通过图像频谱融合,智能评估黑茶枯质量,使用多式联络注意网络与对比学习
Qin Ouyang1, Huilin Chang1, Dengshan Li1
1School of Food and Biological Engineering, Jiangsu University, Zhenjiang 212013, PR China.
Food chemistry
|January 22, 2026
概括
这项研究引入了一种新的计算机视觉 (CV) 和近红外光谱 (NIRs) 方法来评估黑茶枯. 多式联运方法准确地分类了枯程度,并预测了关键的质量指标.
科学领域:
- 农业科学 农业科学
- 食品科学 食品科学 食品科学
- 频谱学是一种光谱学.
背景情况:
- 黑茶的质量受到枯过程的严重影响.
- 传统的对茶叶枯的手动评估是主观和低效的.
- 实时质量控制需要客观和自动化的方法.
研究的目的:
- 开发使用计算机视觉 (CV) 和近红外光谱 (NIR) 的黑茶枯多式评估系统.
- 为了实现枯度的准确分类和关键质量参数的定量预测.
- 为了使茶叶加工过程中的智能和自动化质量评估.
主要方法:
- 为了分类,开发了一个多式注意力融合网络,将金字塔CNN (CV) 和注意力增强的CNN/GRU (NIR) 结合起来.
- 对比式学习用于跨模式融合.
- 一个增强的挤压和激发ResNet18模型被用来融合CV和NIR的特征用于定量预测.
- 模型使用测量水分和可溶糖含量的数据集进行训练和验证.
主要成果:
- 多式联动注意力融合网络在分类衰退度方面实现了95.56%的准确性.
- 定量评估模型产生了高的相关系数:湿度为0.9151,可溶性糖含量为0.9023.
- 综合CV和NIR方法在定性和定量评估中表现强.
结论:
- 拟议的多式联运CV和NIRs方法为评估黑茶枯提供了一个全面而智能的策略.
- 这项技术可以显著提高黑茶质量控制的客观性,效率和准确性.
- 这些发现为茶叶行业的先进自动化铺平了道路.
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