Objective The yield of rice is related to food security of all mankind. How to effectively prevent and detect rice diseases and pests is an important topic in the field of smart agriculture. Deep learning has become the preferred method for identifying rice diseases and pests due to its excellent performance in self-learning image features. However, in natural environments, the dataset is relatively small and susceptible to complex backgrounds, resulting in overfitting and difficulty in extracting subtle features during training. This study aims to address the aforementioned issues.

Method We proposed a rice disease and pest identification model with multi-scale dual branch structure based on improved ResNet (MSDB ResNet). On the basis of the ResNet model, ConvNeXt residual blocks were introduced to optimize the calculation proportion of residual blocks, construct a dual branch structure, and extract disease features of different sizes from the input disease image by adjusting the convolution kernel size of each branch. In response to issues such as complex real world environments, small datasets, and overfitting, a total of 5932 rice pest and disease images captured from natural environments was utilized. Using data preprocessing methods such as random brightness and motion blur, as well as data augmentation methods such as mirroring, cropping, and scaling, the dataset was expanded to 20000 pictures. The MSDB-ResNet model was trained to identify four common rice diseases.

Result MSDB-ResNet had good recognition performance on rice disease and pest datasets, with a recognition accuracy of 99.10%, which was 2.42 percentage points higher than the original ResNet model and obviously superior to classic networks such as AlexNet, VGG, DenseNet, ResNet, etc. This model had good generalization ability and strong robustness.

Conclusion The MSDB ResNet model is feasible and progressiveness in the identification of rice diseases and pests, which provides a reference for the identification of rice diseases and pests under complex background.