 93.63%, respectively. Hence, our newly proposed method is adequate for automatically detecting the pearlite spheroidization grade of steel using optical metallography.

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“… Pan et al., 2016a , Pan et al., 2016b utilized a technique based on the hyperspectral imaging method to detect cold injury of peach, and utilized ANN to predict quality parameters. Poonnoy (2014) adopted ANN to classify the shapes of boiled shrimps based on the Relative Internal Distance (RID) values. The four shapes considered included ‘regular’, ‘no tails’, ‘one tail’, and ‘broken body’, and the RIDs were calculated by segmenting the shrimp images and drawing the co-related lines on the segmented contour.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Deep learning and machine vision for food processing: A survey

Zhu

Spachos

Pensini

et al. 2021

Current Research in Food Science

156

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The quality and safety of food is an important issue to the whole society, since it is at the basis of human health, social development and stability. Ensuring food quality and safety is a complex process, and all stages of food processing must be considered, from cultivating, harvesting and storage to preparation and consumption. However, these processes are often labour-intensive. Nowadays, the development of machine vision can greatly assist researchers and industries in improving the efficiency of food processing. As a result, machine vision has been widely used in all aspects of food processing. At the same time, image processing is an important component of machine vision. Image processing can take advantage of machine learning and deep learning models to effectively identify the type and quality of food. Subsequently, follow-up design in the machine vision system can address tasks such as food grading, detecting locations of defective spots or foreign objects, and removing impurities. In this paper, we provide an overview on the traditional machine learning and deep learning methods, as well as the machine vision techniques that can be applied to the field of food processing. We present the current approaches and challenges, and the future trends.

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Classification of Boiled Shrimp's Shape Using Image Analysis and Artificial Neural Network Model

Cited by 17 publications

References 10 publications

An Artificial Neural Network‐Based Method to Identify Five Classes of Almond According to Visual Features

An Artificial Neural Network‐Based Method to Identify Five Classes of Almond According to Visual Features

Automatic Detection of Pearlite Spheroidization Grade of Steel Using Optical Metallography

Deep learning and machine vision for food processing: A survey

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