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Highlights

The attention mechanism enhances the ability of the model to learn specific semantic information in encoder.

Abstract. Rice diseases adversely affect rice growth and yield. Precise spot segmentation helps to assess the severity of the disease so that appropriate control measures can be taken. In this article, we propose a segmentation method called AFU-Net for rice leaf diseases, and its performance is verified through experiments. Based on the traditional UNet, this method incorporates an attention mechanism, a residual module and a feature fusion module (FFM). The attention mechanism is embedded in skip connections, which enhances the learning of particular semantic features in the encoder layer. In addition, the residual module is integrated into the decoder layer, which deepens the network and enables it to extract richer semantic information. The proposed FFM structure effectively enhances the learning of boundary information and local detail features. The experimental results show that the mean intersection over union (mIoU), mean pixel accuracy (mPA) and Precision of the proposed model on the self-built rice leaf disease segmentation dataset are 87.25%, 92.23%, and 99.67%, respectively. All three evaluation indexes were improved over the control group, while the proposed model had the lowest number of parameters and displayed a good segmentation effect for smaller disease points and disease parts with less obvious characteristics.