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采用计算机视觉进行棉花虫害程度的自动测定
引用本文:陈佳娟,纪寿文,李娟,赵学笃.采用计算机视觉进行棉花虫害程度的自动测定[J].农业工程学报,2001,17(2):157-160.
作者姓名:陈佳娟  纪寿文  李娟  赵学笃
作者单位:1. 山东莱阳农学院,
2. 吉林大学南岭校区,
摘    要:采用计算机视觉技术,根据棉花叶片的孔洞及叶片边缘的残缺,来测定棉花虫害的受害程度。该方法应用局部门限法完成图像与背景的分割;用高斯拉普拉斯算子,进行棉花图像的边缘检测;利用边缘跟踪算法确定棉叶中的孔洞;利用膨胀算法确定叶片边缘的残缺。实验结果表明,该方法可有效的测定棉花虫害的受害程度,其测定误差小于0.05。

关 键 词:计算机视觉    边缘检测    膨胀算法    棉花虫害    受害程度
文章编号:1002-6819(2001)02-0157-04
收稿时间:6/5/2000 12:00:00 AM
修稿时间:2000年6月5日

Automatic Measurement of Danger Degree of Cotton Insect Pests Using Computer Vision
Chen Jiajuan,Ji Shouwen,Li Juan and Zhao Xuedu.Automatic Measurement of Danger Degree of Cotton Insect Pests Using Computer Vision[J].Transactions of the Chinese Society of Agricultural Engineering,2001,17(2):157-160.
Authors:Chen Jiajuan  Ji Shouwen  Li Juan and Zhao Xuedu
Abstract:In this paper, by using computer vision technology, danger degree of cotton insect pests was automatically measured based on inside hole and irregular edge of cotton leaves. The local threshold algorithm was used to segment the cotton leaf from the background and the LOG algorithm was applied to recognize the edge of cotton leaf. The deformity of leaf edge was certified by using dilatation algorithm, and the hole of the cotton leaf was determined by using the edge tracking algorithm. The experiment results showed that the method can effectively judge the danger degree of insect pests of cotton, and the measurement error of danger degree was less than 0.05.
Keywords:computer vision  edge detection  dilatation algorithm  cotton insect pests  danger degree
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