基于机器视觉的核桃仁动态分级研究

Study on Dynamic Grading Method of Walnut Kernel Based on Machine Vision

【目的】基于机器视觉技术研究出一种适合新疆核桃仁动态分级处理的方法。【方法】利用实时采集且已经完成图像预处理的样品核桃图像得到核桃仁特征集合,运用mRMR特征选择算法筛选原始特征集并对特征的重要性进行排列,通过对支持向量机、决策树和朴素贝叶斯三种机器学习算法进行模型训练和测试,得出最佳分级方法,设计核桃仁自动追踪方法和动态分级流程,构建的核桃仁自动分级系统。【结果】在使用特征bin19、K1和bin15训练朴素贝叶斯分类器时,核桃仁的分级正确率达到最大为97.33%,在动态条件下运用构建的核桃仁自动分级系统对150个核桃仁进行分级测试,正确率为81.33%。【结论】基于机器视觉研究出的核桃仁特征提取与分级方法、核桃仁动态分级处理动作方法,可以有效完成对核桃颜色和完整度的分级。

【Objective】The purpose of this project is to study a kind of dynamic grading method of walnut kernel based on machine vision technology that will be suitable for Xinjiang.【Method】Walnut kernel feature set was obtained from walnut image collected in real time and completed image preprocessing.Then mRMR feature selection algorithm was used to filter the original feature set and arrange the importance of the feature.Finally,support vector machine was used to analyze the importance of the feature.Three machine learning algorithms,decision tree and naive Bayes,were trained and tested,and the optimal classification method was obtained.Finally,the automatic tracking method and dynamic grading process of walnut kernel were designed,and the automatic classification system of walnut kernel was constructed.【Result】When using feature bin19,K1 and bin15 to train naive Bayesian classifier,the classification accuracy rate of walnut kernel classification was 97.33%.Under the dynamic condition,the walnut kernel automatic grading system was used to classify 150 walnut kernel,and the overall accuracy rate was 81.33%.【Conclusion】Based on the feature extraction and grading method of walnut kernel developed by machine vision,the method of walnut kernel dynamic grading can effectively complete the classification task of walnut color and integrity.