Apple Disease Recognition Based on Convolutional Neural Networks With Modified Softmax

Abstract

Accurate and rapid identification of apple diseases is the basis for preventing and treating the apple diseases, and is very significant for assessing disease disaster. Apple disease recognition from its diseased leaf images is one of the interesting research areas in computer and agriculture field. An apple disease recognition method is proposed based on modified convolutional neural networks (MCNN). In MCNN, Inception is introduced into MCNN, global average pooling (GAP) operator is employed instead of several fully connected layers to speedup training model, and modified Softmax classifier is used in the output layer to improve the recognition performance. The modified Softmax classifier uses the modified linear element as the activation function in the hidden layer and adds the local response normalization in MCNN to avoid the gradient disappearance problem effectively. A series of experiments are conducted on two kinds of apple disease image datasets. The results show the feasibility of the algorithm.

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