山东农业大学学报（自然科学版）2024，Vol.55Issue(6)：950-960,11.DOI:10.3969/j.issn.1000-2324.2024.06.017
一种基于改进卷积神经网络的葡萄叶片病害集成识别方法
An Ensemble Recognition Method for Grape Leaf Diseases Based on an Improved Convolutional Neural Network
摘要
Abstract
To effectively improve the accuracy and efficiency of grape leaf diseases recognition,and to achieve timely prevention and control of grape diseases,thereby improving yield and quality,this paper proposes an ensemble recognition method for grape leaf diseases based on an improved convolutional neural network.Initially,the Bagging ensemble learning algorithm is used to generate multiple diverse training subsets;Subsequently,the SE(Squeeze-and-Excitation)and CA(Channel Attention)attention mechanisms are respectively integrated into the ResNet152,DenseNet121 and MobileNetV3 models,resulting in three improved neural network-based learning models,which are then trained on the generated training subsets.Finally,these models are integrated using the idea of weighted averaging.Experiments conducted on a grape leaf diseases dataset demonstrate that the recognition accuracy of this ensemble model reaches 99.38%,making it a relatively effective method for grape leaf diseases recognition.关键词
葡萄叶片病害识别/卷积神经网络/集成学习/Bagging算法/图像识别Key words
Grape leaf diseases recognition/convolutional neural network/ensemble learning/bagging algorithm/image identification分类
信息技术与安全科学引用本文复制引用
陈诗瑶,孔淳,冯峰,王志军,孙博..一种基于改进卷积神经网络的葡萄叶片病害集成识别方法[J].山东农业大学学报（自然科学版）,2024,55(6):950-960,11.基金项目
山东省重大科技创新工程项目:现代果园智慧种植装备与大数据平台研发及示范应用(2019JZZY010706) （2019JZZY010706）
