Intelligent Detection of Moldy Cigarette Leaves Based on Near-infrared Spec-troscopy
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.
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