基于近红外光谱的片烟霉变智能检测方法
Intelligent Detection of Moldy Cigarette Leaves Based on Near-infrared Spec-troscopy
田勇 1廖欢 1江国强 1周涛1
作者信息
- 1. 四川中烟工业有限责任公司什邡卷烟厂,什邡 618400
- 折叠
摘要
该文创新性地提出了一种结合近红外光谱技术与深度学习的智能片烟霉变检测方法.尽管近红外光谱技术在农产品质量检测领域已取得显著成就,然而在烟草工业中,片烟霉变问题一直以其独特的挑战性而备受关注.传统的检测方法往往依赖于繁琐的化学分析或主观的人工审查,这些方法耗时且容易受到人为误差的干扰.因此,该文致力于构建一个高效自动的处理近红外光谱数据的深度学习模型,以实现对片烟霉变问题的准确检测,为烟草工业的发展提供了一种新颖且高效的解决方案.
Abstract
This study innovatively proposes an intelligent detection method for moldy cigarette leaves by combining near-infrared spectroscopy technology with deep learning.While near-infrared spectroscopy technology has made sig-nificant achievements in the field of agricultural product quality testing,the issue of moldy cigarette leaves in the to-bacco industry has always been a unique challenge.Traditional detection methods often rely on cumbersome chemical analysis or subjective manual inspection,which are time-consuming and susceptible to human errors.Therefore,this study is dedicated to constructing an efficient and automated deep learning model for processing near-infrared spec-troscopy data,aiming to achieve accurate detection of moldy cigarette leaves.This provides a novel and efficient solu-tion for the development of the tobacco industry.
关键词
近红外光谱/片烟霉变/智能检测/深度学习Key words
near-infrared spectroscopy/moldy cigarette leaves/intelligent detection/deep learning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据