The review of food safety inspection system based on artificial intelligence, image processing, and robotic

Chen, Tzu-Chia; Yu, Shuyan

doi:10.1590/fst.35421

Cited by 14 publications

(4 citation statements)

References 44 publications

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“…Furthermore, in both underdeveloped and developed nations, it is crucial for human health [9]. AI, ML, Big data, blockchain, etc., have found their applications in identifying, sorting, and determining safety in food products that fasten this screening process, ultimately bene ting the food industry [10,11]. Food-borne illnesses, pathogenic genomes, and emerging dynamic data, such as literary, commercial, and market data, have seen developing machine learning applications, such as antibiotic-resistant forecasting, pathogen source tracing, and food-borne epidemic identi cation and vulnerability assessment [12].…”

Section: Literature Reviewmentioning

confidence: 99%

An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

Geetha

Krishna

Vyas

et al. 2022

Journal of Food Quality

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Chronic diabetes among adults is a public health concern and clinicians are trying to implement new strategies to effectively manage the disease. Traditionally, healthcare professionals are used to monitor and track the lab reports of patients. After that, they used to provide respective medicines and lifestyle plans to manage the chronic disease. The lifestyle of the patients and access to safe and secure food products is also responsible for developing chronic diseases. Thus, the Internet of Things (IoT) has taken an utmost interest in managing diabetes. This research is going to analyze the accuracy of IoT in assisting chronic diabetes management and determining food safety. To accomplish the research objectives, the researchers performed a linear regression analysis to understand whether IoT devices and Artificial Intelligence (AI) assist in assessing food safety and diabetes management. The independent variables selected were lab test values, treatment records, epoch size of AI, and image resolution of the training dataset. Dependent variables were the accuracy of IoT. Here, the accuracy of IoT and AI has been determined. Moreover, the accuracy of clinicians in diabetes management has been observed. It has been found that clinicians have high variance in accuracy (max 99%) whereas machines have limited variance in accuracy (max. 98%). Secondary research identified that clinicians need to be involved along with IoT devices for better management of this chronic disease and help patients by providing the safest food options.

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Section: Literature Reviewmentioning

confidence: 99%

An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

Geetha

Krishna

Vyas

et al. 2022

Journal of Food Quality

View full text Add to dashboard Cite

show abstract

“…This type of algorithm is very important in artificial intelligence because it can reduce the programming burden by selecting explicit features. This algorithm can be used to solve problems in image recognition, speech recognition, text classification, and other applications that require supervision (supervised), no supervised (unsupervised), or some supervised (semi-supervised) [ 35 ].…”

Section: Methodsmentioning

confidence: 99%

Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients’ Nutrition Control

Salaki

Shin

Lee

et al. 2023

Sensors

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The management of type 2 diabetes mellitus (T2DM) is generally not only focused on pharmacological therapy. Medical nutrition therapy is often forgotten by patients for several reasons, such as difficulty determining the right nutritional pattern for themselves, regulating their daily nutritional patterns, or even not heeding nutritional diet recommendations given by doctors. Management of nutritional therapy is one of the important efforts that can be made by diabetic patients to prevent an increase in the complexity of the disease. Setting a diet with proper nutrition will help patients manage a healthy diet. The development of Smart Plate Health to Eat is a technological innovation that helps patients and users know the type of food, weight, and nutrients contained in certain foods. This study involved 50 types of food with a total of 30,800 foods using the YOLOv5s algorithm, where the identification, measurement of weight, and nutrition of food were investigated using a Chenbo load cell weight sensor (1 kg), an HX711 weight weighing A/D module pressure sensor, and an IMX219-160 camera module (waveshare). The results of this study showed good identification accuracy in the analysis of four types of food: rice (58%), braised quail eggs in soy sauce (60%), spicy beef soup (62%), and dried radish (31%), with accuracy for weight and nutrition (100%).

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“…Expert systems are grounded in applications in artificial intelligence and engineering, which due to the growing need of societies to adopt solutions and quick decisions in cases where complex and multiple human knowledge is needed, their importance is increased. Expert systems solve problems that typically require expertise and expertise (Chen & Yu, 2021). Machine iodine is known as expert system iodine only if it has a series of special capabilities, such as knowing its existence, which means that the machine is aware of its existence (Alamir, 2021).…”

Section: Introductionmentioning

confidence: 99%

Application of neural networks in predicting the qualitative characteristics of fruits

et al. 2022

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In this research, the quality properties of persimmon were predicted using artificial intellect techniques. The persimmon samples were transferred to a computer vision lab, room temperature of 24 °C and 22% RH. The samples were divided into three groups for temperature treatment. They were kept at three temperature levels of 5 °C, 15 °C, and 24°C (control group) for 72 hours. The sample was then placed at room temperature and was imaged every second day for a 14 day period. After imaging, each sample underwent destructive tests to determine their quality attributes, including sugar content, firmness, and pH. The results indicate that the neural network's predicted values of acidity, firmness, and sugar of persimmon were not statistically significant differences from their actual values. In predicting the acidity of persimmon, the sugar RMSE is more than the two factors of firmness and acidity. For this reason, the accuracy of firmness and acidity is higher than sugar. MAPE is 10.11, 20.81, and 6.03 for acidity, firmness, and sugar, respectively. The model for sugar indicates a high difference between the actual values and the predicted values.

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The review of food safety inspection system based on artificial intelligence, image processing, and robotic

Cited by 14 publications

References 44 publications

An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

An Investigation in Applying Internet of Things Approach in Safe Food Dietary Plan for Better Chronic Diabetes Management among Elderly Adults

Health to Eat: A Smart Plate with Food Recognition, Classification, and Weight Measurement for Type-2 Diabetic Mellitus Patients’ Nutrition Control

Application of neural networks in predicting the qualitative characteristics of fruits

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