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农产品原产地可追溯性:用于光谱信息处理的轻量级协作神经网络

Yan Shi¹, Yang Yu¹, Jinyue Zhang²

¹School of Automation Engineering, Northeast Electric Power University, Jilin 132012, China; Advanced Sensor Research Institution, Northeast Electric Power University, Jilin 132012, China; Bionic Sensing and Pattern Recognition Team, Northeast Electric Power University, Jilin 132012, China.

Food research international (Ottawa, Ont.)

|April 23, 2025

概括

一个新的AI模型,轻量级协作神经网络 (LC-Net),使用高光谱成像准确识别花生和大米的地理来源. 这项技术有助于防止食品欺诈,并确保农产品的质量和安全.

科学领域:

农业科学农业科学
食品科学食品科学食品科学
人工智能的人工智能

背景情况:

区域气候,土壤和水对农产品质量产生影响.
产品原产地验证可以防止改,并确保食品安全.
超光谱成像为农产品分析提供了潜力.

研究的目的:

开发一种基于人工智能的方法来确定农产品的原产地.
评估轻量级协作神经网络 (LC-Net) 对原产地识别的有效性.
通过原产地核查,确保农产品的质量和安全.

主要方法:

提出了一个与高光谱系统集成的轻量级协作神经网络 (LC-Net).
开发了一种协作光谱特征提取模块 (CSFEM),用于增强特征表达.
应用LC-Net模型来识别来自七个不同地区的花生和大米的原产地.

主要成果:

在花生原产地识别方面,LC-Net实现了99.33%的准确性,98.98%的精度和99.28%的回忆率.
LC-Net实现了99.76%的准确性,99.63%的精度和99.73%的回忆力来识别大米的原产地.
通过本地和全球深度光谱特征提取,CSFEM模块提高了检测性能.

结论:

基于AI的LC-Net与超谱分析是农产品原产地识别的可靠技术.
这种方法有效防止食品欺诈,并确保农产品的质量和安全.
该研究表明,先进的人工智能和光谱分析在保护食品供应链方面的潜力.

关键词:

轻量级协作神经网络是一种轻量级协作神经网络.农产品的原产地农产品的原产地质量识别质量识别频谱分析是一种分析.

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