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使用优化深度学习方法检测和分类肉类新鲜度

Mohamed Abd Elfattah¹, Ahmed A Ewees², Ashraf Darwish³

¹Computer Science Department, Misr Higher Institute for Commerce and Computers, Mansoura 35511, Egypt.

|June 6, 2025

概括

这项研究引入了深度学习方法来分类肉类新鲜度,达到98.51%的准确性. 这种方法提高了食品工业的食品安全和质量控制.

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:

准确的肉类新鲜度评估对于食品安全和减少浪费至关重要.
肉类的有效分类 (新鲜,半新鲜,腐烂) 有利于生产者,零售商和消费者.

研究的目的:

开发一种基于深度学习的方法,用于准确的肉类新鲜度分类.
通过自动分类来改进食品安全监测系统.

主要方法:

使用视觉几何组19 (VGG19) 卷积神经网络进行特征提取.
通过带有粒子群优化 (PSO) 的改进人工原生动物优化器 (IAPO) 进行特征选择.
使用优化功能集进行分类.

主要成果:

提出的方法实现了98.51%的准确性,98.54%的灵敏度和99.24%的特异性.
在基准测试中表现优于其他五种成熟的优化技术.

结论:

深度学习方法在肉类新鲜度分类方面表现出高效.
该方法显示了将其整合到食品安全监测系统中的潜力.

关键词:

人工原生动物优化器深度学习是一种深度学习.功能选择功能选择肉类质量评估肉类质量评估优化优化优化优化在VGG19中,VGG19是VGG19的代表.

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