河南科技大学学报（自然科学版）2024，Vol.45Issue(3)：32-42,11.DOI:10.15926/j.cnki.issn1672-6871.2024.03.005
基于高光谱成像的烟丝中梗签分类识别研究
Research on Classification and Recognition of Stem Sticks in Shredded Cut Tobacco Based on Hyperspectral Imaging
摘要
Abstract
Regarding the classification,recognition,and detection of stem sticks in cut tobacco leaves,this article uses hyperspectral imaging technology and combines machine learning methods to classify and rapidly identify adulterated stem sticks in cut tobacco leaves.Firstly,based on shortwave near-infrared hyperspectral imaging technology,standard normal variate(SNV)transformation is applied to preprocess the spectral data of both cut tobacco leaves and stem sticks.The goal is to eliminate the effects of spectral scattering and reflectance,reducing various sources of interference.Subsequently,a feature wavelength selection is conducted using the successive projections algorithm(SPA),integrate the extreme gradient boosting(XGBoost)algorithm,proposed a stem stick classification model in shredded cut tobacco based on the XGBoost algorithm.Finally,a post-processing method is employed to achieve intelligent detection of stem sticks within cut tobacco leaves,combining the mean-shift mean-shift algorithm and morphological gradient algorithm.The classification results of the model are post-processed using this approach.The results demonstrate that the established SNV-SPA-XGBoost classification model achieves accuracy rates of 100%for the training set and 99.32%for the test set.After post-processing,the accuracy rates for detecting A1(1.0～1.5 cm),A2(0.5～1.0 cm),and A3(<0.5 cm)stem sticks reach 100%,95.50%,and 86%respectively.关键词
高光谱成像/机器学习/梗签/连续投影算法/XGBoost/分类Key words
hyperspectral imaging/machine learning/stem sticks/successive projections algorithm/XGBoost/classification分类
信息技术与安全科学引用本文复制引用
陶发展,杨栋,洪伟龄,苏子淇,付主木,林志平..基于高光谱成像的烟丝中梗签分类识别研究[J].河南科技大学学报（自然科学版）,2024,45(3):32-42,11.基金项目
国家自然科学基金项目(62371182) （62371182）
河南省高校科技创新人才计划项目(23HASTIT021) （23HASTIT021）
河南省科技研发计划联合基金(225200810007,222103810036) （225200810007,222103810036）
中国烟草总公司科技项目(110202202010) （110202202010）
