An Ensemble Recognition Method for Grape Leaf Diseases Based on an Improved Convolutional Neural Network
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.
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