Citrus canker detection based on leaf images analysis

Citrus canker is a quarantine disease which may cause huge damage to citrus production. Effective and fast disease detection methods must be undertaken to minimize the losses of citrus canker infection. In this paper, a new approach is presented to detect citrus canker from leaf images collected in field. Firstly, a global canker lesion descriptor is used to detect citrus diseased-lesion from leaf-background. Then a zone-based combined local descriptor is proposed to identify citrus canker disease from other similar diseased-lesions. Thirdly, a two-level hierarchical detection structure is developed to identify the canker lesion and AdaBoost is adopted in feature selection and classifier learning. Finally, evaluation of the proposed method and its comparison with other approaches are discussed, and the experimental results shows that the proposed approach achieves similar classification accuracy of human experts.