Research Progress of Crop Disease Image Recognition Based on Wireless Network Communication and Deep Learning

Abstract

The traditional digital image processing technology has its limitations. It requires manual design features, which consumes manpower and material resources, and identifies crops with a single type, and the results are bad. Therefore, to find an efficient and fast real-time disease image recognition method is very meaningful. Deep learning is a machine learning algorithm that can automatically learn representative features to achieve better results in areas of image recognition. Therefore, the purpose of this paper is to use deep learning methods to identify crop pests and diseases and to find efficient and fast real-time image recognition methods of disease. Deep learning is a newly developed discipline in recent years. Its purpose is to study how to actively obtain a variety of feature representation methods from data samples and rely on data-driven methods, a series of nonlinear transformations are applied to finally collect the original data from specific to abstract, from general to specified semantics, and from low-level to high-level characteristic forms. This paper analyzes the classical and the latest neural network structure based on the theory of deep learning. For the problem that the network based on natural image classification is not suitable for crop pest and disease identification tasks, this paper has improved the network structure that can take care of both recognition speed and recognition accuracy. We discussed the influence of the crop pest and disease feature extraction layer on recognition performance. Finally, we used the inner layer as the main structure to be the pest and disease feature extraction layer by comparing the advantages and disadvantages of the inner and global average pooling layers. We analyze various loss functions such as Softmax Loss, Center Loss, and Angular Softmax Loss for pest identification. In view of the shortcomings of difficulty in loss function training, convergence, and operation, making the distance between pests and diseases smaller and the distance between classes more greater improved the loss function and introduced techniques such as feature normalization and weight normalization. The experimental results show that the method can effectively enhance the characteristic expression ability of pests and diseases and thus improve the recognition rate of pests and diseases. Moreover, the method makes the pest identification network training simpler and can improve the pest and disease recognition rate better.