4.7 Article

A Plant Disease Recognition Method Based on Fusion of Images and Graph Structure Text

Journal

FRONTIERS IN PLANT SCIENCE
Volume 12, Issue -, Pages -

Publisher

FRONTIERS MEDIA SA
DOI: 10.3389/fpls.2021.731688

Keywords

disease recognition; graph convolutional neural network; text recognition; robustness; fusion

Categories

Funding

  1. National Key Research and Development Program of China [2020YFD1100602]
  2. Hebei Province Key Research and Development Program [20327402D, 19227210D]
  3. National Natural Science Foundation of China [61871041]
  4. Project of Introducing Overseas Students in Hebei Province [C20190340]
  5. Research project of basic scientific research business expenses of provincial colleges and universities in Hebei Province [KY202004]

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The study addressed the issue of weak robustness in disease image recognition models based on deep learning by proposing a feature decomposition and recombination method, and applying graph convolutional neural network for feature learning to build a vegetable disease recognition model based on the fusion of images and graph structure text.
The disease image recognition models based on deep learning have achieved relative success under limited and restricted conditions, but such models are generally subjected to the shortcoming of weak robustness. The model accuracy would decrease obviously when recognizing disease images with complex backgrounds under field conditions. Moreover, most of the models based on deep learning only involve characterization learning on visual information in the image form, while the expression of other modal information rather than the image form is often ignored. The present study targeted the main invasive diseases in tomato and cucumber as the research object. Firstly, in response to the problem of weak robustness, a feature decomposition and recombination method was proposed to allow the model to learn image features at different granularities so as to accurately recognize different test images. Secondly, by extracting the disease feature words from the disease text description information composed of continuous vectors and recombining them into the disease graph structure text, the graph convolutional neural network (GCN) was then applied for feature learning. Finally, a vegetable disease recognition model based on the fusion of images and graph structure text was constructed. The results show that the recognition accuracy, precision, sensitivity, and specificity of the proposed model were 97.62, 92.81, 98.54, and 93.57%, respectively. This study improved the model robustness to a certain extent, and provides ideas and references for the research on the fusion method of image information and graph structure information in disease recognition.

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