基于图像分析的橘科植物病害识别技术

Identification of the Orange Plant Disease Based on the Analysis of the Image

为实现橘科植物病害的计算机识别和病害程度的科学评价,提出通过分析病害图像,自动提取有效特征,设计分类器模型识别的方法.深入研究了怎样对病害图像进行自动增强处理、病斑分割、特征提取,以及怎样构建分类器模型等技术.最后以常见也容易混淆的五种柠檬病害为例,提取其病斑色调、纹理、形态三种特征向量,分别采用支持向量机和BP神经网络进行训练、测试.实验结果表明,该方法能很好识别植物病害类别,为科学防治和病害危害程度评价提供科学依据.

To achieve the computer identification of orange secco plant disease and the scientific evaluation of disease levels,the paper proposed a identification method through the analysis of disease image, automatic extract the effective feature, design classifier model.In the paper method was studied how to enhancement processing the diseases of image, segmentat disease spot, extract feature, and Construct classifier model, etc. Then for example five of confusion between the diseases, extracting the disease spots the tone and texture, shape characteristics, after optimization respectively by using support vector machine （SVM） and BP neural network to identify disease categories. The experimental results show that this method can be a very good recognition plant disease categories for scientific control and give a scientific evaluation for the plant disease harm degree.