基于多源信息融合技术的马铃薯痂疮病无损检测方法  

Nondestructive detection method of potato scab based on multi-sensor information fusion technology

作　　者：李小昱[1] 陶海龙[1] 高海龙[1] 李鹏[1] 黄涛[1] 任继平[2] 

机构地区：[1]华中农业大学工学院,武汉430070 [2]华中农业大学理学院,武汉430070

出　　处：《农业工程学报》2013年第19期277-284,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(61275156);湖北省自然科学基金重点项目(2011CDA033);中央高校基本科研业务费专项基金(0900205116)

摘　　要：为了提高马铃薯痂疮病无损检测识别精度,基于机器视觉和近红外光谱的多源信息融合技术,该文提出DS(dempster shafer)证据理论结合支持向量机的马铃薯痂疮病无损检测方法。试验以360个马铃薯为研究对象,在图像特征分割时,确定了差影法结合马尔可夫随机场模型法为最佳分割方法;在光谱特征提取时,确定主成分分析方法为最佳降维方法。采用支持向量机识别方法分别建立机器视觉和近红外光谱的马铃薯痂疮病识别模型,模型对测试集马铃薯识别率分别为89.17%、91.67%。采用DS证据理论与支持向量机相结合的方法对获取的图像特征和光谱特征进行融合,建立了基于机器视觉和近红外光谱技术的多源信息融合马铃薯痂疮病检测模型,该模型对测试集马铃薯识别率为95.83%。试验结果表明,该技术对马铃薯痂疮病进行检测是可行的,融合模型比单一的机器视觉模型或近红外光谱模型识别率高。The common scab is a skin disease of the potato tuber that decreases the quality of the product and significantly influences the price, so it is very necessary to find a quickly nondestructive way to detect potato scabs. In this study, machine vision technology and near infrared spectroscopy analysis technology were used to detect potato scabs. In order to improve the potato scab nondestructive recognition accuracy, multi-sensor information fusion technique was proposed to detect potato scabs based on machine vision and near infrared spectroscopy. DS evidence theory combined with support vector machine method was used for multi-sensor information fusion technique. In the research, 360 potatoes were taken as testing samples （180 qualified potatoes and 180 scab potatoes）. This study concluded that the difference image method combined with the Markov random field model method was the best segmentation method in the segmentation of image characteristics through the image preprocessing. And the principal component analysis method was the best method in the spectral feature extraction through the spectroscopy preprocessing. This study compared several different spectral preprocessing methods to preprocess the near infrared spectroscopy in near infrared spectroscopy preprocessing. And from the discriminating rate of the support vector machine model with the pretreated near infrared spectroscopy, it was concluded that the dimension reduction method was the best spectroscopy preprocessing method. The support vector machine method was a good pattern recognition method, so this study used the support vector machine method to detect potato scabs based on machine vision technology and near infrared spectroscopy analysis technology. The support vector machine models to discriminate potato scab were built based on machine vision technology and near infrared spectroscopy analysis technology respectively. The discriminating rates of these two models were 89.17% and 91.67% in testing sets respectively. To improve the discrimin

关 键 词：近红外光谱 信息融合 无损检测 机器视觉 痂疮病 马铃薯 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] O657.33[自动化与计算机技术—计算机科学与技术]