准确识别玉米病害有助于对病害进行及时有效的防治.针对传统方法对于玉米叶片病害识别精度低和模型泛化能力弱等问题,该研究提出了一种基于改进卷积神经网络模型的玉米叶片病害识别方法.改进后的模型由大小为3×3的卷积层堆栈和Inception模块与ResNet模块组成的特征融合网络两部分组成,其中3×3卷积层的堆栈用于增加特征映射的区域大小,Inception模块和ResNet模块的结合用于提取出玉米叶片病害的可区分特征.同时模型通过对批处理大小、学习率和dropout参数进行优化选择,确定了试验的最佳参数值.试验结果表明,与经典机器学习模型如最近邻节点算法(K-Nearest Neighbor,KNN)、支持向量机(Support Vector Machine,SVM)和反向传播神经网络(Back Propagation Neural Networks,BPNN)以及深度学习模型如AlexNet、VGG16、ResNet和Inception-v3相比,经典机器学习模型的识别率最高为77％,该研究中改进后的卷积神经网络模型的识别率为98.73％,进一步提高了模型的稳定性,为玉米病害检测与识别的进一步研究提供了参考.“,”Maize leaf disease is a serious problem in the process of agricultural production. Controlling maize leaf disease is of great significance to improving maize yield and quality, maintaining food security, and promoting agricultural development. In this study, traditional machine learning methods often needed spot segmentation and feature extraction to identify maize leaf disease, but due to the subjective and exploratory nature of artificial feature extraction, the result of feature extraction seriously affected the accuracy of disease recognition. The Common convolutional Neural Network (CNN) had many parameters, which made it difficult to converge and had low generalization ability. Aiming at the problems such as low accuracy of traditional methods for leaf disease identification and weak model generalization ability of maize, this study presented an improved CNN, which included seven convolutional layers, four maximum pooling layers, one Inception module, one ResNet module, two Global Average Pooling (GAP) layers, and one SoftMax classification layer. The CNN improved the traditional CNN structure. The backbone network of the model was composed of a convolutional layer stack with a size of 3×3 and a feature fusion network composed of the Inception module and ResNet module. The 3×3 convolutional layer stack was used to increase the area size of the feature map, and the Inception module and ResNet module were combined to extract the distinguishable features of the maize leaf disease. At the same time, the improved CNN used the GAP instead of the full connection layer to optimize the training time and improve the training accuracy. This study randomly scaled, flipped, and rotated the original image of the data set to obtain the enhanced image, and took 80％ of the image as the training data set, and the rest as the test data set. The size of the image was uniformly modified to 256×256 pixels for training. The improved CNN in this study randomly abandoned some neurons and their connections during the training process, reducing the number of intermediate layer features. Selecting appropriate Dropout effectively solved the problem of model overfitting. At the same time, the study showed that the learning rate controlled the speed of adjusting the weights of the neural network based on the loss gradient. In order to find the best model parameters, this study optimized and selected the batch size, learning rate, and Dropout parameters, and determined that the validation accuracy of the model was the highest when the batch size was 64, the learning rate was set to 0.001, and the dropout parameter was set to 30％, thus further improving the efficiency of the model. Based on 3852 maize data sets in PlantVillage and 110 maize leaf blight data captured in the field, this study compared the test accuracy of the traditional model and the improved CNN. The experimental results showed that compared with classical Machine learning models such as K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Back Propagation Neural Networks (BPNN) and deep learning models such as AlexNet, VGG16, ResNet, and Inception-v3, the accuracy of improved CNN in this study reached over 98％. The classical machine learning model had a maximum recognition rate of 77％. In order to show the performance of the improved CNN, the recall rate, average precision, and F1-score of different models were compared. The results showed that the precision of the improved CNN was 95.74％, the recall rate was 99.41％ and F1-score was 97.36％, which further improved the stability of the model and provided a reference for further research on corn disease detection and recognition.