山东农业大学学报（自然科学版）2024，Vol.55Issue(3)：356-366,11.DOI:10.3969/j.issn.1000-2324.2024.03.007
一种基于改进ResNet18神经网络的玉米叶片病害识别方法
Maize Leaf Disease Recognition Method Based on Improved ResNet18 Neural Network
摘要
Abstract
In order to develop fast and efficient method for maize leaf disease identification,we proposes a corn disease identification method based on improved ResNet18 neural network using five common maize leaf diseases such as grey spot,southern rust,small spot,rust,and leaf spot as the research object.By introducing Pyramidal Convolution on the basis of ResNet18 network,multi-scale feature information can be used to improve the identification and localization ability of single leaf in the complex growth environment,to effectively accelerate the convergence of the model and significantly improve the disease identification accuracy of the model.The activation function of the residual structure was replaced with the activation function of PReLU(Parametric Rectified Linear Unit)to avoid neuronal death during model training.Experiments on the collected real maize leaf disease dataset show that the accuracy,precision,recall and F1-scores of maize leaf disease identification are improved by 1.86%,1.78%,1.78%and 1.87%compared with the original ResNet18 residual network;The parameter size of the model was reduced by 1.85%.This model can be used as an effective method to detect maize leaf diseases in complex growth environments.关键词
玉米叶片/病害识别/ResNet18/金字塔卷积/PReLUKey words
Maize leaves/disease identification/ResNet18/pyramid convolution/PReLU分类
计算机与自动化引用本文复制引用
马春悦,郭秀茹,王琛,孙博,王志军..一种基于改进ResNet18神经网络的玉米叶片病害识别方法[J].山东农业大学学报（自然科学版）,2024,55(3):356-366,11.基金项目
山东省自然科学基金面上项目:基于类重叠视角的类不平衡数据分类方法研究(ZR2023MF098) （ZR2023MF098）
