Computer Science ›› 2017, Vol. 44 ›› Issue (10): 302-306.doi: 10.11896/j.issn.1002-137X.2017.10.054

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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

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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