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农学学报 ›› 2014, Vol. 4 ›› Issue (6): 101-106.

• 农业工程 农业机械 生物技术 食品科学 • 上一篇    下一篇

基于SVM的高粱叶片病斑图像自动分割提取方法研究

白文斌 白帆 贺文文 王伟仁 程彦俊 刘璋   

  • 收稿日期:2013-11-10 修回日期:2013-12-16 出版日期:2014-06-20 发布日期:2014-06-20
  • 基金资助:
    山西省农业科学院科技攻关项目;山西省科技攻关项目;山西省农业科学院重点科技攻关项目

A Method of Sorghum Leaf Disease for Image Automatic Segmentation and Extraction Based on the SVM

  • Received:2013-11-10 Revised:2013-12-16 Online:2014-06-20 Published:2014-06-20

摘要: 为实现高粱叶片病斑的自动化无损监测,利用支持向量机(SVM)技术对高粱叶片病斑图像进行自动分割提取研究。结果表明,通过选取RGB、HIS和Lab 3种颜色空间的颜色特征值可以消除对作物病斑拍照时产生的光照、亮度等影响。在MATLAB软件环境下调用LIBSVM软件对病斑图片中的病斑图像像素点和背景图像像素点建立支持向量机分类模型,可以实现对病斑的高效分割和高质量提取。分割提取效果与人眼识别的病斑图像高度吻合。如果利用大量采集的病斑图像进行模型训练,就可以真正实现完全自动化的病斑分割、提取和判别。因此,该研究对建立完全自动化的作物病斑图像识别系统意义重大。

关键词: 山楂, 山楂, 杂交育种, 授粉

Abstract: In order to achieve the automated nondestructive monitoring of sorghum leaf disease spot, the author uses support vector machine (SVM) technology to research automatic segmentation and extraction of sorghum leaf disease spot image. The results showed that selecting the color feature values of the 3 kinds of color spaces (RGB, HIS and Lab) could eliminate the influence of the light brightness when you took a photo. In the MATLAB software environment using LIBSVM software to establish support vector machine (SVM) classification model of disease spot image pixels and background image pixels, could implement disease spot image efficient segmentation and high-quality extraction. The disease spot image which was extracted automatically by programs could closely match the recognition by the human eye. If using a large number of sampled disease spot image to train model, could achieve the disease spot image fully automated segmentation, extraction and determination. So this research has very important significance to build fully automated crop disease spot image recognition system.

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