## 4 Discussion  
In research on automated laser weeding, many scholars focus on object detection algorithms, neglecting the coarseness of bounding box localization, which can lead to accidental damage to seedlings and incomplete eradication of weeds. Conversely, with significant improvements in the efficiency and portability of segmentation algorithms, instance segmentation techniques are more advantageous for locating weeds in complex environments. [PERSON] et al. (2019) reported an mAP50 of 92.2% using an improved YOLOv8-seg model for segmenting tomato diseases at different stages, demonstrating significant potential of the YOLOv8 series in segmentation tasks.  
Feature extraction of small and irregular objects has always been a hot topic in computer vision, with multiscale feature fusion and dynamic deformable convolutions being the most common in addressing segmentation of such objects. In agricultural production, there are many applications for the segmentation of small targets, such as determining the extent of infection in plants by segmenting and locating plant pests and diseases. There  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Box**} & \multicolumn{4}{c}{**Mask**} & \multirow{2}{*}{**FPS**} \\ \hline
**Precision** & **Recall** & **mAP50** & **mAP50-95** & **Precision** & **Recall** & **mAP50** & **mAP50-95** \\ \hline
0.974 & 0.924 & 0.958 & 0.916 & 0.974 & 0.924 & 0.958 & 0.817 & 24.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Testing results on Jetson Orin nano.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **mAP50** & **mAP50-95** & **FPS** & **Size (MB)** \\ \hline Mask RCNN & 0.895 & 0.876 & 0.88 & 0.682 & 34 & 228 \\ YOLOv5-seg & 0.701 & 0.781 & 0.854 & 0.593 & 227 & 4.2 \\ YOLOv7-seg & 0.917 & 0.95 & 0.975 & 0.749 & 18.3 & 76.4 \\ YOLOv8-seg & 0.926 & 0.894 & 0.96 & 0.776 & 270 & 6.8 \\ Ours & 0.975 & 0.975 & 0.988 & 0.842 & 101 & 6.8 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison results of different segmentation model performances.  
fore, precise segmentation of irregular small targets in complex backgrounds remains a significant challenge in agricultural applications.  
The present study demonstrates that the BFFDC-YOLOv8-seg effectively enhances weed segmentation capabilities in complex agricultural environments, making it suitable for small, cost-effective, automated laser weeding devices and capable of efficiently detecting and segmenting weeds in vegetable fields. Although this research has made some progress, many key technical issues still need to be addressed to make laser-targeted weeding equipment more aligned with practical needs: a more comprehensive dataset of field weeds needs to be established to increase the diversity of feature collection and further enhance the model's accuracy and generalizability; the model's detection precision needs further optimization and its complexity reduced to meet the real-time demands and resource constraints of practical applications; image coordinates obtained from the model's segmentation need to be integrated with a positioning algorithm for precise target localization; a mismatch between the speed of the laser source and detection efficiency leads to reduced efficiency, necessitating a more efficient laser source. Although this research has achieved certain results, further optimization of the model's detection precision and reduction in complexity remains a focus. We hope that our efforts will lead to widespread adoption of laser weeding devices equipped with the BFFDC-YOLOv8-seg model in agricultural production, promoting sustainable green agriculture.