Methodology For Rapid And Nondestructive Detection Of Fruit Quality Based On Image Processing And Spectral Analysis Technologies

Since current methods for detecting and grading Kuerle fragrant pear are behindhand, inefficient and destructive for its internal quality,this study aimed to develop nondestructive methods to determine the external and internal quality(sorts,weight,sugar content,fruit stalks, shape and so on) of Kuerle fragrant pear,comprehensively utilizing the knowledge of image processing technologies,spectroscopy analysis technologies,hyperspectral imaging technologies,pattern recognition,chemometrics,optics and physiology of fruit trees and so on. On the basis of the above study,rapid detection methods and models for fruit quality were established,which will provide theoretical support for developing devices of online nondestructive detection of Kuerle fragrant pear.Moreover,the research findings in this study can be used for other similar fruits.Main contents and conclusions of this study were listed as follows:(1) The discriminant analysis of the detaching calyx fruit and persistent calyx fruit were carried out by using image processing technologies.Characteristics of calyx that located at the bottom of pears were extracted by using image processing technologies,and their roundness was calculated.Thereafter,the discriminant analysis of detaching calyx fruits and persistent calyx fruits was carried out according to the threshold which was determined by statistical properties.The results indicated that the recognition accuracy for detaching calyx fruit and persistent calyx fruit were 79.7%and 85.9%.The recognition accuracy for all samples of fragrant pears was 82.8%.It can be concluded that the detaching calyx fruit and persistent calyx fruit can be classified based on image processing technologies.(2) The discriminant analysis of detaching calyx fruit and persistent calyx fruit was carried out based on NIR spectroscopy.The performances of models for qualitative analysis were compared by applying discriminant analysis(DA) and SIMCA methods with different bands and different preprocessing methods.The result indicated that the DA model,using NIR analysis technique(800-2500 nm) and DA method combined with the scope of the original spectrum(9091～4000 cm^-1) was optimal.The classification accuracy of the calibration set was 100%,and the accuracy of prediction set was 95%.When the SIMCA method and NIR-diffuse reflectance spectra(9091～4000 cm^-1) were adopted in the qualitative analysis of detaching calyx fruit and persistent calyx fruit,the accuracy of classification of correction set was 100%for the model that established with the original spectrum,and the accuracy of classification of prediction set was 70%.Accordingly,in the discriminant of detaching calyx fruit and persistent calyx fruit of Kuerle fragrant pear,the calibration model built by DA method had higher prediction accuracy than that was built by SIMCA method.The results indicate that the detaching calyx fruit and persistent calyx fruit can be classified based on NIR spectroscopy technologies.(3) The extraction methods for fruit stalk images of Kuerle fragrant pear was studied by using imaging processing technologies.RGB color models were used for analysis of the gray scale distribution of RGB component and the background were segmented by R-B color factor.After background segmentation of fragrant pears' images and edge detection,The edge images of the fragrant pear were acquired after background segmentation and edge detection,and then a new method based on mathematical morphology was proposed for automatic extraction of stalks of fragrant pears.The experimental results showed that the accuracy rate of this method for extracting stalks of fragrant pears was 90.7%.It can be concluded that the fruit stalk of Kuerle fragrant pear can be extracted automaticly based on image processing technologies.(4) The quantitative and qualitative analysis for sugar content of Kuerle fragrant pears was carried out based on NIR spectroscopy.The performances of models developed by partial least squares regression(PLSR) and principal component regression(PCR) for sugar content were compared coupled with different bands and different preprocessing methods. The performance of PLSR was obviously better than that of PCR.The results of the study showed that when PLSR model was applied,in the range of 12400～4000 cm^-1,with seven principal component factors,the optimal correction model could be generated with the original spectra corrected by MSC.The correlation coefficient of the model,r_cal,was 0.964 and the RMSEC of calibration set was 0.518.The RMSEP was 0.324.The correction model established with standard of classification for Kuerle fragrant pear(NY/T 585-2002 ) was used.The results showed that overall correct classification rate of high-grade,grade 1, grade 2 and low-grade were 86.36%,61.54%,88.89%,and 80%,respectively.The results indicate that the sugar content of Kuerle fragrant pear can be determinated based on NIR spectroscopy technologies.(5) The prediction model for weight of fruits,which were based on image processing,was studied.The area of side projection and top projection were acquired using image processing technologies.The regression equation,(?)=-47.3213+2.474232x₁+3.03103x₂, was established on the basis of relationship between side & top projection area and the weight,and the coefficient of determination was 0.975.The fruits were determined using prediction samples.The average prediction error of weights was 2.74,and the rate of weight classification for Kuerle fragrant pear was 91.30%.It can be concluded that the weight of Kuerle fragrant pear can be predicted based on image processing technologies.(6) The study on recognition methods of fruit shape based on digital image processing was described.After the process of image preprocessing and background segmentation,images of fruit stalks were obtained by morphology strategies,and therefore images of fragrant pear were acquired through application of image subtraction.On the basis of proper extraction of fruit edges,four different methods,using discrete index,artificial neural networks(ANN),support vector machines(SVM),and fuzzy C-means(FCM) clustering algorithm,were compared for classification of fruit shapes.Identification results were as follows:the accuracy of fruits' shape recognition based on discrete index was 88.28%. When discrete index and roundness were used as input variables in BP neural network and the number of hidden layer nodes was 1,results showed the accuracy recognition for training set was 90.91%when TRAINRP train function was used in BP network,and the accuracy recognition for prediction set was 89.66%.When LS-SVM was used for classification and recognition of fruit shapes,results showed that the accuracy recognition for training set was 91.92%,and the accuracy recognition for prediction set was 89.66%.In the case of unsupervised FCM clustering,the total recognition rate of fruits was 86.72%. Results of this study on recognition methods of Kuerle fragrant pear showed that performance of model which was based on LS-SVM was optimal among the recognition methods of fruits' shape.The results indicate that the shape of Kuerle fragrant pear can be identified based on image processing technologies.(7) An experimental platform of hyperspectral imaging for detection of fruit quality was established(400-1000 nm),and the research method for testing sugar content of Kuerle fragrant pear was developed.In the spectral range of 422～982 nm,the influence of different preprocessing methods on performance of the model,which was established by PLSR,were evaluated.By comparison,the relation coefficient of models,which were treated by first-order differential,standard normal variate(SNV),Norries filter and other algorithms,were much higher than that of model of original spectrum.When models were preprocessed by second-order differential,Norris filter and SNV,the coefficient of relation was increased from 0.803 to 0.898,and the RMSEP was decreased from 0.644 to 0.596. The RMSECV of model was decreased from 0.720 to 0.704.It indicated that this model was better than that established with original spectrum.After comprehensive comparison, the corrected model,which was treated with normalization,second-order differential and Norris filter,was optimal.The result of this study showed that soluble solid content of Kuerle fragrant pear could be predicted by hyperspectral imaging technologies.It can be concluded the sugar eontent of Kuerle fragrant pear can be predicted based on hyperspectral imaging technologies.