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基于随机森林方法的小麦叶片病害识别研究

  

  1. 郑州轻工业学院计算机与通信工程学院,河南 郑州 450001
  • 出版日期:2018-02-28 发布日期:2018-02-06
  • 基金资助:
    国家自然科学基金项目(61302118,81501547)

Identification of Wheat Leaf Disease Based on Random Forest Method

  1. College of Computer and Communication Engineering, Zhenzhou University of Light Industry, Zhengzhou Henan 450001, China
  • Online:2018-02-28 Published:2018-02-06

摘要: 为了提高小麦叶部病害的识别准确率,采用高斯混合模型结合EM 算法对小麦叶
片进行提取,获得较大目标,使得分割准确率比直接分割病害区域有所提高,同时降低了分割
难度。并结合HSV 主颜色直方图和通过Tamura 纹理特征中的粗糙度、方向度和对比度作为特
征进行筛选,采用随机森林方法对小麦健康叶片、白粉病、叶枯病和叶锈病图像进行了识别,
整体识别准确率可达95%。通过实验验证,该方法是有效可行的,并优于同等条件下的支持向
量机(SVM)方法。

 

关键词: 高斯混合模型, EM 算法, HSV 主颜色直方图, 纹理特征, 支持向量机

Abstract: In order to improve the recognition accuracy of wheat leaf disease, the Gaussian mixture
model combined with EM algorithm was used to extract the wheat leaves and obtain the bigger target,
which made the segmentation accuracy higher than the direct segmentation disease area. And the
roughness, the degree of orientation and the contrast were selected by combining the HSV main color
histogram and the Tamura texture feature. The images of wheat healthy leaves, powdery mildew, leaf
blight and leaf rust were identified by random forest method and recognition accuracy is up to 95%.
Experiments show that this method is effective and superior to the support vector machine (SVM)
method under the same conditions.

Key words: Gaussian mixture model, EM algorithm, HSV main color histogram, texture feature, support vector machine