# SMR-RS: An Improved Mask R-CNN Specialized for Rolled Rice Stubble Row Segmentation
## 4 Conclusions  
1. This paper presented a novel approach to improving a model without adding to or replacing any existing advanced modules on the original model. The model was improved by analyzing the effects of the individual components of the model. Firstly, based on the functions of the first stage and second stage, two of the three functional branches in the second stage of Mask R-CNN are removed, and only the mask generation branch is retained, and the region proposals output from the first stage was directly used as the mask generation region to obtain a preliminary simplified model SMR. The key parameter affecting its segmentation quality was obtained through theoretical analysis and experiments, and its segmentation performance was greatly improved by simple adjustment of this parameter. Further, a theoretical and experimental analysis of the extent to which the SMR utilized the feature map in the task of segmenting rolled rice stubble rows was carried out as a basis for simplifying the backbone network to obtain SMR-RS.
2. SMR-RS can be seen as a single-stage model with a mask generation module attached but still implements instance segmentation. Compared to Mask R-CNN, SMR-RS ran 41.4% faster, with a single image processing time of only 77.4 ms, meeting the real-time requirements of fieldwork; 36.8% fewer parameters and 36.3% less GPU memory occupation, making it more suitable for deployment on mobile devices; the IoU value of SMR-RS reaches 0.843, and the F1 value was 0.915. Compared to other  
Figure 11: Mask prediction results of five models for four images from the test set: (**a**) Yellowish; (**b**) Greenish; (**c**) Heavily straw-covered; (**d**) Sparse.  
advanced instance segmentation models, SMR-RS also achieved a significant reduction in prediction time consumption and hardware resource usage with slightly better segmentation results. The model improvement methods in this paper can provide a reference for other researchers, and the proposed instance segmentation model can provide technical support for the implementation of automatic row alignment for stubble-lifting machines.  
In addition, SMR-RS is also highly extensible, and the model's performance was further improved by using more advanced modules, which is one of the directions for future research.  
Conceptualization, Y.L. and P.F.; data curation, Y.L., L.X., Z.L. and M.L.; formal analysis, P.F., L.X., J.C., X.C., J.Y. and J.L.; funding acquisition, P.F. and L.X.; methodology, Y.L., P.F., L.X. and Z.L.; project administration, P.F., and L.X.; supervision, Z.L. and M.L.; visualization, Y.L., Z.L. and J.Y.; writing--original draft, Y.L., L.X., P.F., Z.L., M.L., X.C. and J.Y.; writing--review and editing, P.F., Y.L. and L.X. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China, grant number 31971799; Science and Technology Research Project of Jiangxi Educational Committee, grant number GJJ2200415.  
Not applicable.  
Please contact the corresponding author for the model code and experiment data.  
We are thankful to [PERSON], [PERSON], and [PERSON], who have contributed to our data collection.  
The authors declare no conflict of interest.