基于PCA-SVM的红枣缺陷识别方法

以干制红枣的黑斑、破头以及分类难度较高的干条3种病害图像作为研究对象,分别采用颜色矩和灰度共生矩阵提取颜色、纹理特征中的14维特征向量,然后采用主成分分析法对特征向量进行优化,得到4个主因素特征向量作为支持向量机输入。采用交叉算法确定最优支持向量机惩罚参数c和核函数参数g对支持向量机多分类模型进行训练,利用训练后的模型对红枣进行多分类试验。结果证明,该方法能够对红枣黑斑、破头和干条3种缺陷果进行快速准确的识别,识别率分别为93.3%,100.0%和96.6%,总识别率可达97.2%,且分类效率高。

Recognition method of jujube defects based on PCA-SVM

In this study,three kinds of disease images of jujubes,black spots,broken heads and dry strips with high classification difficulty were used as research materials.The color moment and gray level co-occurrence matrix were used to extract 14-dimensional eigenvectors of the color and texture features of jujube,and the principal component analysis method was used to optimize the features.Four principal factors of eigenvectors were obtained and then used as the input of support vector machine.The crossover algorithm was used to determine the optimal support vector machine penalty parameter c and kernel function parameter g,which was used as the parameter of the support vector machine multi-classification model to train the model.Using the trained model to perform multi-classification experiments on the jujube,the results proved that the three kinds of defects of jujube could recognized quickly and accurately,with the recognition rate at 93.3%,100.0%and 96.6%,respectively.The classification accuracy of this model for jujube defects could reach 97.2%,with high efficiency.