| In recent years, machine vision technology has been developed in all sectors rapidly. Particularly, its superiority in the detection of food and agricultural products is outstanding. Soybean as a major agricultural product has provoked an increasing emphasis. However, the detection of soybean, classification remains at a level of artificial. Therefore, application of machine vision technology to detect soybean quality is of important significance.In this study, soybean feature extraction system was constructed based on machine vision. Firstly, choosing image pre-processing algorithm. Secondly, extracting soybean shape and color characteristics. Finally, using soybean features to construct neural network and detecting soybean quality as a preliminary exploration. The main conclusions were obtained as following.1. In this study conditions, lighting way was checked out through testing different ways. Finally, forward light source was selected. In the same light source and lighting way, black background was selected from white, blue, red, yellow, black ones. Series of image preprocessing, such as transformations geometric, color-transformation, image enhancement, morphological processing, and image segmentation and so on, were analyzed and compared. Median filter could significantly lower soybeans image noises. In the case of grey-scale changes in small value, well smoothing effect could be achieved. Compared to morphology algorithm, watershed algorithm could achieve better effect. Application of bimodal algorithm and growth algorithm combining algorithm, background and objective beans were separated, removed most of the noises, and single beans was obtained. Then objective image was obtained.2. 29 morphology features of soybean were defined and extracted. The algorithm could extract multiple features of soybeans at the same time; the efficiency of soybeans feature extraction has been greatly improved. Structural characteristics and design parameter of BP artificial neural network were analyzed. BP artificial neural network structure of was designed using MATLAB software. Compared with kinds of optimization functions by experimental analysis, Levenberg-Marquardt function was selected as training parameter. Nodal points of hidden layer were selected to be 10 for one kind of defective soybeans detecting, which was selected to be 23 for all kinds of defective soybeans detecting.3. Feature Selection Optimized. 29 characteristics were analyzed with LOGISTIC Regression and CORR analytical methods using SAS software. According to different requirements, different feature were selected .In terms of training results it was known that the effect of defective identification by the method of neural network was good to respectively detect different defects. The rate of immature beans detection was 100%, and others were above 95%, except broken was 81%. In one-time identification of a number of defective beans study, after SAS analyzing, 10 features were obtained to construct neural network to do the detection. The good one's detection rate was 99%, and the rate of mistaking good ones for defect ones was 1%. The detection rate of moth-eaten beans, mildew beans, broken beans, immature beans and beans with spots caused by Bacteria were 40%,51%,42%,96%,87%,respectively. The rate of mistaking defect ones for good ones was 0.2%.4. Based on regression analysis, using SAS, Mathematical equations between appearance and nutritional quality had been developed as following: Protein: x1 0, x1 2, x 25, x 26 were aspect ratio, roundness likeness, Standard deviation, B Standard deviation, respectively. Oil: x1 , x1 5, x1 8 were area average value Standard deviation, respectively. |
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