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A Graph-Related High-Order Neural Network Architecture via Feature Aggregation Enhancement for Identification Application of Diseases and Pests

文献类型： 外文期刊

作者： Kong, Jianlei 1 ; Yang, Chengcai 1 ; Xiao, Yang 1 ; Lin, Sen 2 ; Ma, Kai 3 ; Zhu, Qingzhen 4 ;

作者机构： 1.Beijing Technol & Business Univ, Sch Artificial Intelligence, Beijing 100048, Peoples R China

2.Beijing Acad Agr & Forestry Sci, Res Ctr Intelligent Equipment Technol, Beijing 100097, Peoples R China

3.Beijing Forestry Univ, Sch Engn, Beijing 100086, Peoples R China

4.Jiangsu Univ, Sch Agr Equipment Engn, Zhenjiang 212013, Jiangsu, Peoples R China

期刊名称：COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE （ 影响因子：3.12； 五年影响因子：3.877 ）

ISSN： 1687-5265

年卷期： 2022 年 2022 卷

页码：

收录情况： SCI

摘要： Diseases and pests are essential threat factors that affect agricultural production, food security supply, and ecological plant diversity. However, the accurate recognition of various diseases and pests is still challenging for existing advanced information and intelligence technologies. Disease and pest recognition is typically a fine-grained visual classification problem, which is easy to confuse the traditional coarse-grained methods due to the external similarity between different categories and the significant differences among each subsample of the same category. Toward this end, this paper proposes an effective graph-related high-order network with feature aggregation enhancement (GHA-Net) to handle the fine-grained image recognition of plant pests and diseases. In our approach, an improved CSP-stage backbone network is first formed to offer massive channel-shuffled features in multiple granularities. Secondly, relying on the multilevel attention mechanism, the feature aggregation enhancement module is designed to exploit distinguishable fine-grained features representing different discriminating parts. Meanwhile, the graphic convolution module is constructed to analyse the graph-correlated representation of part-specific interrelationships by regularizing semantic features into the high-order tensor space. With the collaborative learning of three modules, our approach can grasp the robust contextual details of diseases and pests for better fine-grained identification. Extensive experiments on several public fine-grained disease and pest datasets demonstrate that the proposed GHA-Net achieves better performances in accuracy and efficiency surpassing several other existing models and is more suitable for fine-grained identification applications in complex scenes.

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