融合SKNet与MobilenetV3的芒果叶片病虫害分类方法  

Integrated SKNet/Mobilenet V3 Classification of Mango Leaf Diseases and Infestations

作　　者：沈熠辉 何惠彬 陈小宇 颜胜男 SHEN Yihui;HE Huibin;CHEN Xiaoyu;YAN Shengnan(College of Arts and Sciences,Fujian Medical University,Fuzhou,Fujian 350122,China;Intelligent Technology Department,Fujian Agricultural Mechanization Research Institute,Fuzhou,Fujian 350005,China;School of Computer and Control Engineering,Minjiang University,Fuzhou,Fujian 350103,China)

机构地区：[1]福建医科大学文理艺术学院,福建福州350122 [2]福建省农业机械化研究所智能技术部,福建福州350005 [3]闽江学院计算机与控制工程学院,福建福州350103

出　　处：《福建农业学报》2024年第5期584-592,共9页Fujian Journal of Agricultural Sciences

基　　金：福建省技术创新重点攻关及产业化项目(2023G015)。

摘　　要：【目的】针对芒果叶片病虫害缺少数据集和识别准确率低的问题,筛选构建芒果叶片病虫害分类模型,以提高芒果叶病虫害分类准确率。【方法】提出使用去噪扩散模型进行病虫害数据增强,同时联合SKNet与MobilenetV3模型的芒果叶片病虫害分类方法。首先使用去噪扩散模型对数据集进行扩充,再采用多尺度结构相似性指标对生成的病虫害图像与拍摄的病虫害图像之间的相似程度进行评估,接着对DDIM与DCGAN网络训练和生成效果进行比对。在MobilenetV3模型中,将SE注意力模块替换为SKNet模块进行构建网络模型。【结果】使用DDIM生成的所有类型的病虫害图像与拍摄的病虫害图像的MS-SSIM指标均大于0.63,且都高于DCGAN。相较于其他注意力模块,联合SKNet与MobilenetV3的分类效果最佳,在98%以上。对添加CA、CBAM、ECA注意力模块进行平滑类激活图可视化,对比其他注意力模块,使用SKNet注意力分布区域更为集中在病虫害叶片上。【结论】该方法在病虫害叶片检测上具有良好的应用前景,能提升病虫害识别效率与精度,减少检测成本,同时可应用于移动式或者嵌入式设备。【Objective】Leaf diseases and infestations on mango trees were classified for database establishment and precision identification by combining the Mobilenet V3 model with Selective Kernel Network(SKNet).【Method】To improve the accuracy of disease and infestation classification on mango plants,data augmentation was firstly conducted.A denoising diffusion model was applied to expand the dataset followed by using a multi-scale structural similarity index to examine the similarity between the virtually generated and the camera-captured images of the diseases or infestations.Then,the training and generation effects of DDIM and DCGAN networks were compared.In the Mobilenet V3 model,the SE attention module was replaced with SKNet to construct the final platform.【Results】The MS-SSIM index of all types of DDIM images was greater than 0.63,which was higher than that of DCGAN.The classification accuracy of 98% delivered by merging SKNet with Mobilenet V3 was the best performance.Furthermore,combination of the two programs afforded more focus on the diseased leaves than did other smooth grade activation visualization by adding CA,CBAM,or ECA.【Conclusion】The newly developed classification method by integrating SKNet and Mobilenet V3 performed satisfactorily in distinguishing various diseased or infested mango leaves.The application not only significantly improved the efficiency and accuracy of disease identification but also reduced the epidemic monitoring costs by easily incorporating it with mobile or embedded devices.

关 键 词：芒果叶片 扩散概率模型 Mobilenet Selective Kernel Networks 

分 类 号：TQ639.8[化学工程—精细化工] TP183[自动化与计算机技术—控制理论与控制工程]