Fruit Disease Classification Method Based on Improved Residual Network
摘要
The number of layers and parameters of traditional residual networks are redundant in the practical application of fruit disease classification,and the original loss function is easy to misidentify similar disease.In order to solve the problem of redundant parameters and low discrimination of similar samples in fruit classification,this paper proposes an improved residual network structure to reduce the number of residual blocks and convolution kernels to reduce the parameters of the convolution layer.At the same time,the inter-class similarity penalty term is added into the original loss function to widen the distance between different classes,so as to improve the classification accuracy of diseases.Experimental results show that compared with the original residual network,the improved residual network reduces the amount of parameters by about 25%,and the recognition accuracy of the improved loss function reaches 92.76%.