基于二叉树支持向量机的小麦碰撞声分类研究

Study on the classification of wheat impact acoustic signals based on binary tree support vector machine

为了实现小麦颗粒的自动分类,采用二叉树和支持向量机相结合方法对小麦碰撞声进行识别分类.首先从时域和频域对小麦碰撞声信号进行分析和处理,提取信号特征,然后利用二叉树支持向量机分类器进行分类,实验结果表明,对小麦完好粒、虫害粒、霉变粒和发芽粒4类麦粒的识别均达到84.0%以上.该项研究具有较强的实用价值,为小麦的自动识别分类提供了一种可行方法.

In order to sort the wheat kernels into different types automatically,a novel approach which integrates binary tree and support vector machine（SVM） is proposed to discriminate between four different types of wheat kernels by impact acoustic signals.At first,the impact acoustic signals were analyzed and potential features were exacted from them in both time and frequency domains.Then the SVM based on binary tree was used for pattern recognition.Detection accuracy rates of the presented system for undamaged kernel,insect damage,moldy and sprout damage were above 84.0%.The experimental results show that our research has a high value on application and provides a feasible method for automatic classification of wheat kernels.