使用机器学习和深度学习模型的自动咖啡级别分类
René Ernesto García Rivas1, Pedro Luiz Lima Bertarini2, Henrique Fernandes1,3
1Faculty of Computing, Federal University of Uberlandia, Uberlândia, Brazil.
Journal of food science
|September 9, 2025
概括
使用机器学习 (ML) 和计算机视觉实现了100%的准确性. 这种进步为咖啡行业提供了一致的客观质量控制.
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:
- 咖啡质量受到烤过程的严重影响,传统上是手动评估.
- 手动的烤肉分类是主观的,不一致的,耗时的.
- 机器学习 (ML) 和计算机视觉方面的进步提供了自动化解决方案.
研究的目的:
- 评估用于自动咖啡烤层级分类的多个ML模型.
- 为了比较CNN与传统ML算法的性能.
- 开发一种可靠,可扩展的咖啡质量控制解决方案.
主要方法:
- 训练并测试了ML模型,包括使用Xception,AdaBoost,随机森林 (RF) 和支持矢量机器 (SVM) 的CNN.
- 利用一个公开的数据集,包括1600张图像,分为四个烤肉级别 (绿色,浅色,中等,深色).
- 应用图像增强技术,以提高模型的通用性.
主要成果:
- 所有评估的模型都实现了100%的准确性和F1分数,用于分类咖啡烤水平.
- 与之前的研究相比,提出的自动化方法表现出强的表现.
- 图像增强提高了模型的概括性.
结论:
- 机器学习和计算机视觉提供了一个高度准确和自动化的方法,用于咖啡烤的分类.
- 这项技术为咖啡行业的质量控制提供了显著的改进.
- 开发的解决方案可靠,可扩展,并有助于持续的咖啡生产.
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