计算机科学 ›› 2017, Vol. 44 ›› Issue (10): 302-306, 311.doi: 10.11896/j.issn.1002-137X.2017.10.054

• 图形图像与模式识别 • 上一篇    下一篇

一种用于农作物叶部病害图像识别的双权重协同表示分类方法

杜海顺,蒋曼曼,王娟,王胜   

  1. 河南大学图像处理与模式识别研究所 开封475004,河南大学图像处理与模式识别研究所 开封475004,河南大学图像处理与模式识别研究所 开封475004,河南大学图像处理与模式识别研究所 开封475004
  • 出版日期:2018-12-01 发布日期:2018-12-01
  • 基金资助:
    本文受国家自然科学基金委-河南人才联合基金项目(U1504621),河南省教育厅科学技术研究重点项目(15A413009)资助

Double Weighted Collaborative Representation Based Classification for Crop Leaf Disease Image Recognition

DU Hai-shun, JIANG Man-man, WANG Juan and WANG Sheng   

  • Online:2018-12-01 Published:2018-12-01

摘要: 农作物病害是我国主要的农业灾害之一,准确识别病害类型是防治农作物病害的关键。因此,首先采集了小麦、玉米、花生、棉花4种农作物的22种常见叶部病害的441张图像;然后,在对每张病害图像中的叶片和病斑进行分割的基础上,分别提取了描述农作物种类的叶片特征参数和描述病害类型的病斑特征参数;其次,将这两类特征参数组合并作归一化处理,得到病害图像的数据特征向量;再次,采用所有病害图像的数据特征向量,构建了一个农作物叶部病害数据集;最后,在同时考虑数据特征重要性和数据空间局部性的基础上,提出了一种双权重协同表示分类(DWCRC)方法并将其用于农作物叶部病害识别。在农作物叶部病害数据集上的实验结果表明,提出的双权重协同表示分类方法在用于农作物叶部病害识别时具有较高的识别率。

关键词: 特征提取,协同表示,双权重协同表示分类,农作物叶部病害,图像识别

Abstract: Crop disease is one of the main agriculture disasters in our country.It is critical to prevent and control crop disease to recognize the category of crop disease.In this paper,we acquired 441 images composed of 22 kinds of crop leaf disease images of wheat,maize,peanut,and cotton.For each crop leaf disease image,we extracted its leaf and disease spot features after the leaf and disease spot have been segmented out,respectively.Furthermore,we combined the leaf and disease spot features into a feature vector,and then normalized the feature vector by max-min normalization operation.Using the feature vectors of all crop leaf disease images,we constructed a crop leaf disease dataset.By considering both the importance of data features and the data locality,we proposed a double weighted collaborative representation-based classification (DWCRC) method for crop leaf disease recognition.Experimental results on the crop leaf disease dataset show that DWCRC is more effective than the state-of-the-art methods for crop leaf disease recognition.

Key words: Feature extraction,Collaborative representation,Double weighted collaborative representation-based classification,Crop leaf disease,Image recognition

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