基于深度学习的高光谱腊肉营养安全分级

本文设计的卷积神经网络-支持向量机(CNN-SVM)模型,从腊肉的高光谱成像出发,将深度学习提取特征与传统机器学习提取特征有机结合,设计出准确可靠的腊肉营养安全四分类器。利用三维卷积神经网络提取腊肉高光谱图像的深层特征,同时融合高光谱的光谱特征,联合输入支持向量机(SVM)实现对腊肉的分类和健康风险评价。结果:获得了与国家腊肉生化检测标准相一致的高光谱营养品质检测与健康风险评估指标,实现了可靠、快速评价其安全、营养品质的目标。在腊肉两分类的基础上,该方法实现的腊肉四分类的准确率达到92.5%,试验结果证明了该方法的可行性和有效性。

Nutritional Health Risk Grading of Bacon Hyper-spectrum Based on Deep Learning

Based on the hyperspectral imaging of bacon,the CNN-SVM model designed in this paper organically combines deep learning extraction features with traditional machine learning extraction features to design an accurate and reliable bacon nutrition and health risk four classifier.The three-dimensional convolutional neural network is used to extract the deep features of the hyperspectral image of bacon,and the spectral features of the hyperspectral are fused.Both input the support vector machine (SVM) to realize the classification and health risk assessment of bacon,which is comparable to the national bacon biochemical detection standard.Consistent hyperspectral nutrition quality detection and health risk assessment indicators have achieved the research purpose of reliable and rapid evaluation of its health and nutrition quality.Based on the two classifications of bacon,the accuracy of the four classifications achieved by this method reaches 92.5%.The experimental results prove the feasibility and effectiveness of this method.