乌龙茶品种的分类基于计算机视觉和卷积神经网络
Yiwen Zhu1, Siyuan Chen1, Hanzhe Yin1
1Department of Food Science & Technology, School of Agriculture & Biology, Shanghai Jiao Tong University, Shanghai, China.
Journal of the science of food and agriculture
|October 16, 2023
概括
机器学习和计算机视觉准确地使用视觉特征对乌龙茶品种进行分类. 这种自动化方法提高了茶叶质量控制和可追溯性.
科学领域:
- 食品科学与技术 食品科学与技术
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 精确区分乌龙茶品种对于食品行业的可追溯性和质量控制至关重要.
- 目前用于乌龙茶分类的方法在速度和准确性方面面临挑战.
- 需要使用视觉属性的自动分类来解决这些局限性.
研究的目的:
- 利用机器学习和计算机视觉开发一种用于分类乌龙茶品种的自动化系统.
- 评估支持矢量机器 (SVM) 和卷积神经网络 (CNN) 对此任务的性能.
- 为了确定乌龙茶区分的关键视觉特征.
主要方法:
- 从五种乌龙茶品种中提取13种视觉特征 (颜色,纹理).
- 实施数据增强和图像裁剪,以提高模型的稳定性.
- 开发和比较SVM和CNN模型,包括ResNet50架构.
主要成果:
- 卷积神经网络 (CNN) 的一种类型ResNet50模型在乌龙茶的分类中实现了超过93%的Top-1准确性.
- 像颜色和纹理这样的视觉属性被成功地用于区分茶叶品种.
- 该研究证明了实施的机器学习模型的有效性.
结论:
- 将计算机视觉与机器学习集成为快速准确的乌龙茶分类提供了一个有希望的,非侵入性的方法.
- 这种方法对茶叶行业的工艺监测,质量保证和真实性验证有重大影响.
- 这些发现支持使用自动化视觉分析来检测伪造,并确保茶叶产品的完整性.
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