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

文献类型： 外文期刊

作者： Wang, Chunshan 1 ; Zhou, Ji 1 ; Zhang, Yan 2 ; Wu, Huarui 1 ; Zhao, Chunjiang 1 ; Teng, Guifa 2 ; Li, Jiuxi 5 ;

作者机构： 1.Natl Engn Res Ctr Informat Technol Agr, Beijing, Peoples R China

2.Hebei Agr Univ, Sch Informat Sci & Technol, Baoding, Peoples R China

3.Beijing Res Ctr Informat Technol Agr, Beijing, Peoples R China

4.Hebei Key Lab Agr Big Data, Baoding, Peoples R China

5.Hebei Agr Univ, Sch Mech & Elect Engn, Baoding, Peoples R China

关键词： disease recognition; graph convolutional neural network; text recognition; robustness; fusion

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：6.627； 五年影响因子：7.255 ）

ISSN： 1664-462X

年卷期： 2022 年 12 卷

页码：

收录情况： SCI

摘要： 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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