摘要
Abstract
In the tobacco grading process,inconsistent grading results are often observed due to factors such as human subjectivity and inconsistent grading standards.To address these issues,a tobacco grade classification model based on one-dimensional residual convolution is proposed.The VGG16 network is improved by replacing the square matrix convolutional kernels and pooling windows with vector convolution kernel and pooling window suitable for one-dimensional spectral data.The BasicBlock residual module is employed to replace the structure of multi-layer convolutional stacking for deeper extraction of spectral data and prevention of gradient vanishing issues.A BN layer module is added behind the convolutional layer to prevent the network efficiency reduction caused by scattered data distribution after convolutional computation by means of the normalization way.The near-infrared spectral data of five different grades of tobacco leaf samples,including B2V,B1F,C4F,C1L,and X2L are selected for experiments.The results show that the average classification accuracy of the training and testing sets for five levels of tobacco leaves in the proposed method is 98.0%and 97.3%,respectively,which is significantly higher than those of other methods.This method to some extent can solve the errors caused by manual grading of tobacco leaves,reduce manpower output,and improve efficiency.关键词
烟叶分级/残差卷积神经网络/残差模块/近红外光谱/数据特征提取/数据采集Key words
tobacco grading/residual convolutional neural network/residual module/near infrared spectroscopy/data feature extraction/data acquisition分类
电子信息工程
