## 7 Conclusions  
Deep learning models are commonly used to detect insect pests in plants. However, a major problem is low accuracy when real-world images are presented to the model. In this study, after conducting a comprehensive comparison of the eight frameworks from DL, we developed an object detection system that can detect insect pests in digital images in real-time using the single-stage object detection architectures of YOLO-Lite, YOLOv3, YOLOR, and YOLOv5 with five different scales (n, s, m, l, and x). To implement the proposed approach, we created a new IP-23 dataset with twenty-three classes. We performed training, validation, and testing with our IP-23 dataset on an Asus computer with Intel(R) Core (TM) i7-11370H CPU @ 3.30 GHz (8 CPU) 3.3 GHz, 16 G RAM, GPU NVIDIA GeForce RTX 3060, 6 G video memory and GPU acceleration software CUDA11.1 and CUDNN 8.0.5. We also used matpool cloud GPU RTX NVIDIA 2080 Ti for training the models with CUDA11.0, and CUDNN 8.0, and then we test our developed model both on our personal Asus computer and on the IP camera of our smartphone. Among the counter models, YOLOv5x was found the most successful model with a significant result. In terms of detection rate, our developed YOLOv5x model with trained parameters achieved an average precision value ([EMAIL_ADDRESS]) of 98.3%, ([EMAIL_ADDRESS]:0.95) of 79.8%, a precision value of 94.5%, and recall value of 97.8%, and F1 score of 96%. In addition, the YOLOv5x model is fully functional and capable of correctly identifying the twenty-three species of insect pests with a detection rate of 40.5 milliseconds (ms). The results revealed that our proposed approach performs better on both smartphone IP cameras and our PC which can be used for realistic applications in agriculture. We have also observed some false negative detections due to shape, background, and occlusion which can be improved by adding more images with different complex scenarios, environments, and complex orchards. The accuracy of the model can be improved by adding more images to the dataset and applying pre-processing and data augmentation methods such as cropping, adding brightness, noise, and blur to the image. In our future work, we will increase the amount of data in each class with a few samples and add more insect pest species. To put our insect pest detection system into practice, we will embed the model into mobile devices such as Android and IOS, which could be useful for promoting agricultural production. The accuracy of the object detection system can be improved by increasing the size of the dataset using data augmentation methods. Moreover, the dataset can be expanded by adding images of the different pest species to detect the pests in earlier periods. Figure 14: Common problems encountered in our object detection system. **Author Contributions:** Conceptualization, I.A. and Y.W. ; methodology, Y.Y. ([PERSON]); software, I.A. ; formal analysis, Y.Y. ([PERSON]); investigation, C.Y. and I.A. ; resources, I.A. ; data curation, I.A. ; writing--original draft preparation, I.A. ; writing--review and editing, I.A., X.C. and M.H. ; visualization, I.A. ; supervision, Y.W. ; project administration, Y.W. ; funding acquisition, Y.W. and Y.Z. All authors have read and agreed to the published version of the manuscript. **Funding:** This work was supported by the Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information Open Fund Project (BDSYS2021003); 2020 Natural Science Foundation of Anhui Province (2008085 QF293); Special Fund for Anhui Characteristic Agriculture Industry Technology System (2021-2025). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Acknowledgments:** We thank to Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information for supporting our work and provide funding to us. We also thank all of the authors of the primary studies includes in this article.