摘要
以“红”李子和“青”李子为研究对象,提出了基于高光谱成像技术结合误差反向传播(error Back Propagation,BP)网络无损检测李子硬度的方法。采用高光谱图像采集系统获取了李子样本的高光谱图像,并提取了感兴趣区域的平均光谱反射率;综合比较了不同光谱预处理方法(一阶导数(derivative)、标准正态变换(SNV)和多元散射校正(MSC))对BP网络模型检测效果的影响;并利用主成分分析方法对预处理后的光谱数据进行降维,以提取能反映李子硬度的特征光谱。研究结果表明:derivative预处理后的光谱具有较好的李子硬度校正能力(R C=0.939,RMSEC=0.153),而SNV预处理后的光谱具有较好的李子硬度预测能力(R P=0.723,RMSEP=0.580);采用主成分分析法选择了累计贡献率超过99.99%的主成分作为样本集特征光谱数据,很好地实现了光谱数据的降维,提升了BP网络模型的运行效率。这表明高光谱成像技术结合BP网络可实现李子硬度的无损检测。
The nondestructive detection on firmness of“Red”and“Green”plums is proposed based on hyperspectral imaging technology combined with error back propagation(BP)network.The hyperspectral imaging system is used to collect the hyperspectral image of plums,and the average spectral reflectance of the region of interest(ROI)is acquired.Then the effectiveness of BP network model using derivative,standard normal variation(SNV)and multi-scatter calibration(MSC)is compared and evaluated.Finally,the characteristic spectrum of firmness of plums are extracted by principal component analysis(PCA).The results show that the preprocessing effect of derivative on spectral reflectivity has the best calibration ability of firmness of plums(R C=0.939,RMSEC=0.153),and the preprocessing effect of SNV on spectral reflectivity has the best prediction ability of firmness of plums(R P=0.723,RMSEP=0.580).And principal components with cumulative contribution rate of 99.99%are selected as the characteristic spectral data in the sample set by principal component analysis,and the dimensionality reduction of the spectral data is well realized,leading to improving the efficiency of BP network model.This study indicates that hyperspectral imaging technology combined with BP network is effective for detection on firmness of plums.
作者
孟庆龙
张艳
尚静
MENG Qing-long;ZHANG Yan;SHANG Jing(Food and Pharmaceutical Engineering Institute,Guiyang University,Guiyang 550005,China;The Research Center of Nondestructive Testing for Agricultural Products,Guiyang University,Guiyang 550005,China)
出处
《激光与红外》
CAS
CSCD
北大核心
2019年第8期968-973,共6页
Laser & Infrared
基金
国家自然科学基金项目(No.61505036)
贵州省科技计划项目(No.黔科合基础[2019]1010)
贵州省普通高等学校工程研究中心(No.黔教合KY字[2016]017)
贵阳市科技局贵阳学院专项资金(No.GYU-KYZ[2018]01-08)资助
关键词
遥感
无损检测
高光谱成像
BP网络
李子
硬度
remote sensing
nondestructive detection
hyperspectral imaging
BP network
plums
firmness
