Classification algorithm based on convolutional neural network for wild fungus

Abstract

Poisoned by the edible fungus accident occurred frequently in recent years since that there were no effective and quick recognition methods for the wild fungus. To tackle the problem, a wild fungus classification algorithm based on a deep convolutional neural network (CNN) and Residual Network (ResNet), is proposed in this paper. An optimization method is also proposed for network training. In order to verify the effectiveness of the model and optimization method, a wild fungus database, in total of 1280 images, is used in this paper. The experimental results show that the proposed algorithm can effectively complete the classification task of wild mushrooms, and the optimization algorithm proposed in this paper can also effectively improve the classification effect of the algorithm model.