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Rice, a staple food crop worldwide, is pivotal in agricultural productivity and public health. Automatic classification of typical rice pests and diseases is crucial for optimizing rice yield and quality in practical production. However, infrequent occurrences of specific pests and diseases lead to uneven dataset samples and similar early-stage symptoms, posing challenges for effective identification methods. In this study, we employ four image enhancement techniques—flipping, modifying saturation, modifying contrast, and adding blur—to balance dataset samples throughout the classification process. Simultaneously, we enhance the basic <i>RepVGG</i> model by incorporating the ECA attention mechanism within the Block and after the Head, resulting in the proposal of a new classification model, <i>RepVGG_ECA</i>. The model successfully classifies six categories: five types of typical pests and diseases, along with healthy rice plants, achieving a classification accuracy of 97.06%, outperforming <i>ResNet34</i>, <i>ResNeXt50</i>, <i>Shufflenet V2</i>, and the basic <i>RepVGG</i> by 1.85%, 1.18%, 3.39%, and 1.09%, respectively. Furthermore, the ablation study demonstrates that optimal classification results are attained by integrating the ECA attention mechanism after the Head and within the Block of <i>RepVGG</i>. As a result, the classification method presented in this study provides a valuable reference for identifying typical rice pests and diseases.