Study On Auto-classification For Korla Fragrant Pear Of The Classification Of Fruit Shape And Mass Predicting

Since current methods for classification and analysis of fruit shape and mass for Korla fragrant pear are behindhand, this study aimed to develop feasible methods to determine the classification of Korla fragrant pear shape and models for the mass predicting. The shape characters and geometrical parameters are determined using image processing methods. The discriminate on normal and misshapen fruit shape, the classification of normal fruit shape, models for mass prediction of Korla fragrant pears based on these geometrical parameters are identified. All above can supply basis for the research of mass classification using machine vision method. Main contents and conclusions of this study were listed as follows:The study of discriminate of normal and misshapen fruit shape based on the integrated neural network ensemble (InNNE) based on clustering technology. The 20 shape characters are determined by the straight-line divided method from the edge of Korla fragrant pear, which are the input layer of the BP neural network. The activation function of output layer are the logarithm and linear on, which include 3 nodes.. It can be concluded that the average accuracy is about 80%.The study on classifying for normal fruit shape of Korla fragrant pear:three normal fruit shapes in Korla fragrant pear based on the A.R are sub orbicular, ovoid and spindle. The A.R of sub orbicular pear range from 0.98 to 1.14, and for ovoid and spindle pear is 1.15 to 1.27 and 1.28 to 1.39.The study on predicting models of the mass for Korla pear:Different linear and nonlinear regression models for mass prediction of Korla fragrant pears based on these geometrical parameters were identified. The results indicated that among the mass prediction models of Korla fragrant pear based on diameters, the linear model with the independent variables as major and intermediate diameter (or minor diameter) had higher correlation. Also, among the mass prediction models based on the projected area, the polynomial model with the independent variable as the projected area which includes the major diameter can be optimum. The results showed that it is feasible to predict the mass of Korla fragrant pear use the model M=0.0079Pab1.197, R2= 0.9899, which is more feasible.The development of automatic classification software for Korla fragrant pear:the Object oriented programming method is used to design the automatic classification software for Korla fragrant pear based on MATLAB, All the modules realized the discriminate of normal and misshapen fruit shape, classifying of the normal fruit shape and mass predicting.