基于改进DenseNet和迁移学习的荷叶病虫害识别模型  

Model for identifying lotus leaf pests and diseases using improved DenseNet and transfer learning

作　　者：张国忠[1,2] 吕紫薇 刘浩蓬 刘婉茹[1,2] 龙长江 黄成龙 ZHANG Guozhong;LYU Ziwei;LIU Haopeng;LIU Wanru;LONG Changjiang;HUANG Chenglong(College of Engneering,Huazhong Agricultural University,Wuhan 430070,China;Key Laboratory of Agricultural Equipment in Mid-lower Yangtze River,Ministry of Agriculture and Rural Affairs,Wuhan 430070,China;National Key Laboratory of Crop Genetic Improvement,Huazhong Agricultural University.Wuhan 430070,China)

机构地区：[1]华中农业大学工学院,武汉430070 [2]农业农村部长江中下游农业装备重点实验室,武汉430070 [3]华中农业大学作物遗传改良国家重点实验室,武汉430070

出　　处：《农业工程学报》2023年第8期188-196,共9页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家特色蔬菜产业技术体系专项资助项目(CARS-24-D-02);湖北省高等学校优秀中青年科技创新团队计划项目(T201934);中央高校基本科研业务费专项基金资助(2662020GXPY012)。

摘　　要：病虫害的发生将会严重影响莲藕品质与产量,开展病害诊断与识别对藕田病虫害及时对症对病诊治、提升莲藕生产质量与经济效益具有重要意义。该研究以荷叶病虫害高效、准确识别为目标,提出了一种基于改进DenseNet和迁移学习的荷叶病虫害识别模型。采用分支结构对模型的浅层特征提取模块进行改进,并在Dense Block与Transition Layer中引入Squeeze and Excitation注意力机制模块和锐化的余弦卷积,最后基于Plantvillage数据集进行迁移学习,实现了91.34%的识别准确率。该研究实现了对荷叶腐败病、病毒病、斜纹夜蛾、叶腐病、叶斑病的识别,并将改进后的模型推广应用于基于无人机图像的藕田病虫害检测,实现了病害分布可视化,可对莲藕病虫害的智能化防治提供有益指导。Influenced by the ecological environment and other factors,the quality and yield of lotus root have been seriously affected by the occurrence of diseases and insect pests in recent years.With the improvement of living standards and the development of the lotus industry chain,people are looking for green food,high-yield and high-quality products.Nowadays,many farmers and planters are unable to accurately identify the diseases and pests of lotus due to lack of professional knowledge of diseases and insect pests control.There is a shortage of efficient,low-cost and automatic identification technology for the prevention and control of lotus diseases and insect pests.The diagnosis and identification of diseases and insect pests are of great significance for the prevention and control of diseases and insect pests in lotus fields.Over the past few years,deep learning technology has been widely used in the field of plant diseases and insect pests recognition to automatically extract the features of plant diseases and insect pests.In order to achieve an efficient and accurate diagnosis of lotus leaf diseases and insect pests,lotus leaf diseases and insect pests dataset was constructed and preliminary experiments were constructed on AlexNet,VGG-16,ResNet50,ResNeXt50,and DenseNet121 models.The experimental results indicated that DenseNet121 has the best performance on the dataset,lotus leaf diseases and insect pests identification model based on improved DenseNet was improved.Firstly,different methods for dynamic data enhancement were compared in this paper.The results show that resizing and randomly resizing the image is more accurate than directly resizing to the same size.The loss of detail information in part of the image is caused by over-compressing the image size,which affects the model’s recognition effect.The accuracy of the model was increased from 81.47%to 85.01%by using the data enhancement method of resize,random resized crop,random horizontal flip and random adjust sharpness.AdaMax optimizer was used to repl

关 键 词：模型 无人机 病虫害识别 荷叶 DenseNet 注意力机制 余弦相似度 迁移学习 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] S24[自动化与计算机技术—计算机科学与技术]