Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety
This review explores the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in food safety detection and risk prediction. This paper highlights the advantages of CNNs in image processing and feature recognition, as well as the powerful capabilities of RNNs (espe...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Foods |
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| Online Access: | https://www.mdpi.com/2304-8158/14/2/247 |
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| author | Haohan Ding Haoke Hou Long Wang Xiaohui Cui Wei Yu David I. Wilson |
| author_facet | Haohan Ding Haoke Hou Long Wang Xiaohui Cui Wei Yu David I. Wilson |
| author_sort | Haohan Ding |
| collection | DOAJ |
| description | This review explores the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in food safety detection and risk prediction. This paper highlights the advantages of CNNs in image processing and feature recognition, as well as the powerful capabilities of RNNs (especially their variant LSTM) in time series data modeling. This paper also makes a comparative analysis in many aspects: Firstly, the advantages and disadvantages of traditional food safety detection and risk prediction methods are compared with deep learning technologies such as CNNs and RNNs. Secondly, the similarities and differences between CNNs and fully connected neural networks in processing image data are analyzed. Furthermore, the advantages and disadvantages of RNNs and traditional statistical modeling methods in processing time series data are discussed. Finally, the application directions of CNNs in food safety detection and RNNs in food safety risk prediction are compared. This paper also discusses combining these deep learning models with technologies such as the Internet of Things (IoT), blockchain, and federated learning to improve the accuracy and efficiency of food safety detection and risk warning. Finally, this paper mentions the limitations of RNNs and CNNs in the field of food safety, as well as the challenges in the interpretability of the model, and suggests the use of interpretable artificial intelligence (XAI) technology to improve the transparency of the model. |
| format | Article |
| id | doaj-art-7d7e0b6593a347fcae9a5d4960a75afb |
| institution | Kabale University |
| issn | 2304-8158 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Foods |
| spelling | doaj-art-7d7e0b6593a347fcae9a5d4960a75afb2025-01-24T13:33:00ZengMDPI AGFoods2304-81582025-01-0114224710.3390/foods14020247Application of Convolutional Neural Networks and Recurrent Neural Networks in Food SafetyHaohan Ding0Haoke Hou1Long Wang2Xiaohui Cui3Wei Yu4David I. Wilson5Science Center for Future Foods, Jiangnan University, Wuxi 214122, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, ChinaSchool of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, ChinaScience Center for Future Foods, Jiangnan University, Wuxi 214122, ChinaDepartment of Chemical & Materials Engineering, University of Auckland, Auckland 1010, New ZealandElectrical and Electronic Engineering Department, Auckland University of Technology, Auckland 1010, New ZealandThis review explores the application of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) in food safety detection and risk prediction. This paper highlights the advantages of CNNs in image processing and feature recognition, as well as the powerful capabilities of RNNs (especially their variant LSTM) in time series data modeling. This paper also makes a comparative analysis in many aspects: Firstly, the advantages and disadvantages of traditional food safety detection and risk prediction methods are compared with deep learning technologies such as CNNs and RNNs. Secondly, the similarities and differences between CNNs and fully connected neural networks in processing image data are analyzed. Furthermore, the advantages and disadvantages of RNNs and traditional statistical modeling methods in processing time series data are discussed. Finally, the application directions of CNNs in food safety detection and RNNs in food safety risk prediction are compared. This paper also discusses combining these deep learning models with technologies such as the Internet of Things (IoT), blockchain, and federated learning to improve the accuracy and efficiency of food safety detection and risk warning. Finally, this paper mentions the limitations of RNNs and CNNs in the field of food safety, as well as the challenges in the interpretability of the model, and suggests the use of interpretable artificial intelligence (XAI) technology to improve the transparency of the model.https://www.mdpi.com/2304-8158/14/2/247food safetydeep learningconvolutional neural networksrecurrent neural networkslong short-term memory |
| spellingShingle | Haohan Ding Haoke Hou Long Wang Xiaohui Cui Wei Yu David I. Wilson Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety Foods food safety deep learning convolutional neural networks recurrent neural networks long short-term memory |
| title | Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety |
| title_full | Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety |
| title_fullStr | Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety |
| title_full_unstemmed | Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety |
| title_short | Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety |
| title_sort | application of convolutional neural networks and recurrent neural networks in food safety |
| topic | food safety deep learning convolutional neural networks recurrent neural networks long short-term memory |
| url | https://www.mdpi.com/2304-8158/14/2/247 |
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