Image-based Extraction of Characteristic Value of Pathological Leaf Surface  

基于图像的植物病变叶面特征值提取研究(英文)

作　　者：程鹏飞[1] 周春娥[2] 刘静香[1] 

机构地区：[1]河南机电高等专科学校机电工程系,河南新乡453002 [2]河南师范大学生命科学学院,河南新乡453007

出　　处：《Plant Diseases and Pests》2010年第5期18-20,25,共4页植物病虫害研究（英文版）

基　　金：Supported by Natural Science Foundation in Education Department of Henan Province(2008B210001)~~

摘　　要：[ Objective] Computer image processing technology was used to distinguish the angular leaf spot and spotted disease in the agricultural production. [Method] The computer vision technology was used to carry out chromatic research on the plant pathological characteristics. The color and texture were taken as the plant disease image characteristic parameter to extract the perimeter, area and the shape of the lesion image, thus carrying out the classification judgment on the disease image. [ Result] C IE1976H IS chorma percentage histogram method was adopted to extract chromaticity characteristic parameters, the process was simple and effective with fast operation speed, eliminating the effect of leaf size and shape. The statistical characteristic parameter of chorma histogram was analyzed to obtain chroma skewness, which could significantly distinguish different symptoms of disease. [ Conclusion] The study suggested that chroma skewness could be adopted as the characteristic parameter to distinguish spotted disease with angular leaf spot.[ Objective] Computer image processing technology was used to distinguish the angular leaf spot and spotted disease in the agricultural production. [Method] The computer vision technology was used to carry out chromatic research on the plant pathological characteristics. The color and texture were taken as the plant disease image characteristic parameter to extract the perimeter, area and the shape of the lesion image, thus carrying out the classification judgment on the disease image. [ Result] C IE1976H IS chorma percentage histogram method was adopted to extract chromaticity characteristic parameters, the process was simple and effective with fast operation speed, eliminating the effect of leaf size and shape. The statistical characteristic parameter of chorma histogram was analyzed to obtain chroma skewness, which could significantly distinguish different symptoms of disease. [ Conclusion] The study suggested that chroma skewness could be adopted as the characteristic parameter to distinguish spotted disease with angular leaf spot.

关 键 词：Image processing Contour following Plant disease Characteristic value extraction CHROMA 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]