## 4 Conclusions  
In order to solve the problems that the simulation post-processing of a cleaning chamber almost completely depends on empirical knowledge, the airflow in the cleaning chamber was analyzed based on the object detection algorithm and the reasoning mechanism in this paper. The main conclusions are as follows.  
The cls_loss and dfl_loss of YOLOv8l were slightly lower than those of other models when comparing the loss curves and mAP curves of the training process of the five models of YOLOv8n, YOLOv8s, YOLOv8m, YOLOv8l, and YOLOv8x. YOLOv8l had higher precision and recall than other models, and the convergence curve was smoother and had smaller oscillations.  
The YOLOv8l model was used to identify the key points of the scatter plot of the key cross-section of the cleaning chamber. The errors between the key-point model identification results and the manual marking results were less than 2.5%.  
The key areas of the flow field in the cleaning chamber were divided through the reasoning mechanism and the key points identified by YOLOv8l. Then, the airflow analyses within the regions were carried out based on the relevant knowledge and the post-processing reasoning mechanism, and the airflow distribution characteristics of six regions in the cleaning chamber were obtained. Zhendao 18 rice was selected as the cleaning object. The results of airflow analyses in six regions were obtained as follows: On the upper-sieve surface, the wind velocity in the front section is too low, which can easily cause low cleaning efficiency; the wind velocity in the middle section is too low, which can easily cause high impurity content; and the wind velocity in the tail section is too high, which can easily cause a high loss rate. There is a vortex in the area near the feed port, and the regional wind velocity is about 0.95 m/s, which is not conducive to material transfer; moreover, there is the hidden danger that the material is affected by the eddy, which causes accumulation, but the hidden danger is negligible because of the small strength of the eddy current. There is no eddy in the area near the grain collection site, and the regional wind velocity is about 1.1 m/s, which does not affect the grain collection work.  
**Author Contributions:** Conceptualization, Q.L. and R.W.; methodology, Q.L., R.W., Z.W., Y.Y. and X.Z.; software, R.W. and Z.W.; validation, Q.L., R.W. and Z.W.; formal analysis, Q.L. and R.W.; data curation, Z.W., Y.Y. and X.Z.; writing--original draft preparation, Q.L., R.W., Z.W., Y.Y. and X.Z.; writing--review and editing, Q.L., R.W. and Z.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (61771224) and Priority Academic Program Development of Jiangsu Higher Education Institutions (No. PAPD2023-87).  
Not applicable.  
**Data Availability Statement:** The data that support the findings of this study are available upon request from the corresponding author. The data are not publicly available due to privacy restrictions.  
We gratefully acknowledge our colleagues at the School of Agriculture of Jiangsu University for their assistance. All the authors acknowledge and thank their respective institutes and universities for providing library facilities.  
The authors declare no conflicts of interest.