融合ECA机制与DenseNet201的水稻病虫害识别方法  

Rice Disease and Pest Recognition Method Integrating ECA Mechanism and DenseNet201

作　　者：潘晨露 张正华[1] 桂文豪 马家俊 严晨曦 张晓敏 PAN Chenlu;ZHANG Zhenghua;GUI Wenhao;MA Jiajun;YAN Chenxi;ZHANG Xiaomin(College of information engineering(College of Artificial Intelligence),Yangzhou University,Yangzhou 225127,China)

机构地区：[1]扬州大学信息工程学院(人工智能学院),江苏扬州225127

出　　处：《智慧农业（中英文）》2023年第2期45-55,共11页Smart Agriculture

基　　金：2020年江苏省现代农业发展项目(2020-SJ-003-YD03);扬州大学学科特区学科交叉课题(yzuxk202008);2022年江苏省大学生创新训练计划项目重点项目(国家级)(202211117065Z)。

摘　　要：[目的/意义]针对传统人工识别病虫害存在的效率过低、成本过高等问题,提出一种融合ECA (Efficient Channel Attention)注意力机制与DenseNet201的水稻图像识别模型GE-DenseNet (G-ECA DenseNet)。[方法]首先在ECA机制上引入Ghost模块的思想构成G-ECA Layer结构,增强其提取特征的能力。其次,在DenseNet201原有的Dense Block前引入G-ECA Layer,使模型具有更优的通道特征提取能力。由于实验所用的数据集较小,将DenseNet201在ImageNet数据集上预训练的权重参数迁移到GE-DenseNet中。训练时,采用Focal Loss函数来解决各分类样本不均衡的问题。同时,使用Adam优化器以避免在模型训练初期由于部分权重随机初始化而导致反向传播的梯度变化剧烈的问题,在一定程度上削弱了网络训练的不确定性。[结果和讨论]在包含水稻胡麻斑病、水稻铁甲虫、稻瘟病与健康水稻的3355张图像数据集上进行了实验测试,识别准确率达到83.52%。由GE-DenseNet模型的消融对比实验可得,引入了Focal Loss函数与G-ECA Layer层之后,模型准确率上升2.27%。将所提模型与经典NasNet (4@1056)、VGG-16和ResNet50模型相比,分类准确率分别提高了6.53%、4.83%和3.69%;相较于原始的DenseNet201,对水稻铁甲虫的识别准确率提升达20.32%。[结论]加入G-ECA Layer结构能够使模型更为准确地捕捉适合于水稻病虫害识别的特征信息,从而使GE-DenseNet模型能够实现对不同水稻病虫害图像更为准确地识别,为及时防治病虫害,减少各类损失提供技术支持。[Objective] To address the problems of low efficiency and high cost of traditional manual identification of pests and diseases,improve the automatic recognition of pests and diseases by introducing advanced technical means,and provide feasible technical solutions for agricultural pest and disease monitoring and prevention and control,a rice image recognition model GE-DenseNet(G-ECA DenseNet) based on improved ECA(Efficient Channel Attention) mechanism with DenseNet201 was proposed.[Methods] The leaf images of three pests and diseases,namely,brownspot,hispa,leafblast and healthy rice were selected as experimental materials.The images were captured at the Zhuanghe Rice Professional Cooperative in Yizheng,Jiangsu Province,and the camera was used to manually take pictures from multiple angles such as the top and side of rice every 2 h,thus acquiring 1250 images of rice leaves under different lighting conditions,different perspectives,and different shading environments.In addition,samples about pests and diseases were collected in the Kaggle database.There were 1488 healthy leaves,523 images of brownspot,565 images of hispa,and 779 images of leafblast in the dataset.Since the original features of the pest and disease data were relatively close,firstly,the dataset was divided into a training set and a test set according to the ratio of 9:1,and then data enhancement was performed on the training set.A region of interest(ROI) was randomly selected to achieve a local scale of 1.1 to 1.25 for the sample images of the dataset,thus simulating the situation that only part of the leaves were captured in the actual shooting process due to the different distance of the plants from the camera.In addition,a random rotation of a certain angle was used to crop the image to simulate the different angles of the leaves.Finally,the experimental training set contains 18,018 images and the test set contains 352 images.The GEDenseNet model firstly introduces the idea of Ghost module on the ECA attention mechanism to constitute the G-ECA La

关 键 词：DensetNet201 ECA注意力机制 病虫害识别 迁移学习 卷积神经网络 Ghost模块 

分 类 号：S126[农业科学—农业基础科学]