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作 者:段杰 张成 赵刚 熊天娥 周厚发 何元胜 徐杰 张永俊 杨宏辉 黄进生 李本辉 DUAN Jie;ZHANG Cheng;ZHAO Gang;XIONG Tiane;ZHOU Houfa;HE Yuansheng;XU Jie;ZHANG Yongjun;YANG Honghui;HUANG Jinsheng;LI Benhui(Yunnan Tobacco Lincang Area Company,Lincang 677099,China)
机构地区:[1]云南省烟草公司临沧市公司,云南临沧677099
出 处:《安徽农学通报》2025年第5期23-27,共5页Anhui Agricultural Science Bulletin
基 金:中国烟草总公司云南省公司科技项目(2022530000241006)。
摘 要:为探究应用近红外光谱技术建立的模型对雪茄鲜烟叶部位等级的识别效果,以雪茄鲜烟叶部位等级识别模型为研究对象,选取634个雪茄鲜烟叶样本数据作为训练集,构建包括PLS-DA、随机森林和KNN等机器学习分类模型,并基于Stacking方法构建单个部位等级识别融合模型,以273个雪茄鲜烟叶样本数据作为测试集评估融合模型的识别效果。结果表明,在3种基础模型中,PLS-DA模型在测试集上的预测效果最佳,平均准确率和F1均值分别为0.707和0.672。融合模型在测试集上的平均准确率和F1均值分别为0.756和0.752,且融合模型对雪茄鲜烟叶各部位等级的识别效果均较好。综合来看,基于近红外光谱数据构建的雪茄鲜烟叶部位等级融合模型具有较好的识别效果,为雪茄鲜烟叶部位等级的快速测定提供参考。In order to explore the identification effect of the model established by near infrared spectroscopy on cigar fresh tobacco position grade,the cigar fresh tobacco position grade recognition model was selected as the research object,634 cigar fresh tobacco sample data were selected as the training set,and machine learning classification models including PLS-DA,Random Forest and KNN were constructed.A fusion model for the rank recognition of individual positions was constructed based on the Stacking method.273 fresh cigar leaf samples were used as the test set to evaluate the recognition effect of the fusion model.The results showed that among the 3 basic models,the PLS-DA model had the best prediction effect on the test set,and the average accuracy and F1 mean were 0.707 and 0.672,respectively.The average accuracy and F1 mean of the fusion model on the test set were 0.756 and 0.752,respectively,and the fusion model had a good effect on the position grade recognition of each part of fresh cigar tobacco.In summary,the cigar leaf position grade fusion model based on the near infrared spectral data had a good recognition effect.The results provide a reference for the rapid determination of cigar fresh tobacco position grade.
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