## 6 Conclusions  
1. Addressing the impact of complex paddy field environments and other interfering factors on seedling missing detection, as well as various scenarios such as isolated missing seedlings, continuous missing seedlings, and floating seedlings with missing seedlings, this paper proposes a centroid detection method. By calculating the distance between adjacent seedlings and selecting appropriate thresholds, it can swiftly locate and assess the missing seedling situation between adjacent seedlings, achieving an overall accuracy of 93.7%.
2. For the detection of seedling qualification and floating status, this paper improves upon the YOLOv8n model. Extraction of multi-scale feature information of seedling status using ASPP structure to replace SPPF structure. Additionally, a SimAM attention module is added to the backbone network to enhance the extraction of crucial information. Moreover, the introduction of AFPN enhances feature fusion efficiency while maintaining network lightweight, thereby further improving detection performance.
3. Based on the actual conditions of paddy fields, this paper constructs datasets for seedling qualification and floating status to train, validate, and test the model. Compared with the original model, the improved YOLOv8n model in this study shows enhancements in accuracy, recall rate, and average precision of seedling recognition by 3.6%, 0.9%, and 1.7%, respectively. Despite the increase in model complexity, the weight size has increased by 2.5 MB, and the FPS has decreased to 113. However, it still meets the requirements for lightweight and real-time detection. The seedling planting status recognition model consists of centroid detection and the YOLOv8 network. This model demonstrates good performance in seedling detection under actual field conditions and can provide a reference for monitoring the quality of transplanting operations and the development of unmanned rice field management in the future.
4. The study in this paper shows that this assay has high accuracy and stability in detecting the rice seedling planting status. Crops such as corn, wheat, and soybean, for example, also require an insertion process, and the method could be considered for application to these crops in the future. It can also be used for intelligent applications such as detecting the growth status and health of crops, farm monitoring, and management.  
Overall, different agricultural environments may have conditions such as light, soil type, humidity, and temperature, which may affect the performance of the detection algorithm. Therefore, applying the method to different agricultural environments and evaluating its performance and stability are important directions for future research.  
Conceptualization, B.Z. ([PERSON]); methodology, B.Z. ([PERSON]) and Q.Z.; validation, Q.Z.; writing--original draft preparation, Q.Z.; writing--review and editing, B.Z. ([PERSON]), Q.Z., Y.L., Y.C. and B.Z. ([PERSON]); investigation, B.Z. ([PERSON]); supervision, Y.L. and Y.C.; funding acquisition, B.Z. ([PERSON]) and Y.L. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key R&D Program of China, Grant No. 2021 YED2000601.  
Not applicable.  
Data Availability Statement: The data of the experimental images used to support the findings of this research are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy.  
The authors would like to extend their appreciation to National Key Laboratory of Agricultural Equipment Technology, Beijing and Qingdao Agricultural University, Qingdao, for supporting the project possible.  
The authors declare no conflicts of interest.