###### Abstract
[16] explored the identification and classification of plant diseases in leaf images using deep learning (DL) and machine learning (ML) algorithms. [PERSON] et al. [17] studied plant leaf disease classification using the EfficientNet deep learning model. [PERSON] et al. [18] achieved a good result using a deep convolutional neural network to estimate weed density in a soybean crop using smart agriculture. [PERSON] et al. [19] used hybrid convolutional neural networks to recognize allopatric disease by applying feature reduction. However, most of these studies focused solely on identifying the presence of diseases without accurately locating the diseases in plants. Thus, the proposed Res-Attention mechanism can better solve this problem and improve the accuracy of disease identification. Additionally, some studies have investigated the use of attention mechanisms for object recognition in deep learning. For example, [PERSON] et al. [20] proposed a squeeze and excite (SE) block, which uses attention mechanisms to enhance feature maps. Similarly, [PERSON] et al. [21] proposed a lightweight attention-based convolutional neural network for tomato leaf disease classification to improve the accuracy of image classification. In [PERSON] et al. 's study [22], the attention mechanism was embedded into a residual network for plant disease severity detection. Inspired by these studies, we integrated the Res-Attention mechanism into RiceDRA-Net, thus improving the model's ability to identify rice leaf diseases. Several studies have investigated the use of deep learning for identifying rice diseases. For example, [PERSON] et al. [23] proposed using CNN and migration learning to classify rice leaf diseases. They created a small dataset of rice diseases and used migration learning to develop a deep learning model that achieved an accuracy of 92.46%. Similarly, [PERSON] et al. [24] used a convolutional neural network to identify rice diseases by training using 3500 images of healthy and diseased rice leaves, achieving an accuracy of nearly 70%. [PERSON] et al. [25] proposed a two-stage small CNN architecture for rice pest identification and detection. [PERSON] et al. [26] proposed a fast disease detection method for rice based on the fusion of FCM-KM and Faster R-CNN. [PERSON] et al. [27]used deep learning techniques and mobile device targets for rice leaf disease classification and achieved an accuracy of 81.87% for the training data and 81.25% for the validation data. [PERSON] et al. [28] proposed a new method to improve the computational and classification performance of rice disease identification. [PERSON] et al. [29] proposed a multimodal data fusion framework for rice disease diagnosis, Rice-Fusion. However, these studies all used a relatively simple CNN architecture and did not explore the use of attention mechanisms. Therefore, using RiceDRA-Net with the Res-Attention mechanism can be highly effective in improving the recognition of rice leaf diseases. None of the models for recognizing rice leaf diseases discussed above have employed attention mechanisms to enhance accuracy. Furthermore, most have focused solely on identifying the presence of disease without precisely locating it in the plant. Moreover, the models proposed previously are typically trained in a single context and do not guarantee reliable recognition in more complex contexts. To address these shortcomings, we have developed a novel approach that offers three key contributions:  
1. The novel RiceDRA-Net with a denser residual structure and a Res-Attention mechanism was proposed, which effectively improves the accuracy, robustness, and disease localization capabilities of the model for identifying rice leaf diseases. 2. A new single-background rice leaf disease dataset, SBG-Dataset, was constructed. 3. RiceDRA-Net has better recognition capabilities in rice leaf disease identification with a complex background compared with other classical models.