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基于多尺度双注意力网络的植物病虫害识别

Plant pest and Disease Identification Based on MultiscaleDual Attention Network
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摘要 植物病虫害问题是农业上的重大难题,准确识别植物病虫害是农业病虫害预防和治理的关键步骤。经验丰富的植物病理专家通过观察叶片状态来进行诊断,不仅费时、费力,对于农民来说还需要付很大的成本来联系专家。因此,在ResNet模型的基础上设计了一种高效的多尺度双注意力模型(Multiscale Dual Attention Network)的植物病虫害识别方法。首先,通过多尺度卷积获取不同尺度的子特征图,然后,使用空间注意力和通道注意力对输入叶片重要特征进行加权处理。深度提取叶片图像中重要的全局特征和局部特征,快速准确的对植物病害进行识别。实验结果表明,在AI Challenge2018的植物病害数据集中,MDANet获得了90.2%的准确率,与其它卷积神经网络模型相比有着明显的优势。 Plant pest and disease problems are a major challenge in agriculture,and accurate identification of plant pests and diseases is a key step in the prevention and management of agricultural pests and diseases.Experienced plant pathologists make diagnoses by observing leaf states,which is not only time-consuming and labor-intensive,but also requires a significant cost for farmers to contact experts.Therefore,this paper designs an efficient Multiscale Dual Attention Network(MDANet)plant pest identification method based on the ResNet model.First,sub-feature maps at different scales were obtained by multiscale convolution,and then,the important features of input leaves were weighted using spatial attention and channel attention.The important global features and local features in the leaf images are extracted in depth for fast and accurate plant disease recognition.The experimental results show that MDANet obtained 90.2%accuracy in the plant disease dataset of AI Challenge 2018,which has a significant advantage over other convolutional neural network models.
作者 常开心 侯彦东 陈政权 李泉龙 CHANG Kai-xin;HOU Yan-dong;CHEN Zheng-quan;LI Quan-long(College of Artificial Intelligence,Henan University,Zhengzhou Henan 450000,China;College of Computer and Information Engineering,Henan University,Kaifeng Henan 475000,China)
出处 《计算机仿真》 2024年第4期175-179,共5页 Computer Simulation
基金 河南大学研究生培养创新与质量提升行动计划(SYLYC2022081)。
关键词 病虫害识别 多尺度注意力机制 卷积神经网络 Plant disease identification Multiscale attention mechanism Convolutional neural networks
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