Abstract
Plant disease recognition is a challenging task in agriculture to classify the diseases from the image of plants’ leaves, because the plant species and diseases can be various and images of infected leaves may have various appearances and similar structure to normal ones. To solve this problem, hierarchical classification is usually adopted. However, the class information of plant species and diseases has not been yet well exploited. In this paper, we proposed an end-to-end multi-branch hierarchical classification model based on convolutional neural network. Through our designed Select Branch, the proposed model can choose the sub-class from the current cluster iteratively. Meanwhile, a generalized model in hierarchical structure is presented to make the model more scalable for similar classification task. Experiments have been conducted on the benchmark dataset, and the proposed model can achieve better accuracy and be trained much faster than the previous flat classification model.
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Acknowledgement
This work is partially supported by the National Natural Science Foundation of China (Grant no. 61772568), the Guangzhou Science and Technology Program (Grant no. 201804010288), and the Fundamental Research Funds for the Central Universities (Grant no. 18lgzd15).
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Mao, Z., Chen, J., Yang, M. (2019). Multi-branch Structure for Hierarchical Classification in Plant Disease Recognition. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11859. Springer, Cham. https://doi.org/10.1007/978-3-030-31726-3_45
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DOI: https://doi.org/10.1007/978-3-030-31726-3_45
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