中文摘要：

提出了一种基于空气耦合超声波技术的玉米种子虫蛀孔洞颗粒和完好颗粒分类识别方法。首先根据玉米颗粒的弹性模量、泊松比和密度等物理量计算出了玉米颗粒的声速，并根据检测精度需求设定了激励信号频率。然后采用MATLAB对采集的两类种子超声波信号数据进行分析处理，并分析了种子厚度和摆放方位对超声波响应特征的影响。最后建立了K近邻（KNN）、簇类独立软模式法（SIMCA）、Fisher线性判别（LDA）和决策树（DT）识别模型，并对模型性能进行了测试。结果表明：种子孔洞深度、胚部厚度和正反面方位不同，即超声波在种子表面的反射程度不同、在种子中传播声程不同，则超声波信号衰减程度不同，导致接收到信号的幅值不同，且样本点在主成分分析（PCA）特征空间的分布也不同。4种识别模型均可以实现对两类玉米的分类识别，其中KNN模型性能最佳，其对虫蛀孔洞颗粒和完好颗粒的正确识别率分别为98％，100％，误差带为2％，0.此结果说明采用空气耦合超声波技术可以实现对玉米种子虫蛀孔洞颗粒的检测。

英文摘要：

It is suggested to use a classication recognition method for worm damaged and intact particles, based on an air-coupled ultrasonic technology. Firstly, record the physical attributes of corn particles, such as elastic modulus, poisson＇s ratio and the density, which can then be used to calculate the ultrasonic velocity. With that information, determine the frequency of the excitation signal based on the needed detection precision. Secondly, use MATLAB to analyze the collected ultrasonic signal data of the two types of seeds and analyze the impact of the seed thickness and position on the ultrasonic response characteristics, l~＇om there, establish the four kinds of recognition models, including K nearest neighbor （KNN）, soft independent modeling of class analogy （SIMCA）, Fisher linear discriminant （LDA） and decision tree （DT）, and test the identification models performance. The results of this study clearly illustrate that the hole depth, embryo thickness and bearing of the corn different, the reflection degree of ultrasonic signal on the seed surface and its propagation path are different, then the ultrasonic signal attenuation degree, the amplitude of received signal and the distribution of sample points in the feature space are different. The four pattern recognition models all could identify both holed and intact particles, and the KNN model has the best performance, with an accuracy rate for damaged and intact particles of 98% and 100%, respectively and an error band of 2% and 0, respectively. The results prove that using the air-coupled ultrasonic technology makes it possible to accurately assess the moth-eaten corn seed testing.