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

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Computer and Computing Technologies in Agriculture X (CCTA 2016)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 509))

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Abstract

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.

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Acknowledgments

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

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Authors and Affiliations

  1. Department of Plant Pathology, China Agricultural University, Beijing, 100193, China

    Feng Qin & Haiguang Wang

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  1. Feng Qin
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  2. Haiguang Wang
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Correspondence to Haiguang Wang .

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Editors and Affiliations

  1. China Agricultural University, Beijing, China

    Daoliang Li

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Qin, F., Wang, H. (2019). Development of a Recognition System for Alfalfa Leaf Diseases Based on Image Processing Technology. In: Li, D. (eds) Computer and Computing Technologies in Agriculture X. CCTA 2016. IFIP Advances in Information and Communication Technology, vol 509. Springer, Cham. https://doi.org/10.1007/978-3-030-06155-5_22

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  • DOI: https://doi.org/10.1007/978-3-030-06155-5_22

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-06154-8

  • Online ISBN: 978-3-030-06155-5

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