Developing a Machine Vision System Equipped with UV Light to Predict Fish Freshness Based on Fish-Surface Color

Liao, Qiuhong; Wei, Chao; Liu, Ying; Guo, Lin’an; Ouyang, Huaxue

doi:10.4236/fns.2021.123019

Cited by 4 publications

(9 citation statements)

References 27 publications

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“…In particular, it can be used in industrial control and automobile autonomous navigation. A complete machine vision system contains the following five parts: lighting systems, lens, high-speed cameras, image acquisition cards, and vision processors [12,13]. The requirements of each part are shown in Table 1.…”

Section: Machine Vision Sensingmentioning

confidence: 99%

Application of Machine Learning to Badminton Action Decomposition Teaching

Long

Rong

2022

Wireless Communications and Mobile Computing

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The study was aimed at realizing the identification of athletes’ actions in badminton teaching. The teaching process is segmented into many independent actions to help learners standardize their movements in badminton play, improving the national physical quality. First, the principle and advantages of machine vision sensing are introduced. Second, the images and videos about the action decomposition of badminton teaching are collected, and the image data are extracted by Haar-like. Subsequently, badminton players’ actions are recognized and preprocessed, and a dataset is constructed. Furthermore, a new algorithm model is implemented and trained by using Haar-like and Adaptive Boosting (AdaBoost). Finally, the badminton players’ action recognition algorithm is tested and compared with the traditional hidden Markov model (HMM) and support vector machine (SVM). The results show that action images improved by machine vision can process the captured actions effectively, making the computer better identify different badminton teaching actions. The proposed method has a recognition rate of more than 90% for each action, the average recognition accuracy of actions reaches 95%, the average recognition rate of the same person’s actions is 96.5%, and the average recognition rate of different people’s actions is 94.8%. The badminton teaching action recognition model based on Haar-like and AdaBoost can recognize and classify badminton actions and improve the quality of badminton teaching. This study shows that the image processing technology can effectively process the players’ static images, which gives the direction for physical education (PE) under artificial intelligence (AI).

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Section: Machine Vision Sensingmentioning

confidence: 99%

Application of Machine Learning to Badminton Action Decomposition Teaching

Long

Rong

2022

Wireless Communications and Mobile Computing

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“…Machine vision has broad application prospects in different fields: graph optimization processing [1], sensors [2], sound source reconstruction [3], visual prediction [4], and pollution monitoring [5]. In view of a series of problems existing in the process of maize species analysis based on machine vision theory, the authenticity analysis method was introduced to obtain an optimized machine vision analysis model [6].…”

Section: Introductionmentioning

confidence: 99%

Design and Application of Art Education Communication Platform Based on Machine Vision

Chen

Tang

et al. 2022

Security and Communication Networks

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There are some problems in the design and application of the art education exchange platform. In order to further analyze the relevant content of the art education exchange, based on machine vision theory, model calibration, and coordinate change are adopted to modify the original model, so as to obtain the optimized model. This model can be used to analyze the design and application of art education platform. Relevant studies show that, according to the coordinate parameter curve, the parameter α plays a promoting role on the coordinate value, while the parameter β plays a promoting role on the coordinate value first and then plays an inhibiting role. The parameter γ also promotes and inhibits coordinate values. The curve corresponding to parameter M1 shows a gradual decline, and the curve of parameter M2 has little influence on coordinate data. The curve corresponding to parameter M3 also shows a linear trend of change. Through analysis, it can be seen that M3 has the greatest influence on coordinate data. By using machine vision model to design and analyze the communication platform of art education, the changing trend of different indicators can be obtained. It can be seen from the analysis that both language and vocal music have obvious stability in the calculation process, which is an inherent attribute of communication platform. Finally, the accuracy of the optimization model is verified by analyzing and predicting the data. This research can provide theoretical support for the application of machine vision model in other fields.

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“…Fish skin is another important part of fish that can be used to study the fish freshness as it changes its appearance after being caught (Sengar et al., 2018). Several studies have been conducted to develop frameworks based on image processing to determine fish freshness by assessing skin changes (Liao et al., 2021; Sengar et al., 2018).…”

Section: Current Applications Of Artificial Senses For Fish Freshness...mentioning

confidence: 99%

“…The HSV and L * a * b * color spaces have been used to analyze the color features of fish skin, and regression analyses have been used to develop the predictive model. The regression models of a * value and b * value have shown clear prediction results with high correlation coefficients of 0.97 and 0.85, respectively (Liao et al., 2021).…”

Section: Current Applications Of Artificial Senses For Fish Freshness...mentioning

confidence: 99%

“…The all above‐reported studies under applications of computer vision have proven the feasibility of developing computer vision systems based on fish eye, gills, and skin images in freshness determination. More studies have been conducted on the fish eye and gill and a lesser number of studies have been conducted on fish skin (Dutta et al., 2016; Kunjulakshmi et al., 2020; Liao et al., 2021; Mohammadi Lalabadi et al., 2020; Sengar et al., 2018; Shi, Qian, et al., 2018; Tolentino et al., 2017). Computer vision is a noncontact and fast responsive method and it could be easily implemented in online automated fish processing lines to determine the freshness of fish before further processing.…”

Section: Current Applications Of Artificial Senses For Fish Freshness...mentioning

confidence: 99%

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Current applications and future trends of artificial senses in fish freshness determination: A review

Madhubhashini,

Liyanage,

Alahakoon

et al. 2023

Journal of Food Science

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Fish is a highly demanding food product and the determination of fish freshness is crucial as it is a fundamental factor in fish quality. Therefore, the fishery industry has been working on developing rapid fish freshness determination methods to monitor freshness levels. Artificial senses that mimic human senses are developed as convenient emerging technologies for fish freshness determination. Computer vision, electronic nose (e‐nose), and electronic tongue (e‐tongue) are the emerging artificial senses for fish freshness determination. This review article is uniquely worked upon to investigate the current applications of the artificial senses in fish freshness determination while describing the steps, and fundamental principles behind each artificial sense, comparing them with their advantages and limitations, and future trends related to fish freshness determination. Among the artificial senses, computer vision determines the freshness of fish in a completely nondestructive way while the e‐tongue determines the freshness of fish in a completely destructive way. There are developed e‐noses for fish freshness determination in both destructive and nondestructive ways. By analyzing visual cues such as color, computer vision systems can assess fish quality without the need for physical contact and it makes computer vision suitable for large‐scale industrial fish quality assessing applications. Overall, this review study reveals artificial senses as a proven replacement for traditional sensory panels in determining fish freshness precisely and conveniently. As future trends, there is a demand for developing applications for consumers to determine fish freshness based on artificial senses.

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Developing a Machine Vision System Equipped with UV Light to Predict Fish Freshness Based on Fish-Surface Color

Cited by 4 publications

References 27 publications

Application of Machine Learning to Badminton Action Decomposition Teaching

Application of Machine Learning to Badminton Action Decomposition Teaching

Design and Application of Art Education Communication Platform Based on Machine Vision

Current applications and future trends of artificial senses in fish freshness determination: A review

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