一种基于RGB图像和超光谱数据的模型,用于识别Tieguanyin Oolong茶的发酵程度
Yuyan Huang1, Yongkuai Chen1, Chuanhui Li1
1Institute of Digital Agriculture, Fujian Academy of Agricultural Sciences, Fuzhou 350003, China.
Foods (Basel, Switzerland)
|January 28, 2026
概括
图像和高光谱特征的数据融合显著改善了乌龙茶发酵评估. 使用融合数据的机器学习模型实现了更高的准确性,识别了用于质量控制的关键光谱和图像特征.
科学领域:
- 农业科学 农业科学
- 食品科学 食品科学 食品科学
- 数据科学数据科学数据科学
背景情况:
- 乌龙茶的质量和味道严重依赖于发酵过程.
- 精确评估茶叶发酵程度对于质量控制和产品的一致性至关重要.
研究的目的:
- 开发和比较机器学习模型,以评估安铁贵宁乌龙茶的发酵程度.
- 调查图像和超光谱数据 (数据融合) 结合的有效性,以提高准确性.
主要方法:
- 从RGB图像中提取颜色和纹理特征,并从超光谱数据中提取光谱特征.
- 应用了各种预处理,特征选择 (Pearson,CARS,SPA,L1) 和缩小维度 (PCA) 技术.
- 开发和评估机器学习模型 (SVM,LSTM,GRU),使用单源和融合特征,包括对特征重要性进行SHAP分析.
主要成果:
- 使用单一特征的模型达到高达87.50%的精度.
- 数据融合模型显著提高了性能,在Pearson+Nor-SPA+L1+SVM模型中达到94.44%的最大精度.
- SHAP分析确定了关键的超谱波段和图像特征,这些特征对于区分发酵阶段至关重要.
结论:
- 数据特征融合提供了对乌龙茶发酵过程的更全面的描述,从而提高了模型的准确性.
- 该研究为对Tieguanyin乌龙茶发酵的客观评估和智能检测系统的开发提供了科学基础.
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