基于粒子群算法和支持向量机的黄花菜叶部病害识别

使用数字图像处理技术,以黄花菜叶部病害图像为识别对象,基于Lab空间和K-means聚类算法分割病害区域,提取目标区域的颜色特征、方向梯度直方图(histogram of oriented gradient,HOG)特征和形状特征,分别建立单一特征模型和特征融合模型,采用粒子群(particle swarm optimization,PSO)算法通过交叉验证优化支持向量机(support vector machine,SVM)模型的惩罚因子和核参数,建立基于PSO-SVM的多特征融合分类模型识别黄花菜病害。基于SVM的多特征融合分类模型识别率高于单一特征分类模型,识别率可达为81.67%;基于PSO-SVM多特征融合分类模型识别率高达92.39%。基于PSO-SVM的多特征分类模型识别率高,可以及时、便捷、高效地识别黄花菜病害。

Recognition of Hemerocallis citrina Leaf Disease Based on PSO and SVM

By using digital image processing technology, the disease image of Hemerocallis citrina leaf was taken as the recognition object. The disease area was segmented based on Lab space and K-means clustering algorithm, and the color characteristics, histogram of oriented gradient (HOG) and shape characteristics of the target areas were extracted from the images. The single-feature model and multi-feature model were established respectively based on the extracted features. Particle swarm optimization (PSO) algorithm was used to optimize the penalty factor and kernel parameter of the support vector machine (SVM) model through cross validation. Multi-feature classification model based on PSO-SVM was established to identify diseases of H. citrina leaves. The recognition rate of SVM based multi-feature classification model was higher than that of single-feature classification model, and the recognition rate could reach 81.67%. The recognition rate of multi-feature classification model based on PSO-SVM was as high as 92.39%. The multi-feature classification model based on PSO-SVM has high recognition rate and can identify the disease of H. citrina leaf timely, conveniently and efficiently.