基于多维视觉特征和CNNs-SHAP的云南阿拉伯咖啡豆的烤度和可解释性分析的识别
Siheng Lu1, Jing Zhao1, Qian Qin1
1School of Grain Science and Technology, Jiangsu University of Science and Technology, Zhenjiang 212100, China.
Food chemistry
|February 11, 2026
概括
这项研究开发了一种可解释的AI系统,用于使用融合图像特征识别咖啡烤度. 卷积神经网络实现了高精度,可解释性增强了决策,以保持一致的咖啡质量.
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:
- 准确的咖啡度识别对于持续的质量至关重要.
- 传统的方法往往缺乏客观性和透明度.
- 深度学习模型提供了潜力,但经常充当黑子.
研究的目的:
- 开发一个可解释的人工智能系统,用于精确识别咖啡度.
- 为了将多个图像特征融合在一起,以便进行强大的咖啡豆分析.
- 为了克服咖啡的深度学习的黑子限制.
主要方法:
- CIE L*a*b*颜色组图,纹理特征 (GLCM-LBP) 和形态参数的加权融合.
- 卷积神经网络 (CNN) 的应用用于分类.
- 对于模型可解释性的SHapley添加式解释 (SHAP) 分析.
主要成果:
- CNNs在识别烤度方面取得了最高的准确性.
- SHAP分析显示,关键特征和预测之间存在强烈的负相关性,因烤量而异.
- 外部验证显示了高准确度:100.0%的黑暗,91.5%的光,和93.8%的中等烤肉.
结论:
- 拟议的可解释的人工智能系统能够准确和透明地识别咖啡烤度.
- 该系统集成了自动图像采集,特征提取和推断,支持可追溯性.
- 这种方法提供了一个精确的,可解释的技术,用于标准化咖啡过程.
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