Multi-channel feature fusion networks with hard coordinate attention mechanism for maize disease identification under complex backgrounds

Abstract

Maize leaf disease has a negative impact on food security. If maize leaf disease is not controlled in time, it can spread and affect corn quality and yield. Severe maize leaf disease may even result in no harvest at all. In the existing deep learning technology for maize leaf disease recognition, due to the complex growing environment of maize, it is easy to capture the complex background of maize in the image acquisition process, which brings difficulties to the recognition of maize leaf disease. Therefore, we propose a new network architecture, HCA-MFFNet, for maize leaf disease recognition in a complex background. In HCA-MFFNet, hard coordinated attention (HCA) assigned at different spatial scales was used to extract features from maize leaf disease images to reduce the influence of complex backgrounds. In order to retain the feature information of maize leaf disease in the sampling process to the maximum extent, a multi-feature fusion network that can extract the weight information in two spatial directions was constructed. The traditional convolutional layer was replaced by depthwise separable convolutional layer, reducing the number of parameters in the network. In order to verify the feasibility and effectiveness of this model in complex environment, we compare it with existing methods. The average recognition accuracy of the model is 97.75%, and the F1 value is 97.03%. The proposed method is better than the existing methods.