摘要
现有植物病害图像检测方法存在检测病害单一的问题,因此,本文针对叶片的链格孢病、炭疽病、细菌性枯萎病、尾孢菌叶斑病4种病害和健康叶片,提出了基于核函数支持向量机的多分类检测方法。根据植物叶部病害图像具有多变的特点,首先通过受病叶片图像预处理增强病害部分与健康部分的对比度,使病害部分更加明显。然后在Lab彩色空间模型下的a、b分量上进行叶片分割并提取特征,采用K均值聚类方法,增强分割聚类效果。最后采用基于核函数的支持向量机多分类方法对4种病害进行检测识别并分类。为提高检测准确度,用500次迭代评估出最大精度,考虑交叉验证系数的影响,将样本的40%作为验证数据,60%作为训练数据,采用径向基核函数对其进行训练。该方法将传统的2种叶片病害识别扩大至4种,实验结果证实对4种病害的识别率最高达到89.5%,最低也达到了70%,证明了该方法的有效性。
The health of plants is directly related to the quality and quantity of agricultural products,therefore the disease detection of plants is an important research problem in agriculture.A multiclassification detection method based on kernel function support vector machine(SVM) was proposed for classification of healthy leaves and diseased leaves,and the detection of four diseases,including Alternaria alternata,Anthracnose,Bacterial Blight and Cercospora leaf spot.Because the image of diseased leaf was changeable,firstly,the contrast of diseased part and the healthy part was enhanced by the preprocessing,making the disease part more obvious.Then,leaf features were segmented and extracted on "a"and "b"component of the Lab color space.Using K-means clustering method,the clustering effect was enhanced.Finally,support vector machine(SVM) based on kernel function was used to identify and detect the four diseases.To improve the detection accuracy,500 iterations were used to assess the maximum precision.Considering the influence of the coefficient of cross validation,40% of the samples were used as validation data set,60% were used as the training data set.Radial basis kernel function was adopted to carry out the training.In this method,traditional two kinds of leaf disease identification was extended to four kinds,and the experimental results proved the effectiveness of leaf classification of four kinds of diseases.And the recognition rate of the 4 diseases was the highest,reaching 89.5%,and the lowest was 70%.
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2017年第S1期166-171,共6页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金项目(61472172
61673200)
山东省自然科学基金项目(ZR2016FM15
ZR2017MF062)
关键词
植物叶片
病害检测
特征提取
支持向量机多分类
plant leaves
disease detection
feature extraction
support vector machine multi-classification
