基于激光散斑的梨缺陷与果梗/花萼的识别  

Pear defect and stem/calyx discrimination using laser speckle

作　　者：刘海彬[1] 高迎旺 卢劲竹[1] 饶秀勤[1] 

机构地区：[1]浙江大学生物系统工程与食品科学学院,杭州310058

出　　处：《农业工程学报》2015年第4期319-324,共6页Transactions of the Chinese Society of Agricultural Engineering

基　　金："十二五"国家科技支撑计划资助项目(2012BAF07B06)

摘　　要：为了测试利用激光散斑技术区分梨的缺陷与果梗/花萼的可行性,建立了激光散斑图像采集系统,对皇冠梨缺陷(腐烂)部位以及完好部位(花萼/果梗,无缺陷部位)分别进行了激光散斑图像的采集。利用Fujii方法(Fujii’s method)和加权广义差分方法(weighted generalized differences,WGD)对512幅散斑图像进行分析,对得到的Fujii和WGD结果图进行灰度共生矩阵特征提取,分别提取了角二阶矩、熵、惯性矩和相关性相应的均值及标准差,共计16组特征量。利用ROC曲线(receiver operator characteristic curve,ROC)进行特征量选取,结合约登指数测试单一特征量的分类效果,并利用二元logistic回归方法对所选特征量两两组合进行分析,结果显示基于WGD方法得到的角二阶矩均值与相关性标准差相结合在区分缺陷时效果最好,建模和预测准确率均达到了97.5%。试验的结果表明利用激光散斑图像方法对梨缺陷与果梗/花萼进行识别是可行的。Laser speckle is a new technology for non-destructive detection in agriculture. When laser light with good coherence irradiates on the optically rough surface or the surface with some kind of activity, the scattering light will interfere with each other and form a mottled pattern called laser speckle. The laser speckle contains various information of the reflector, such as the roughness, the particle＇s motion and the temperature information. Huangguan pears were used as the object in this research to investigate the possibility of identifying the defects from the stem/calyx and the sound area of pears based on laser speckle technology. A laser speckle imaging system was established which contained a semiconductor laser（635 nm, 50 mw） applied as the light source and a digital signal generator applied as the trigger source of the CCD（charge coupled device） camera. Two hundred pears including one hundred sound pears and one hundred pears with the defect（rot） were tested. The speckle images of defect（rot） parts and good parts（calyx/stem, sound area） of the pears were collected under the same condition. First of all, the speckle images were converted into the grayscale images using Matlab 2011 b. The method of Fujii and the weighted generalized differences method（WGD） were used to analyze the grayscale speckle images to get the images of Fujii and WGD. Then gray level co-occurrence matrix（GLCM） was used to extract the mean and the standard deviation values of the angular second moment（ASM）, entropy（ENT）, moment of inertia（INE） and correlation（COR） from the images of Fujii and WGD, respectively. Therefore, in total, 16 features were extracted. The performance of each feature was evaluated by the receiver operator characteristic（ROC） curve. According to the ROC curves, the features whose values of the area under the curves（AUC） were higher than 0.5 were chosen for further analysis. The best threshold value of each selected feature was calculated by Youden＇s index.

关 键 词：识别 图像处理 水果 激光散斑 缺陷 花萼/果梗 梨 

分 类 号：S123[农业科学—农业基础科学]