###### Abstract  
In this study, computer vision applicable to traditional agriculture was used to achieve accurate identification of rice leaf diseases with complex backgrounds. The researchers developed the RiceDRA-Net deep residual network model and used it to identify four different rice leaf diseases. The rice leaf disease test set with a complex background was named the CBG-Dataset, and a new single background rice leaf disease test set was constructed, the SBG-Dataset, based on the original dataset. The Res-Attention module used 3 \(\times\) 3 convolutional kernels and denser connections compared with other attention mechanisms to reduce information loss. The experimental results showed that RiceDRA-Net achieved a recognition accuracy of 99.71% for the SBG-Dataset test set and possessed a recognition accuracy of 97.86% on the CBG-Dataset test set. In comparison with other classical models used in the experiments, the test accuracy of RiceDRA-Net on the CBG-Dataset decreased by only 1.85% compared with that on the SBG-Dataset. This fully illustrated that RiceDRA-Net is able to accurately recognize rice leaf diseases with complex backgrounds. RiceDRA-Net was very effective in some categories and was even capable of reaching 100% precision, indicating that the proposed model is accurate and efficient in identifying rice field diseases. The evaluation results also showed that RiceDRA-Net had a good recall ability, \(F1\) score, and confusion matrix in both cases, demonstrating its strong robustness and stability. Keywords:computer vision; rice leaf disease; complex backgrounds; deep residual; RiceDRA-Net; Res-Attention module +
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identification model was 96.8%, representing a higher accuracy than that of a traditional back-propagation neural network model. In another study by [PERSON] et al. [4], they used random forest to diagnose tomato plant diseases and developed a disease identification system with an overall accuracy of 95%. [PERSON] et al. [5] trained diseased and healthy leaf datasets collectively under random forest to classify diseased and healthy images. [PERSON] et al. [6] used machine learning techniques to detect rice leaf diseases and achieved an accuracy of more than 97% on the test dataset. [PERSON] et al. [7] used a support vector machine approach for rice disease identification and obtained a good result. Although these machine-learning-based models showed good results, they still face some challenges in terms of their practical applications. Several previous studies have investigated the use of deep learning for identifying plant diseases. For instance, [PERSON] et al. [8] proposed using deep learning for image-based plant disease detection. [PERSON] et al. [9] conducted a comparative study on fine-tuning deep learning models for plant disease recognition, adapting and comparing different deep learning models for plant diseases. [PERSON] et al. [10] proposed a novel deep learning model based on initial layers and residual connections, where depth-separable convolution could be used to reduce the number of parameters. [PERSON] et al. [11] proposed a deep convolutional neural network (CNN) for identifying maize leaf diseases, which achieved an accuracy of 97.89%. Similarly, [PERSON] et al. [12] used CNN and migration learning methods to identify plant leaf diseases and achieved better recognition results in their experiments. In [PERSON] et al. 's study [13], a deep learning method was used for strawberry leaf powdery mildew detection using RGB-based images, resulting in 98% classification accuracy. [PERSON] et al. [14] conducted a study on deep learning in apple leaf disease recognition and achieved a recognition rate of 96% in a database of collected apple leaf disease images. [PERSON] et al. [15] introduced transfer learning in plant disease recognition, which uses networks pre-trained on large labeled datasets, such as ImageNet, to initialize weights, rather than randomly initializing weights and training from scratch. [PERSON] et al.