Development of a Recognition System for Alfalfa Leaf Diseases Based on Image Processing Technology

  • Feng Qin
  • Haiguang Wang
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 509)


To implement rapid identification and diagnosis of leaf diseases on alfalfa, an image-based recognition system was developed using the GUIDE platform under MATLAB software environment. An integrated segmentation method of K_median clustering algorithm and linear discriminant analysis was applied to implement the lesion image segmentation in this developed recognition system. A multinomial logistic regression model for disease recognition was built based on 21 color, shape and texture features selected by using correlation-based feature selection method. Using this system, disease image reading, image segmentation and lesion image recognition can be done. This system can be applied to conduct image recognition of four common kinds of leaf diseases on alfalfa including alfalfa Cercospora leaf spot, alfalfa rust, alfalfa common leaf spot and alfalfa Leptosphaerulina leaf spot. Some basis was provided for further development of image recognition system of various alfalfa diseases and for building a network-based automatic diagnosis system of alfalfa diseases.


Alfalfa leaf disease Image processing Image recognition GUIDE platform Multinomial logistic regression analysis 



This work was supported by Special Fund for Agro-scientific Research in the Public Interest of China (201303057).


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Copyright information

© IFIP International Federation for Information Processing 2019

Authors and Affiliations

  1. 1.Department of Plant PathologyChina Agricultural UniversityBeijingChina

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