Study on Corn Disease Identification Based on PCA and SVM

机译：基于PCA和SVM的玉米病害鉴定研究。

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In this paper, corn gray leaf spot, corn rust, corn big spot and healthy corn leaves were studied. In the process of image background segmentation, Otsu method, OpenCV morphological operation and morphological transformation method are used to outline the outline of the object, and then create a mask. Using the outline, the difference set between the corn leaf and the background is taken to get a complete corn leaf image. PCA and SVM are applied to the processed image. When the penalty parameter C of SVM is 100 and the kernel is linear, the classification accuracy of four kinds of diseases is 90.05%, 92.64%, 91.23% and 95.78% respectively.

机译：本文研究了玉米灰叶斑，玉米锈斑，玉米大斑和健康玉米叶。在图像背景分割的过程中，使用Otsu方法，OpenCV形态学运算和形态变换方法勾勒出对象的轮廓，然后创建蒙版。使用轮廓线，获取玉米叶和背景之间的差异集，以获得完整的玉米叶图像。 PCA和SVM应用于处理后的图像。当SVM的惩罚参数C为100且核为线性时，四种疾病的分类准确率分别为90.05％，92.64％，91.23％和95.78％。

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《IEEE Information Technology, Networking, Electronic and Automation Control Conference》|2020年|661-664|共4页
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Zixi Liu; Zhixiong Du; Yue Peng; Ming Tong; Xiaoyu Liu; Weicong Chen;
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Support vector machines; Principal component analysis; Diseases; Image segmentation; Heating systems; Kernel; Image color analysis;

机译：支持向量机;主成分分析;疾病;图像分割;加热系统;内核;图像色彩分析;

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Study on Corn Disease Identification Based on PCA and SVM

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