Automatic citrus canker detection from leaf images captured in field

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.

► A new approach to detecting citrus canker from leaf images captured in field. ► An improved AdaBoost to select the most significant features of citrus lesions. ► Leaf image color and texture information are combined to model citrus canker lesions. ► Local Binary Pattern on each lesion zone to reveal canker spatial properties. ► The proposed approach achieves similar classification accuracy as human experts.