Dual-branch, efficient, channel attention-based crop disease identification

Abstract

Efficient and accurate recognition of crop diseases plays an important role in disease prevention. Aiming at the low accuracy of existing crop disease recognition methods, we improve the attention mechanism and residual neural network (ResNet) to propose a dual-branch, efficient, channel attention (DECA)-based crop disease recognition model. The DECA module uses a dual-branch 1D convolution operation to filter effective feature information. It also introduces an adaptive convolution kernel parameter kand an adaptive parameter αto participate in the reverse training of the model so that the model can independently select effective features and establish dependencies between channels. Then, the DECA module is added to the residual module to recalibrate the channel characteristics and improve the characteristic representability of the residual module. Finally, a DECA_ResNet model is proposed to realize crop disease recognition. The crop disease model based on improved attention is verified on the AI Challenger 2018 dataset, PlantVillage dataset, and self-collected cucumber disease dataset; the disease recognition accuracies are 86.35%, 99.74% and 98.54%, respectively, which are higher than those of the ResNet and squeeze-and-excitation network (SENet) before improvement. The recognition accuracy of the proposed model in the PlantVillage dataset is higher than that of existing models. The experimental results show that the proposed plant recognition model with 18 layers (DECA_ResNet18) can achieve a high recognition accuracy. The DECA module can independently select each branch feature without causing a significant increase in the number of parameters, which improves the plant disease recognition accuracy and reduces the extraction of redundant features.