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相关概念视频

Methods of Controlling Food Spoilage01:26

Methods of Controlling Food Spoilage

Food spoilage is caused by microbial growth or by chemical and physical changes, all of which affect the taste, texture, and safety of food.Temperature-Based PreservationRefrigeration at 0–4 °C slows microbial growth and enzyme activity, making it ideal for short-term storage. However, certain spoilage organisms—such as psychrotrophs like Listeria monocytogenes—can still proliferate at these temperatures. Freezing below -18 °C further slows biological processes by forming ice crystals, which...

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Experimental Assessment of Mouse Sociability Using an Automated Image Processing Approach
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基于计算机视觉的猪肉切片和新鲜度确定方法的研究

Shihao Song1, Qiqi Guo2, Xiaosa Duan2

  • 1School of Information Science and Engineering, Shanxi Agricultural University, Jinzhong 030801, China.

Foods (Basel, Switzerland)
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概括

本研究引入了用于猪肉质量评估的自动化计算机视觉系统. MobileNetV3_Small模型在检测猪肉部分和新鲜度方面取得了98.59%的准确性,提高了肉类检查效率.

关键词:
计算机视觉 计算机视觉卷积神经网络是一种卷积神经网络.食品安全 食品安全肉质检测检测检测肉质检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测检测猪肉切片 猪肉切片 在线观看

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科学领域:

  • 农业科学 农业科学
  • 计算机科学 计算机科学
  • 食品科学 食品科学 食品科学

背景情况:

  • 传统的手动猪肉质量检查是低效的,缺乏准确性.
  • 需要自动化方法来改善猪肉质量评估.

研究的目的:

  • 开发一种使用计算机视觉的自动猪肉质量检测系统.
  • 评估用于猪肉质量评估的不同卷积神经网络 (CNN) 模型的效率和准确性.

主要方法:

  • 高分辨率摄像头捕获了来自金芬白猪的猪肉图像 (后腿,腰部,腹部).
  • 数字图像处理扩展了数据集;用于特征识别,使用了五种CNN模型 (VGGNet,ResNet,DenseNet,MobileNet,EfficientNet).
  • 使用PYQT5框架部署了MobileNetV3_Small模型,用于端到端的系统.

主要成果:

  • 移动网络V3_小模型实现了98.59%的准确性,超过了其他经过测试的CNN架构.
  • 统计分析显示,MobileNetV3_Small和ResNet101,EfficientNetB0和EfficientNetB1之间没有显著的性能差异 (p > 0.05).
  • 其他模型表现出统计学上显著的性能差异 (p < 0.05).

结论:

  • 开发的系统提供了一种高效和准确的方法,用于自动检测猪肉质量.
  • 这项技术可以显著提高猪肉质量检查的可靠性,并支持猪肉安全监测系统.