Identification of Apple Leaf Disease Based on Dual Branch Network.

Due to the complex background environment and the similarity of disease spots, there are subtle interclass differences and large intra-class differences in the pathological images of apple leaves, which causes great difficulties in the identification of apple leaves diseases. To solve this problem, a new dual branch network (DBNet) for apple leaf pathology identification is proposed. DBNet is composed of multi-scale branch (MS) and multidimensional attention branch (DA). MS branch fuses pathological features of different scales and levels through different types of convolution kernel and cross-layer connections, so as to alleviate the impact brought by complex background environment. At the same time, the DA branch makes the network pay attention to the small differences between disease spots by integrating the attention of three different dimensions, namely width, height and channel, and automatically changes the proportion of the attention of three dimensions with the deepening of the network layers. This branch is used to alleviate the adverse effects caused by the similarity of some disease spots. Finally, DBNet integrates multi-scale features and multi- dimensional features extracted from the dual- branch network. In addition, the experimental comparison with AlexNet, VGG-16, ResNet-50 and B-CNN on the apple leaf pathology dataset shows that the proposed method can effectively improve the recognition accuracy. [ABSTRACT FROM AUTHOR]