Wu Chunyan, Wang Xuefeng
Research Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing, China.
PLoS One. 2017 Jul 27;12(7):e0181537. doi: 10.1371/journal.pone.0181537. eCollection 2017.
This paper presents a survey on a system that uses digital image processing techniques to identify anthracnose and powdery mildew diseases of sandalwood from digital images. Our main objective is researching the most suitable identification technology for the anthracnose and powdery mildew diseases of the sandalwood leaf, which provides algorithmic support for the real-time machine judgment of the health status and disease level of sandalwood. We conducted real-time monitoring of Hainan sandalwood leaves with varying severity levels of anthracnose and powdery mildew beginning in March 2014. We used image segmentation, feature extraction and digital image classification and recognition technology to carry out a comparative experimental study for the image analysis of powdery mildew, anthracnose disease and healthy leaves in the field. Performing the actual test for a large number of diseased leaves pointed to three conclusions: (1) Distinguishing effects of BP (Back Propagation) neural network method, in all kinds of classical methods, for sandalwood leaf anthracnose and powdery mildew disease are relatively good; the size of the lesion areas were closest to the actual. (2) The differences between two diseases can be shown well by the shape feature, color feature and texture feature of the disease image. (3) Identifying and diagnosing the diseased leaves have ideal results by SVM, which is based on radial basis kernel function. The identification rate of the anthracnose and healthy leaves was 92% respectively, and that of powdery mildew was 84%. Disease identification technology lays the foundation for remote monitoring disease diagnosis, preparing for remote transmission of the disease images, which is a very good guide and reference for further research of the disease identification and diagnosis system in sandalwood and other species of trees.
本文介绍了一个利用数字图像处理技术从数字图像中识别檀香炭疽病和白粉病的系统的研究。我们的主要目标是研究最适合檀香叶片炭疽病和白粉病的识别技术，为檀香健康状况和病害程度的实时机器判断提供算法支持。从2014年3月开始，我们对不同严重程度的炭疽病和白粉病的海南檀香树叶进行了实时监测。我们使用图像分割、特征提取以及数字图像分类与识别技术，对田间白粉病、炭疽病和健康叶片的图像分析进行了对比实验研究。对大量病叶进行实际测试得出了三个结论：（1）在各种经典方法中，BP（反向传播）神经网络方法对檀香树叶炭疽病和白粉病的区分效果相对较好；病变区域大小最接近实际情况。（2）病害图像的形状特征、颜色特征和纹理特征能够很好地显示两种病害之间的差异。（3）基于径向基核函数的支持向量机（SVM）对病叶的识别和诊断效果理想。炭疽病叶与健康叶的识别率分别为92%，白粉病叶的识别率为84%。病害识别技术为远程监测病害诊断奠定了基础，为病害图像的远程传输做好了准备，对檀香及其他树种病害识别与诊断系统的进一步研究具有很好的指导和参考意义。
