基于高光谱成像技术的山楂损伤和虫害缺陷识别研究

Detection of Hawthorn Fruit Defects Using Hyperspectral Imaging

采用高光谱成像技术（420~1 000nm）对山楂的缺陷（表面的损伤以及虫害区域）进行识别研究。共采摘了134个样品,包含损伤果46个、虫害果30个、损伤及虫害果10个和完好果48个。考虑到山楂的花萼、果梗与损伤、虫害的RGB图像有相似的外观特征,容易造成误判,利用高光谱成像系统采集了损伤、虫害、完好、花萼和果梗五个区域一共230个山楂样本的高光谱图像,并提取相应的感兴趣区域（region of interest,ROI）,得到了样本的光谱数据。使用标准归一化（standard normalized variate,SNV）,卷积平滑（savitzky golay,SG）,中值滤波（median filter,MF）,多元散射校正（multiplicative scatter correction,MSC）方法进行光谱预处理,建立偏最小二乘（partial least squares method,PLS）判别分析模型,结果表明经过SNV预处理后的预测结果较好。最后选取SNV作为预处理方法。应用回归系数法（regression coefficients,RCs）从全波段中提取10条特征波段（483,563,645,671,686,722,777,819,837和942nm）,利用Kennard-Stone算法将各类样本按照3：1的比例随机分成训练集（173个）和测试集（57个）,并对其建立最小二乘支持向量机（least squares-support vector machine,LS-SVM）判别模型,山楂缺陷的正确识别率为91.23%。然后,运用主成分分析（principal componentanalysis,PCA）进行10条敏感波段下单波段图像的数据压缩,分别采用＂sobel＂算子和区域生长算法＂Regiongrow＂识别出86个缺陷山楂样本的边缘与缺陷特征区域,得出单损伤、单虫害和损伤及虫害样本的识别率分别为95.65%,86.67%和100%。研究结果表明：采用高光谱成像技术可以对山楂的损伤、虫害、花萼和果梗进行定性分析和特征识别,该研究为山楂的缺陷无损检测提供了理论参考。

Hyperspectral imaging technology covered the range of 380-1 000 nm was employed to detect defects（bruise and insect damage）of hawthorn fruit.A total of 134 samples were collected,which included damage fruit of 46,pest fruit of 30,injure and pest fruit of 10 and intact fruit of 48.Because calyx·s-1tem-end and bruise/insect damage regions offered a similar appearance characteristic in RGB images,which could produce easily confusion between them.Hence,five types of defects including bruise,insect damage,sound,calyx,and stem-end were collected from 230 hawthorn fruits.After acquiring hyperspectral images of hawthorn fruits,the spectral data were extracted from region of interest（ROI）.Then,several pretreatment methods of standard normalized variate（SNV）,savitzky golay（SG）,median filter（MF）and multiplicative scatter correction（MSC）were used and partial least squares method（PLS）model was carried out to obtain the better performance.Accordingly to their results,SNV pretreatment methods assessed by PLS was viewed as best pretreatment method.Lastly,SNV was chosen as the pretreatment method.Spectral features of five different regions were combined with Regression coefficients（RCs）of partial least squares-discriminant analysis（PLS-DA）model was used to identify the important wavelengths and ten wavebands at 483,563,645,671,686,722,777,819,837 and 942nm were selected from all of the wavebands.Using Kennard-Stone algorithm,all kinds of samples were randomly divided into training set（173）and test set（57）according to the proportion of 3∶1.And then,least squares-support vector machine（LS-SVM）discriminate model was established by using the selected wavebands.The results showed that the discriminate accuracy of the method was 91.23%.In the other hand,images at ten important wavebands were executed to Principal component analysis（PCA）.Using ＂Sobel＂operator and region growing algrorithm ＂Regiongrow＂,the edge and defect feature of 86 Hawthorn could be recognized.Lastly,the detect precision of bruised,insect damage and twodefect samples is 95.65%,86.67% and 100%,respectively.This investigation demonstrated that hyperspectral imaging technology could detect the defects of bruise,insect damage,calyx,and stem-end in hawthorn fruit in qualitative analysis and feature detection.which provided a theoretical reference for the defects nondestructive detection of hawthorn fruit.