对VNIR和SWIR超频谱数据的协同多级融合框架用于检测大豆真菌污染的检测
Xijun Shao1, Zhen Guo1, Yifei Qin2
1College of Agricultural Engineering and Food Science, Shandong University of Technology, No. 266 Xincun Xilu, Zibo, Shandong 255049, China.
Food chemistry
|July 25, 2025
概括
本研究引入了一种非破坏性方法,用于检测菌的大豆,使用合可见和近红外 (VNIR) 和短波红外 (SWIR) 超光谱成像. 该方法显著提高了在农产品中识别真菌污染的准确性.
科学领域:
- 农业科学 农业科学
- 频谱学是一种光谱学.
- 机器学习 机器学习
背景情况:
- 检测大豆中真菌污染的传统方法是破坏性的,低效的,劳动密集的.
- 需要快速,非破坏性的技术来评估大豆的质量和安全性.
- 超光谱成像 (HSI) 提供了在没有物理损伤的情况下进行详细材料分析的潜力.
研究的目的:
- 开发和评估一种高效,非破坏性的分类豆的方法.
- 调查可见和近红外 (VNIR) 和短波红外 (SWIR) HSI数据融合的有效性.
- 为了比较不同的数据融合策略和用于菌检测的特征选择技术.
主要方法:
- 采用了多级框架,将VNIR和SWIR HSI数据的低级数据融合 (LLF) 和中级数据融合 (ILF) 结合起来.
- 使用的特征选择算法:竞争性适应性重权取样 (CARS),连续预测算法 (SPA) 和非信息变量消除 (UVE).
- 用一维卷积神经网络 (1D-CNN) 来对LLF进行分类,并使用ILF的变压器模型进行分类.
主要成果:
- 与1D-CNN相结合的LLF策略实现了97.52%的高分类精度.
- 使用变压器模型的ILF策略达到95.71%的分类准确率.
- 两种融合策略在识别真菌污染方面都表现出强的表现.
结论:
- 拟议的HSI数据融合框架为检测菌大豆提供了准确和高效的非破坏性方法.
- 这项技术为实时农业质量监测和食品安全提供了有希望的进步.
- 整合VNIR和SWIR HSI数据显著提高了真菌污染的检测能力.
关键词:
数据融合数据融合深度学习是一种深度学习.菌污染的污染情况超光谱成像技术的使用.豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆豆更多相关视频
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