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Automatic citrus canker detection from leaf images captured in field

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journal contribution
posted on 22.02.2013, 10:18 by Min Zhang, Qinggang Meng
Citrus canker, a bacterial disease of citrus tree leaves, causes significant damage to citrus production worldwide. Effective and fast disease detection methods must be undertaken to minimize the losses of citrus canker infection. In this paper, we present a new approach based on global features and zone-based local features to detect citrus canker from leaf images collected in field which is more difficult than the leaf images captured in labs. Firstly, an improved AdaBoost algorithm is used to select the most significant features of citrus lesions for the segmentation of the lesions from their background. Then a canker lesion descriptor is proposed which combines both color and local texture distribution of canker lesion zones suggested by plant phytopathologists. A two-level hierarchical detection structure is developed to identify canker lesions. Thirdly, we evaluate the proposed method and its comparison with other approaches, and the experimental results show that the proposed approach achieves similar classification accuracy as human experts.

History

School

  • Science

Department

  • Computer Science

Citation

ZHANG, M. and MENG, Q., 2011. Automatic citrus canker detection from leaf images captured in field. Pattern Recognition Letters, 32 (15), pp. 2036 - 2046.

Publisher

© Elsevier B.V.

Version

AM (Accepted Manuscript)

Publication date

2011

Notes

This article was published in the journal, Pattern Recognition Letters [© Elsevier Ltd.] and the definitive version is available at: http://dx.doi.org/10.1016/j.patrec.2011.08.003

ISSN

0167-8655

Language

en

Exports