迁移MobileNetV3的玉米病害识别方法OA

A method of corn disease recognition based on transfer learning using MobilenetV3 is proposed.The training dataset is augmented through online data enhancement,and the learning results of the MobilenetV3-Small network on the ImageNet dataset are utilized as pre-trained weights to construct the transfer learning model.A deep separable convolution module is adopted to reduce the model's parameters count.Additionally,a channel attention mechanism and the H-Swish activation function are incorporated to enhance both the accu-racy and efficiency of the model's recognition capabilities.The Adam optimizer and cross-entropy loss function are used to train the top-level classifier after migration.Experimental results show that the model achieves an accuracy of 95.68%on the test set.Subsequently,the last one-third of layers in the transfer model are unfro-zen,and the model is well tuned by adjusting the learning rate and optimizer parameters,resulting in a final test accuracy of 98.15%,which is an improvement of 2.47%compared to the pre-tuning accuracy.