To address the limitations of traditional rice pest and disease recognition methods, such as low efficiency and susceptibility to subjective interference, as well as the shortcomings of existing deep learning models in capturing subtle features of rice pests and diseases and handling category-imbalanced data, a series of research and practical efforts have been undertaken. Firstly, pest and disease images were collected in rice fields using AR glasses. A rice pest and disease dataset was then constructed by combining these images with the publicly available dataset IP102 and images sourced from the web. Data augmentation techniques were employed to expand the training samples, thereby mitigating issues related to category imbalance and image quality. Secondly, based on the EfficientNet-V2 model, the CBAM (Convolutional Block Attention Module) attention mechanism was introduced to replace the original SE (Squeeze-and-Excitation) module, aiming to enhance the model’s ability to capture detailed features of rice pests and diseases. Additionally, the PolyLoss loss function was adopted to optimize the learning process for unbalanced data, leading to the construction of the EfficientNet-V2-Rice model for rice pest and disease recognition. Finally, leveraging the improved recognition model, a companion intelligent recognition APP for Android smartphones was developed. This APP boasts a rich set of features, integrating core modules such as user registration and login, image uploading, intelligent recognition, retrieval of recognition results, and viewing of detailed information. Users can simply capture images of rice pests and diseases using their smartphone cameras and upload them to the APP to quickly obtain accurate recognition results. They can also retrieve and view detailed information about historical recognition records at any time. To verify the effectiveness of the model improvement strategy, ablation and comparison experiments were conducted. The experimental results demonstrate that the proposed EfficientNet-V2-Rice model performs exceptionally well in rice pest and disease recognition tasks, achieving precision, recall, and F₁ scores of 84.92%, 86.00%, and 85.45%, respectively. The Android smartphone APP developed based on this model provides users with convenient and efficient recognition services, offering a practical tool for the intelligent monitoring and auxiliary diagnosis of rice pests and diseases.