Fish freshness rapid detection based on fish-eye image

Wang, Feng; Zang, Yue; Wo, Qiqi; Zou, Chen; Wang, Nan; Wang, Xiaobo; Li, Dadong

doi:10.1117/12.2019634

Cited by 8 publications

(5 citation statements)

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“…Some studies have been conducted on the use of image recognition for monitoring seafood quality (Muhamad at al., 2009;Wang et al, 2013;Duta et al, 2016). Of these, Muhamad at al.…”

Section: Image Recognitionmentioning

confidence: 99%

“…Of these, Muhamad at al. (2009) proposed a fuzzy logic-based method for classifying the freshness of fish whilst Wang et al (2013) suggested a regression-based technique on depending on the eye from samples of fish. Fuzzy logic technology was used in a 2009 study by Muhamad and colleagues to classify fish freshness based on image processing.…”

Section: Image Recognitionmentioning

confidence: 99%

See 1 more Smart Citation

Reduction of Losses and Wastage in Seafoods: The Role of Smart Tools and Biosensors Based on Artificial Intelligence

Ayısı Larbı,

Osei

2024

Journal of AI

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This paper reviews current knowledge on the role of smart tools and biosensors based on artificial intelligence in reducing seafood loss and wastage. This study shows that a variety of biosensors, categorised according to how they function, can be used to measure the quality of seafood. These include optical biosensors, enzyme-based biosensors, immunosensors, microbial biosensors, DNA-based biosensors, electrochemical biosensors, optical biosensors, tissue-based biosensors, and piezoelectric biosensors. Among these biosensors, optical biosensors, electrochemical biosensors, and mechanical biosensors are the most significant. Again, this study report that, for seafood traceability and management, a variety of smart solutions including blockchain technology, quick response (QR) codes, data analytics, digital twins, and radio frequency identification (RFID) tags can be utilised. Catch data, vessel tracking data, and data from the processing plant are some of the different data sources that can be utilised to trace seafood products. Artificial intelligence tools like neural networks, deep learning, machine learning, and others can be used to forecast and improve seafood quality. It is crucial to study the development of biosensors that can properly identify the earliest signs of seafood contamination or rotting.

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“…Some studies have been conducted on the use of image recognition for monitoring seafood quality (Muhamad at al., 2009;Wang et al, 2013;Duta et al, 2016). Of these, Muhamad at al.…”

Section: Image Recognitionmentioning

confidence: 99%

Section: Image Recognitionmentioning

confidence: 99%

Reduction of Losses and Wastage in Seafoods: The Role of Smart Tools and Biosensors Based on Artificial Intelligence

Ayısı Larbı,

Osei

2024

Journal of AI

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show abstract

“…Image analysis is a non-destructive, non-hazardous frequent tool for assessment of data based on images and analysis of its colour variations throughout imaging software can be an key method to classify the quality of fish [17], some in food production sectors using image processing [18], other used fuzzy logic based method for classifications of fish freshness [19].…”

Section: Introductionmentioning

confidence: 99%

Freshness Classification of Milkfish (Chanos chanos) using Wavelet Transformation in Image Extraction

Diamante¹

2020

IJATCSE

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It is important to assess a fish if it is fresh or not fresh because a not fresh fish can harm the human's health. Milkfish (Chanos chanos) is the most familiar fish in a market today. In present generations many sensor technologies have been created or develop to answer the drawbacks of conventional methods for freshness classification of fish. Most of these methods are still in the stage of laboratory research and need further improvements and exploration researches for practical applications. There are several techniques used for conversion of image and one of them is the image processing. It is automatic, efficient, and non-destructive method for segmentation of tissues and monitoring the freshness of the fish. This study, generally aimed to classify milkfish (chanos chanos) freshness into fresh and not fresh based on Trained Cascade Model and Coiflet Wavelet Filter and Support Vector Machine.The eyes, gills, and body tissues of milkfish (chanos chanos) are segmented using clustering based method and its feature are strategically extracted in the wavelet transformation domain using Coiflet wavelet filter. Six scaling was used and wavelet function coefficients so improved in pixel averaging and differencing lead to a smoother wavelet and increased capability in several imageprocessing techniques. The study showed the accuracy results of 85.407% in Region of Interest Detection and 98% in Confusion Matrix for classification. The study is considered as significant to fish vendor, fish grower and fish consumers. Generally, study showed that the method used had improved results in freshness classifications.

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“…; Pérez‐Esteve et al ., ; Wang et al . ) or combination of the above in multisensors (Olafsdottir et al . ).…”

Section: Introductionmentioning

confidence: 99%

“…Both approaches have certain cons. Sensory evaluation of freshness requires expertise and also inevitably involves human subjectivity, whereas physi- (Gil et al 2007; Barat et al 2008;Mierke-Klemeyer et al 2008;Ruiz-Rico et al 2013), gas sensors measuring changes of volatile substances altering with freshness reduction (Jun et al 2009a,b;Berna 2010), other chemical multisensors (Thandavan et al 2013;Zaragozá et al 2013), imaging of optical freshness-related changes (Dowlati et al 2013;Kimiya et al, 2013;Liu et al 2013;Pérez-Esteve et al, 2014;Wang et al 2013) or combination of the above in multisensors (Olafsdottir et al 2004). The low sensitivity during the early shelf life is the weak point for many of these applications, as commented in Dimogianopoulos and Grigorakis (2011).…”

Section: Introductionmentioning

confidence: 99%

Nondestructive Textural Assessment of Fish Freshness: A Stochastic Model‐Based Approach Robust to Fish Size Variations

Dimogianopoulos

Grigorakis

2014

Journal of Texture Studies

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This paper aims at proposing a nondestructive scheme for assessing freshness of whole raw fish of various sizes/weights, using a stochastic model-based fault diagnosis framework. Unlike most alternatives, the scheme detects early postmortem texture alterations linked to freshness reductions, thus facilitating the accurate estimation of high-quality shelf life of fish. The method involves the experimental testing of fish samples via vibration-like procedures, and the evaluation of differences between a sample of unknown freshness and a whole group of fresh fish of possibly different sizes/weights. Freshness degree is reliably concluded via statistical decision-making hypothesis tests, which also quantify the uncertainty of the process. Eleven whole raw sea bass (Dicentrarchus labrax) of various sizes/weights and freshness degrees were tested, with freshness results correctly assessed as confirmed by organoleptic freshness evaluation. PRACTICAL APPLICATIONSThe proposed diagnostic scheme introduces a practical, nondestructive method for assessing fish freshness by evaluating texture alterations based on a stochastic model-based fault diagnosis framework. The scheme in its current stage of development detects differences between a sample of unknown freshness and a group of fresh fish, without requiring similar sizes/weights of the tested fish samples. Freshness degree is reliably concluded via statistical decision-making hypothesis tests, which also quantify the uncertainty of the process. These characteristics make it ideal for assessing freshness in fisheries and aquaculture industry, with tested fish samples of unknown freshness being compared with a (potentially expanding) database of "benchmark" fresh fish.

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Fish freshness rapid detection based on fish-eye image

Cited by 8 publications

References 0 publications

Reduction of Losses and Wastage in Seafoods: The Role of Smart Tools and Biosensors Based on Artificial Intelligence

Reduction of Losses and Wastage in Seafoods: The Role of Smart Tools and Biosensors Based on Artificial Intelligence

Freshness Classification of Milkfish (Chanos chanos) using Wavelet Transformation in Image Extraction

Nondestructive Textural Assessment of Fish Freshness: A Stochastic Model‐Based Approach Robust to Fish Size Variations

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