Research On Rice Kernels' Sorting Based On Machine Vision

China is the world's largest rice producer and consumer countries, but not among the major rice exporting countries. One of the main reasons is technological backwardness of China's rice quality inspection, which can not guarantee the quality of export rice and make China's exporting rice lack of international competitiveness. In this paper, japonica rice planted in Jiangsu, for example, were researched based on machine vision technology. According to practical problems existed in the process of rice kernels processing and sorting,static and dynamic rice kernels image capturing machine vision system was constructed, image processing algorithms was given to detect rice appearance quality on-line. Experiments were done to analysis relevance of rice's internal and external quality and verify the appearance quality sorting of rice affected its taste quality. According to characteristics of rice appearance quality, rice kernels grading methods were given, which laid the foundation of developing commercial rice automated grading system. The main contents and conclusions of this paper were as follows:1. Machine vision inspection system of rice appearance quality was constructed to achieve multi-kernel rice color image in the static and dynamic state, through color correction and geometric calibration. The appearance features of rice kernels image was described and the definitions of sound and whole rice, chalky rice, broken rice, yellow rice, and off-type rice were given. According to rice kernels mix mode encountered at processing of rice grading, three kinds of image like all the sound and whole rice image,all the chalky rice image and five classes of rice mixture image were captured to research processing algorithms of multi-kernels color image.2. A method based on improved ultimate measurement accuracy (UMA) was proposed to evaluate color image segmentation performance. Getting the segmentation edge contour gray image removed the background; the gray mean and variance were calculated from this and made as the segmentation judging criterion. Three kinds of color rice sample image were researched on this criterion to select the segmentation method and segmentation color band. The experiments verified that image segmentation using the maximum difference between-cluster with I1 color band can obtain good results. A chalky segmentation algorithm based on Chebyshev approximation was given. Using this method, three kinds of rice samples images were segmented and extracted chalky area. The results showed that this method was time-saving and robust, realized the chalky zone automatic accurate segmentation. The chalky region segmentation and area calculation on rice both sides tested the feasibility of the monocular application.3. An algorithm extracting rice kernels' size and shape features based on Hotelling transform (HT) was given. The algorithm of minimum enclosing rectangle (MER) widely used at present was improved and compared with HT algorithm at time-consuming and accuracy. Indica rice ratio of length and width planted in Jiangsu province was measured with the above two method. The results showed that the calculation time-consuming percent kernel of improved MER method is 267ms and error is 2.24%, while that of HT method is 31ms and error is 1.65%.The HT method extracted rice kernel image features of size and shape at good real-time and high accuracy.4. Five kinds of Japonica rice planted in Jiangsu province such as Wu Xiang japonica 14, Huai Rice 5, Xu Rice 3, Ning japonica Rice 1 and Xu Rice 4 were selected to research. 3750 rice kernels were selected randomly, which 150 sound and whole rice kernels,150 chalky rice kernels,150 broken rice kernels and 150 off-type rice kernels from every kind of rice,150 yellow rice kernels from all.250 static color images were captured. The algorithms given in this paper were used to extract 9 size features,10 morphological features and 31 color features.5. The method of evaluating rice appearance quality based on multi-structure neural network was researched in this paper. Principal component analysis of size, morphological, and color features gave such four neural network inputs as area, kernel ratio of length to width, chalky area and H value. Primary training of the networks indicated that 5×(4-4-5-1) network was most suitable for the rice grading. The structure of 4-4-5-1 represented those four inputs, four neurons in the first hidden layer, five neurons in the second layer and one in the output layer. The performance of the MSNN classifier was compared against the performance of a multi-layer BP neural network (MLNN) classifier with a similar network complexity. It showed that the accuracy was 98.3% for sound and whole rice,92.4% for chalky rice,97.5% for broken rice,96% for yellow rice and 93%for off-type rice. On the average the MSNN classifier had 6.4% higher recognition accuracy of and shorter training time than the MLNN classifier.6.Rice video images were captured at speed of 0.08m/s,0.12m/s,0.16m/s and 0.2m/s.Rice dynamic image inspection method based on improved background subtraction was researched and it was realized that background automatic extraction, rice kernels segmentation, rice tracking and kernels' features extraction. Compared features extracted at different speed with the static features,0.12m/s speed was chose for the best suitable speed in terms of dynamic deviation and the relative error. Grading rice using MSNN classifer, the accuracy was 95.2% for sound and whole rice,89.6% for chalky rice,97.3% for broken rice,90.5% for yellow rice and 82.3% for off-type rice. The way of algorithm optimization and acceleration is also given.7. The quality relevance of rice inside and outside was analysized by two group experiments. It showed that Japonica rice taste quality planted in Jiangsu province is related to gel consistency (GC), chalkiness, amylose content (AC) and the moisture content. Protein content (PC) is related to rice length and ratio of length to width. To a single kind of rice, the smaller rice kernels' chalky area is, the higher GC and PC is; the bigger chalky area is, the higher AC is. The relevant experiment of single kind rice and variance analysis showed that the appearance quality of single kind rice such as length and chalky area has a significant influence on the internal quality such as AC and GC. So removing broken rice and chalky rice by sorting can improve rice taste quality.