Apple leaf disease recognition method based on Siamese dilated Inception network with less training samples

Abstract

The application of existing deep learning networks may prove difficult when datasets are small. Siamese network can achieve better accuracy on small dataset. A recognition method of apple leaf disease based on Siamese dilated Inception network (SDINet) with few training samples is proposed. Dilated Inception module is introduced into AlexNet to construct two subnetworks for SDINet, and two subnetworks are responsible for extracting multi-scale features from image pair in the same layer, and the global pooling instead of the fully connected layers are utilized to reduce the number of model parameters and ensure that the features are not lost. SDINet is trained with image pairs, each pair consisting of two real diseased leaf images or one real and one healthy leaf image. SDINet makes full use of the advantages of multi-scale dilated Inception to enrich and improve the information, enhance the adaptability of the model. Different from the existing deep CNNs, SDINet uses cosine distance learning to calculate the similarity between the leaf image pairs to recognize apple diseases. Experimental results on the apple diseased leaf image dataset validate that the proposed method is effective to recognize apple leaf disease using a small number of training samples.