Automatic cotton spider mites' damage grading algorithm for depth residual network based on transfer learning

Abstract

Consider the problems of low accuracy, time consumption and high cost in traditional artificial diagnosis of cotton spider mites. This paper proposed an automatic cotton spider mites' damage grading algorithm for improved deep residual network based on transfer learning (C-ResNet50). In this study, three kinds of image of cotton spider mites and healthy cotton leaf were collected with single background and natural background respectively to construct the image dataset of cotton spider mites. C-ResNet50 is improved on the basis of ResNet50, by first utilizing the PlantVillage data set pre-training model, then the focal loss function and exponential linear unit(ELU)are introduced in C-ResNet50, and the attention mechanism module is embedded in different network layers for contrast trials, at the same time, the optimization model of Dropout regularization method is added, finally compare the recognition effect of the different models. The expermental results showed that when the momentum factor value was 0.90 and the learning rate value was 0.01,compared with the existing models ResNet50, VGG16, MobileNet, Alexnet and SENet, the C-ResNet50 has the best classification effect, the average accuracy rate of identification of cotton spider mites damage level reached 98.10%.