Robust classification approach for segmentation of blood defects in cod fillets based on deep convolutional neural networks and support vector machines and calculation of gripper vectors for robotic processing

Misimi, Ekrem; Øye, Elling Ruud; Sture, Øystein; Mathiassen, John Reidar

doi:10.1016/j.compag.2017.05.021

Cited by 17 publications

(6 citation statements)

References 41 publications

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“…(2018) Grading ACO-BPANN RMSEp = 6.3834 mg/100g, R = 0.7542 Khulal et al. (2016) Cod Fillets Grading SVM, CNN accuracy = 100% with CNN Misimi et al. (2017) Fish Grading PCA, BP-ANN accuracy = 93.33% Huang et al.…”

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

184

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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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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

184

View full text Add to dashboard Cite

show abstract

“…Statistical and machine learning-based data mining methods have been successfully implemented in the development of an improved decision system of aquaculture in the past studies, e.g., fuzzy inference system in the modeling of soil microbial dynamics [18]; optimum-path forest, support vector machine (SVM), Bayes classifier, multilayer perceptron (MLP), and self-organizing maps to control the aquatic weeds (maximum accuracy of 93.27 ± 0.91%) [19]; the decision tree-based ensemble approach (maximum accuracy of 75%), SVM, Bayes network, MLP, and radial basis function in the prediction of shellfish farm closure [20]; time-series classification in the prediction of shellfish farm closure [21]; SVM in the fish species classification [22]; and quadratic classifier and SVM in the classification of feeding and fasted fish (maximum accuracy of 86.3 ± 0.296%) [23], etc. Besides, in some recent studies, artificial bee colony-water temperature mechanism algorithm in the prediction of the temperature of prawn [24], particle swarm optimization to determine the optimal production strategies of fish [25], a neuro-fuzzy method in the feeding system of fish (accuracy of 98%) [26], an ensemble of wrappers in the fish age classification [27], convolutional neural network (CNN) and SVM methods in the classification of normal vs. defective fillets [28], CNN and MLP in the prediction of dissolved oxygen of aquaculture systems [29], and feature extraction algorithms in the automatic tuna sizing [30], etc., have been implemented.…”

Section: Literature Review and Objective Of The Present Studymentioning

confidence: 99%

“…From the literature survey, it is obvious that several statistical and machine learning-based data mining methods have been used in the automation of aquaculture and fisheries for different applications. Amongst them, SVM is the most widely used and successful method [19,20,22,23,28,[31][32][33]. Though, the application of data mining methods specifically the SVM, and its combination with some feature extraction methods in monitoring the growth of the Ohrid trout haven't explored yet.…”

Section: Literature Review and Objective Of The Present Studymentioning

confidence: 99%

A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout (Salmo Letnica)

Jha¹,

Uzunov²,

Zhang³

2021

Computer Modeling in Engineering &Amp; Sciences

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Ohrid trout (Salamo letnica) is an endemic species of fish found in Lake Ohrid in the Former Yugoslav Republic of Macedonia (FYROM). The growth of Ohrid trout was examined in a controlled environment for a certain period, thereafter released into the lake to grow their natural population. The external features of the fish were measured regularly during the cultivation period in the laboratory to monitor their growth. The data mining methods-based computational model can be used for fast, accurate, reliable, automatic, and improved growth monitoring procedures and classification of Ohrid trout. With this motivation, a combined approach of principal component analysis (PCA) and support vector machine (SVM) has been implemented for the visual discrimination and quantitative classification of Ohrid trout of the experimental and natural breeding and their growth stages. The PCA results in better discrimination of breeding categories of Ohrid trout at different development phases while the maximum classification accuracy of 98.33% was achieved using the combination of PCA and SVM. The classification performance of the combination of PCA and SVM has been compared to combinations of PCA and other classification methods (multilayer perceptron, naïve Bayes, random committee, decision stump, random forest, and random tree). Besides, the classification accuracy of multilayer perceptron using the original features has been studied.

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“…Recently, the field of deep learning has seen adoption in aquaculture. The use of deep neural networks has led to applications ranging from fish identification [13] and fish feeding behavior [12] to segmentation of blood defects in cod fillets [16]. Deep Neural Networks are highly parameterized statistical models used in combination with learning algorithms to approximate the functions underlying the data used to train the model.…”

Section: Introductionmentioning

confidence: 99%

EchoBERT: A Transformer-Based Approach for Behavior Detection in Echograms

Måløy

2020

IEEE Access

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Robust classification approach for segmentation of blood defects in cod fillets based on deep convolutional neural networks and support vector machines and calculation of gripper vectors for robotic processing

Cited by 17 publications

References 41 publications

Deep learning and machine vision for food processing: A survey

Deep learning and machine vision for food processing: A survey

A Combined Approach of Principal Component Analysis and Support Vector Machine for Early Development Phase Modeling of Ohrid Trout (Salmo Letnica)

EchoBERT: A Transformer-Based Approach for Behavior Detection in Echograms

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