The Detection of Maize Leaf Disease Based on an Improved Real-Time Detection Transformer Model

Maize is one of the most important global crops. It is highly susceptible to diseases during its growth process, meaning that the timely detection and prevention of maize diseases is critically important. However, simple deep learning classification tasks do not allow for the accurate identification of multiple diseases present in a single leaf, and the existing RT-DETR (Real-Time Detection Transformer) detection methods suffer from issues such as excessive model parameters and inaccurate recognition of multi-scale features on maize leaves. The aim of this paper is to address these challenges by proposing an improved RT-DETR model. The model enhances the feature extraction capability by introducing a DAttention (Deformable Attention) module and optimizes the feature fusion process through the symmetry structure of spatial and channel in the SCConv (Spatial and Channel Reconstruction Convolution) module. In addition, the backbone network of the model is reconfigured, which effectively reduces the parameter size of the model and achieves a balanced symmetry between the model precision and the parameter count. Experimental results demonstrate that the proposed improved model achieves an [email protected] of 92.0% and a detection precision of 89.2%, representing improvements of 7.3% and 8.4%, respectively, compared to the original RT-DETR model. Additionally, the model’s parameter size has been reduced by 18.9 M, leading to a substantial decrease in resource consumption during deployment and underscoring its extensive application potential.