基于改进MobileNet-V2的田间农作物叶片病害识别  

Recognizing the diseases of crop leaves in fields using improved Mobilenet-V2

作　　者：孙俊[1] 朱伟栋 罗元秋 沈继锋[1] 陈义德 周鑫[1] Sun Jun;Zhu Weidong;Luo Yuanqiu;Shen Jifeng;Chen Yide;Zhou Xin(School of Electrical and Information Engineering,Jiangsu University,Zhenjiang 212013,China)

机构地区：[1]江苏大学电气信息工程学院,镇江212013

出　　处：《农业工程学报》2021年第22期161-169,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：江苏高校优势学科建设工程(三期)资助项目(PAPD-2018-87);江苏大学大学生科研立项资助项目(20AB0019)。

摘　　要：农作物病害是造成粮食产量下降的重要因素,利用智能化手段准确地识别农作物病害有利于病害的及时防治,该研究基于改进的MobileNet-V2识别复杂背景下的农作物病害,对未来覆盖各种作物的智能化病害识别工作具有重要意义。首先创建含有11类病害叶片及4类健康叶片的农作物数据集,采用数据增强操作构造不同的识别场景。其次在原始模型MobileNet-V2的基础上,嵌入轻量型的坐标注意力机制,建立通道注意力与位置信息的依赖关系。然后对网络中不同尺寸的特征图采取上采样融合操作,构建兼具网络高、低层信息的新特征图。此外,采用分组卷积并删除模型中不必要的分类层,减少模型参数量。试验结果表明:改进模型的参数量为2.30×10^(6),改进模型的识别准确率在背景复杂的农作物叶片病害数据集中达到了92.20%,较改进前提高了2.91个百分点。相比EfficientNet-b0、ResNet-50、ShuffleNet-V2等经典卷积神经网络架构,改进模型不仅达到了更高的识别准确率,还具有更平稳的收敛过程以及更少的参数。该研究改进的模型较好地平衡了模型的复杂度和识别准确率,为深度学习模型移植至田间移动病害检测设备提供了思路。Crop pests and diseases are emerging threats to global food security in recent years.Manual diagnosis has also been a serious constraint to recognizing the crop diseases in modern agriculture.The latest Convolutional Neural Network(CNN)models have opened up a new way to control diseases with the development of deep learning.However,a complex real environment in the field has posed a great challenge on the general model for disease recognition,due to the single background of the leaf disease images taken in the laboratory.In this study,an improved MobileNet-V2 was proposed to recognize the diseases of crop leaves in the fields,thereby optimizing the parameters for higher accuracy under the complex background.The specific procedures were as followed.Firstly,an image dataset was collected in the field for the disease classification,including 11 kinds of diseased leaves and 4 kinds of healthy leaves of four crops.A series of enhancement operations were then performed on the disease images,including random brightness,and noise.Secondly,a coordinate attention mechanism was added in the 3-18 layers of the basic MobileNet-V2 model.The Region of Interest(ROI)was effectively positioned to concentrate on the disease regions in the pixel coordinate system,thereby to better identify the background and foreground information of the targets.Since the areas of disease spots were different,it was easy to miss some details of the diseases only when using the high-level features.Thus,a feature pyramid module was added to the model using a multi-scale feature fusion.As such,the low-level features were combined with the high-level features,providing for more targets information and better recognition.The specific sampling was operated from the 7×7 to 14×14 feature map,where the same size was fused.Finally,the unnecessary classification layer was removed to optimize the parameter memory of the improved model,where the operation of group convolution was adopted.Compared with the original,the classification accuracy of the improved mo

关 键 词：农作物 病害 复杂背景 轻量型 上采样 参数量 

分 类 号：S126[农业科学—农业基础科学]