基于注意力机制和多尺度轻量型网络的农作物病害识别  

Crop disease recognition using attention mechanism and multi-scale lightweight network

作　　者：王泽钧 马凤英[1] 张瑜[2] 张芳芳 纪鹏 曹茂永 Wang Zejun;Ma Fengying;Zhang Yu;Zhang Fangfang;Ji Peng;Cao Maoyong(Department of Electronics,Electrical and Control,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250353,China;Department of Mathematics and Artificial Intelligence,Qilu University of Technology(Shandong Academy of Sciences),Jinan 250353,China)

机构地区：[1]齐鲁工业大学(山东省科学院)电子电气与控制学部,济南250353 [2]齐鲁工业大学(山东省科学院)数学与人工智能学部,济南250353

出　　处：《农业工程学报》2022年第S01期176-183,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(61903207);山东省重点研发计划项目(重大科技创新工程)(2019JZZY010731,2020CXGC010901);齐鲁工业大学国际合作研究项目(QLUTGJHZ2018020)。

摘　　要：准确识别农作物病害并及时防护是保障农作物产量的重要措施。针对传统农作物病害识别模型体积大、准确率不高的问题,该研究提出一种基于注意力机制和多尺度特征融合的轻量型神经网络模型(Lightweight Multi-scale Attention Convolutional Neural Networks,LMA-CNNs)。首先,为减少参数量,使模型轻量化,网络主体结构采用深度可分离卷积;其次,在深度可分离卷积基础上设计出残差注意力模块和多尺度特征融合模块;同时引入Leaky ReLU激活函数增强负值特征的提取。残差注意力模块通过嵌入通道和空间注意力机制,增强有用特征信息的权重并减弱噪声等干扰信息的权重,残差连接能够有效防止网络退化。多尺度特征融合模块利用其不同尺度的卷积核提取多种尺度的病害特征,提高特征的丰富度。试验结果表明,LMA-CNNs模型在59类公开农作物病害图像测试集上的准确率为88.08%,参数量仅为0.14×10^(7),优于ResNet34、ResNeXt、ShuffleNetV2等经典神经网络模型。通过比较不同研究者在同一数据集下所设计的网络模型,进一步验证LMA-CNNs模型不仅拥有更高的识别精度,还具有更少的参数。该研究提出的LMA-CNNs模型较好地平衡模型复杂程度和识别准确率,为移动端农作物病害检测提供参考。In recent years,diseases and pests have caused a huge loss in agricultural production.Accurate identification of crop diseases and timely protection are important measures to ensure crop yield.Traditional methods of diagnosing agricultural diseases typically depend on the expertise and judgment of specialists.This approach is dependent on human subjective perception,which is prone to error and cannot ensure timeliness.The optimal time to cure agricultural diseases may be missed by traditional methods,resulting in financial losses.The neural networks and the development of deep learning have brought new technologies to the appraisal of agricultural diseases.However,certain large-scale neural networks cannot be implemented on mobile terminals to accomplish crop disease detection in realistic settings due to the low identification accuracy and a huge number of parameters.To address the problems of large size and low accuracy of traditional crop disease recognition models,we proposed a Lightweight Multi-scale Attention Convolutional Neural Networks(LMA-CNNs)to solve the above problems.First,in order to reduce the number of parameters and make the model lightweight,depthwise separable convolution was adopted as the main structure of the network;secondly,the residual attention module and multi-scale feature fusion module were designed on the basis of depthwise separable convolution;at the same time,the Leaky ReLU activation function was introduced to enhance the extraction of negative-valued features.The residual attention module enhanced the weight of useful feature information and weakened the weight of interference information such as noise by embedding channels and spatial attention mechanisms,and improved the recognition of important features by the network model.Residual connections could effectively prevent network degradation.The multi-scale feature fusion module used its convolution kernels of different scales to extract disease features of multiple scales,which improved the richness of features.The experiment

关 键 词：农作物 模型 图像识别 注意力机制 多尺度卷积 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]