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An Effective Pyramid Neural Network Based on Graph-Related Attentions Structure for Fine-Grained Disease and Pest Identification in Intelligent Agriculture

文献类型： 外文期刊

作者： Lin, Sen 1 ; Xiu, Yucheng 2 ; Kong, Jianlei 2 ; Yang, Chengcai 2 ; Zhao, Chunjiang 2 ;

作者机构： 1.Shenyang Agr Univ, Coll Informat & Elect Engn, Shenyang 110866, Peoples R China

2.Beijing Technol & Business Univ, Natl Engn Res Ctr Agriprod Qual Traceabil, Beijing 100048, Peoples R China

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

关键词： smart agriculture; pest and diseases recognition; graph convolution neural network; attention mechanism; mobile computing application

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.6； 五年影响因子：3.6 ）

ISSN：

年卷期： 2023 年 13 卷 3 期

页码：

收录情况： SCI

摘要： In modern agriculture and environmental protection, effective identification of crop diseases and pests is very important for intelligent management systems and mobile computing application. However, the existing identification mainly relies on machine learning and deep learning networks to carry out coarse-grained classification of large-scale parameters and complex structure fitting, which lacks the ability in identifying fine-grained features and inherent correlation to mine pests. To solve existing problems, a fine-grained pest identification method based on a graph pyramid attention, convolutional neural network (GPA-Net) is proposed to promote agricultural production efficiency. Firstly, the CSP backbone network is constructed to obtain rich feature maps. Then, a cross-stage trilinear attention module is constructed to extract the abundant fine-grained features of discrimination portions of pest objects as much as possible. Moreover, a multilevel pyramid structure is designed to learn multiscale spatial features and graphic relations to enhance the ability to recognize pests and diseases. Finally, comparative experiments executed on the cassava leaf, AI Challenger, and IP102 pest datasets demonstrates that the proposed GPA-Net achieves better performance than existing models, with accuracy up to 99.0%, 97.0%, and 56.9%, respectively, which is more conducive to distinguish crop pests and diseases in applications for practical smart agriculture and environmental protection.

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