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Updated: May 1, 2026

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Deep Neural Networks for Image-Based Dietary Assessment
Published on: March 13, 2021
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卷积神经网络和循环神经网络在食品安全中的应用
Haohan Ding1,2, Haoke Hou2, Long Wang2
1Science Center for Future Foods, Jiangnan University, Wuxi 214122, China.
Foods (Basel, Switzerland)
|January 25, 2025
概括
本文审查了深度学习模型,如卷积神经网络 (CNN) 和循环神经网络 (RNN),以提高食品安全检测和风险预测,并将它们与传统方法进行比较.
科学领域:
- 食品科学 食品科学 食品科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 传统的食品安全检测和风险预测方法存在局限性.
- 深度学习为分析食品安全复杂数据提供了先进的功能.
研究的目的:
- 审查和比较卷积神经网络 (CNN) 和循环神经网络 (RNN) 在食品安全中的应用.
- 与传统方法相比,分析这些深度学习模型的优缺点.
- 探索与新兴技术的整合,以改善食品安全系统.
主要方法:
- 对CNN和RNN与食品安全传统方法进行比较分析.
- 评估用于图像处理的CNN和用于时间序列数据的RNN (包括LSTM).
- 讨论混合方法,将深度学习与物联网,区块链和联合学习相结合.
主要成果:
- 在基于图像的食品安全检测方面,CNN非常出色,而在时间序列风险预测方面,RNN非常强大.
- 深度学习模型在准确性和效率方面比传统方法具有显著的优势.
- 与物联网,区块链和联合学习的整合可以进一步增强食品安全系统.
结论:
- CNN和RNN是推动食品安全检测和风险预测的有希望的工具.
- 将深度学习与其他技术和可解释AI (XAI) 结合起来,对于未来的发展至关重要.
- 解决模型解释性挑战对于食品安全的广泛采用至关重要.
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