Application of Convolutional Neural Networks and Recurrent Neural Networks in Food Safety

Area of Science:

• Food Science
• Computer Science
• Artificial Intelligence

Background:

• Traditional food safety detection and risk prediction methods have limitations.
• Deep learning offers advanced capabilities for analyzing complex data in food safety.

Purpose of the Study:

• To review and compare the applications of Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) in food safety.
• To analyze the strengths and weaknesses of these deep learning models against traditional approaches.
• To explore integration with emerging technologies for improved food safety systems.

Main Methods:

• Comparative analysis of CNNs and RNNs with traditional methods for food safety.
• Evaluation of CNNs for image processing and RNNs (including LSTM) for time series data.
• Discussion of hybrid approaches combining deep learning with IoT, blockchain, and federated learning.

Main Results:

• CNNs excel in image-based food safety detection, while RNNs are powerful for time series risk prediction.
• Deep learning models offer significant advantages over traditional methods in accuracy and efficiency.
• Integration with IoT, blockchain, and federated learning can further enhance food safety systems.

Conclusions:

• CNNs and RNNs are promising tools for advancing food safety detection and risk prediction.
• Combining deep learning with other technologies and interpretable AI (XAI) is crucial for future development.
• Addressing model interpretability challenges is essential for widespread adoption in food safety.