关键词: 病害识别, 苹果叶片, 残差网络, 迁移学习, 深度学习

Abstract: Apple leaf diseases are similar in morphology and different in spot size，and traditional methods relying on labor and agricultural experts to identify them are inefficient. In view of this，this study proposed an apple disease identification model REP⁃ResNet based on improved residual network.This model was improved by adopting the order adjustment of residual structure（that was bath normalization，activation function，convolutional layer），and adding the efficient channel attention and parallel convolution based on the baseline model ResNet⁃50.During the training process，the model weight parameters pretrained in the public dataset PlantVillage were transferred to the above network model for retraining，which aimed to accelerate the convergence speed of the network and improve the recognition ability of the model.Data expansion was used to solve the problem of uneven samples during training.The results showed that the recognition accuracy of the REP⁃ResNet model was 2.41 percentage points higher than that of the benchmark network model.The model was trained by transfer learning，and the accuracy rate of apple leaf disease recognition in complex backgrounds reached 97. 69%. Compared with traditional convolutional neural networks，the recognition effect was greatly improved.

Key words: Disease identification, Apple leaves, Residual network, Transfer learning, Deep learning