融合双分支特征和注意力机制的葡萄病虫害识别模型  

Model for identifying grape pests and diseases based on two-branch feature fusion and attention mechanism

作　　者：彭红星[1] 徐慧明 刘华鼐[2] Peng Hongxing;Xu Huiming;Liu Huanai(College of Mathematics and Informatics,Key Laboratory of Smart Agricultural Technology in Tropical South China,Ministry of Agriculture and Rural Affairs,South China Agricultural University,Guangzhou Key Laboratory of Intelligent Agriculture,Guangzhou 510642,China;School of Chemistry and Chemical Engineering,South China University of Technology,Guangzhou 510641,China)

机构地区：[1]华南农业大学数学与信息学院,农业农村部华南热带智慧农业技术重点实验室,广州市智慧农业重点实验室,广州510642 [2]华南理工大学化学与化工学院,广州510641

出　　处：《农业工程学报》2022年第10期156-165,共10页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(61863011,32071912);2020广东省乡村振兴战略专项(2020KJ261);广州市基础研究计划基础与应用基础研究项目(202102080337);广州市科技计划项目(202002020016)。

摘　　要：葡萄病虫害识别是精细化防治的前提。针对现有研究中存在的数据集少、识别精度低、模型参数量大等问题,该研究构建包含健康叶片、3类病害叶片和16类虫害的葡萄病虫害数据集,提出基于改进MobileNet V2模型的葡萄病虫害识别模型。首先在MobileNet V2模型的反向残差模块中嵌入坐标注意力(Coordinate Attention,CA)机制,提升模型的信息表征能力;然后使用深度可分离卷积设计双分支特征融合模块,加强模型的特征提取能力;最后对模型的通道数进行调整,精简模型结构。试验结果表明:MobileNet_Vitis在葡萄病虫害数据集上的识别准确率和F1分数为89.16%和80.44%,相比改进前的MobileNet V2提高了1.83和9.31个百分点,而模型参数大小为7.85 MB,减少了8.5%。与ResNet101、ShuffleNetV2、MobileNetV3和GhostNet相比,MobileNet_Vitis的识别精度和F1分数更高,参数量更小。MobileNet_Vitis对单张葡萄病虫害图像的推理时间为17.53 ms,可以达到快速识别的要求。该研究提出的模型能够较好地识别葡萄病虫害,并且较大幅度地减少模型的参数量。将MobileNet_Vitis模型部署到移动端的小程序上,可为葡萄病虫害的防治提供帮助。The grape industry has been one of the most important strategies in the rural revitalization and precise poverty alleviation.A serious disturbance to grape planting can be the pests and diseases in recent years.The refined measures of agricultural and biological control are highly required to effectively reduce the occurrence of pests and diseases for economic and safety concerns.Accurate identification and diagnosis of grape diseases and insect pests can be the premise of fine control actions.The traditional identification of pests and diseases relies mainly on the experts or technical personnel for the visual sightings,particularly with the time-consuming,laborious,high cost,low timeliness,and difficult to be widely used.Alternatively,machine learning and computer vision can be expected to serve the promising application of efficient image identification.There is also a great potential to accelerate the identification efficiency of pests and diseases,cost saving,and high accuracy.Machine learning classification includes the feature representation and classifier.However,the manual feature-based identification can only be suitable for the small data sets at present.Most machine learning algorithms also depend on complex image processing and hand-designed features,resulting in low robustness in pests and disease identification,especially in a complex environment.Fortunately,deep learning can be widely expected in the agricultural field,due to the most cutting-edge,modern,and promising technology in recent years.This study aims to treat the small data set and a large number of model parameters in the existing deep learning for higher identification accuracy.A data set of grape pests and diseases was also constructed,containing the healthy leaves,three types of diseased leaves,and 16 types of insect pests.An identification model was then proposed for the grape pests and diseases using the improved MobileNet V2.Firstly,a Coordinate Attention(CA) was embedded in the reverse residual module of MobileNet V2 to enhance t

关 键 词：病虫害 图像识别 葡萄 MobileNet V2 双分支特征融合 坐标注意力机制 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]