Detection of Rice Exterior Quality based on Machine Vision

Abstract

To investigate the detection of rice exterior quality, a machine vision system was developed. The main characteristics of rice appearance including area, perimeter, roughness and minimum enclosing rectangle were calculated by image analysis. Least Squares Support Vector Machines, Naive Bayes Classifier and Back Propagation Artificial Neural Network were applied to achieve classification of head rice and broken rice, and the classification results of three algorithms were analyzed in detail. Genetic algorithm and Particle Swarm Optimization were used to obtain the optimal values of regularization parameter and kernel radial basis function parameter, and adopt a supervised learning approach to train the Least Squares Support Vector Machines model. Meanwhile the robustness of these classification methods was tested, and the results shows that support vector machine have better classification results in this experiment. This study demonstrated the feasibility of detection rice quality using machine vision. Keywords; Rice, Machine Vision, LS-SVM, Classification, Image Processing