## 5 Conclusions  
In this paper, we construct a model for corn leaf disease and insect detection that integrates multi-scale features and attention mechanisms to detect three corn leaf diseases and five pests. The CBAM attention module is added to improve the ability to extract relevant features of pests and diseases. The MFF module is designed in the neck network, and a weighted bidirectional feature fusion network is employed to enable the model to better detect multi-scale objects. The experimental results demonstrate that the constructed model achieves 85.13% accuracy on the test dataset, which is 9.59% higher than the accuracy of the original CenterNet model. The improved model has a parameter scale of 24.296 M, which is 25.7% smaller than the original CenterNet model. It takes 0.025 s to predict a 512 \(\times\) 512 pixel image, and the processing speed reaches 23.69 f/s, which can meet the requirements of real-time detection. Compared with Faster R-CNN, YOLOv5, SSD-VGG, and EfficientDet-D0 models, the proposed model achieves better detection accuracy under the same experimental conditions. Therefore, this model can achieve rapid and accurate detection of pests and diseases and meet the requirements of pest and disease detection. The model is deployed in a web application that allows users to upload photos for quick detection, providing users with convenient services and practical features to assist farmers in understanding the situation of pests and diseases. In the future, a lightweight model will be designed, and the model structure will be further optimized using distillation technology. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**References** & **Type of Plant** & **Dataset** & **Technique** & **Performance** \\ \hline ([PERSON] et al., 2020) [22] & Cucumber & Private & EfficientNet-B4 & Accuracy: 97\% \\ ([PERSON] et al., 2021) [23] & Strawberry & Private & ResNet50 & Parameters: 268.62 M \\  & & & & Accuracy: 98.11\% \\  & & & & Detect each image: \\  & & & & 0.076 s \\  & & & & Paddy: 97.22\% \\ ([PERSON] et al., 2021) [26] & Paddy Wheat & Dataset provided by & VGG16 & Wheat: 98.75 \\  & & website & & Parameters: 68 M \\ ([PERSON] et al., 2022) & Beans & public dataset & MobileNetV2 & Accuracy: 92.0\% \\ ([PERSON] et al., 2022) [24] & Maize (disease) & PlantVillage, Private & Lightweight & Accuracy: 95.4\% \\  & & & dense-scale network & Parameters: 0.59 \\ ([PERSON] et al., 2021) [25] & Maize (disease) & PlantVillage and & DenseNet & Accuracy: 95.86\% \\  & & private & & Accuracy: 98.52\% \\ ([PERSON] et al.,2022) [21] & Maize (disease) & Crop disease AI & EfficientNet & Accuracy: 98.52\% \\  & & Challenge dataset & & Parameters: 46.21 M \\ ([PERSON] et al., 2022) [28] & Maize (disease) & Private & DISE-Net & Accuracy: 97.12\% \\ ([PERSON] et al.,2022) [20] & Maize (Pest) & private & Pest R-CNN & mAP: 60.2\% \\  & & & & mAP: 85.13\% \\ Ours & Maize (disease and & Dataset provided by & Enhancement of & parameters: 24.296 M \\  & & website & Centernet. & Detect each image: \\  & & & & 0.025 s \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison of proposed advanced pest and disease detection method with other state-of-the-art approaches on maize and other plant datasets. **Author Contributions:** Investigation, J.K.; methodology, W.Z. ; software, W.Z. ; supervision, J.K.; writing--original draft preparation, W.Z. ; writing--review and editing, J.K., W.L. and Y.X. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the Youth Fund of the National Natural Science Foundation of China (Grant No. 62203285) and the Youth Fund of Shaanxi Provincial Natural Science Basic Research Program General Project (Grant No. 2022 JQ-181) and was also funded by the Xi'an Science and Technology Plan Project (Grant No. 23 NYGG0070). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Data sharing is not applicable.