## 3 Results  
### Ablation Experiments and Model Training Details  
#### 3.1.1 Ablation Experiments  
This paper constructs ablation comparative experiments to verify the efficacy of optimization techniques, ensuring there is no competition or conflict among various methods, and that resource utilization is optimal to avoid inconsistencies and confusion in the model training and decision-making process. Two distinct optimization approaches were individually integrated into the original YOLOv8-seg network for comparative analysis of YOLOv8-seg, BiFPN YOLOv8-seg, DSConv YOLOv8-seg, and BiDS-YOLOv8-seg networks using the publicly available coco128 dataset, training 50 batches, as shown in Table 4.  
The introduction of BiFPN enhanced the model's precision, recall, mAP50, and mAP50:95 to 91.4%, 83.6%, 88.9%, and 64.1%, respectively, showing improvements over the original model by 1%, 2.5%, 1.8%, and 0.4%. This indicates that BiFPN effectively enhances the model's ability to fuse features across different scales. The introduction of DSConv resulted in an 8% increase in precision and a 1.6% increase in mAP50 compared to the original model, indicating that deformable convolutions perform well in segmenting object edges, beneficial for the segmentation of weed stems and irregular leaf surfaces. However, recall and mAP50:95 are similar to the original network, suggesting that DSConv's feature recognition across multiple categories is somewhat limited, leading to poor balance, and, thus, should not be used in isolation. The mAP50 curves for different models across training batches are shown in Figure 7. The BiDS-YOLOv8-seg model, formed by the fusion of BiFPN, DSConv, and YOLOv8-seg, exhibits the best detection performance with significant improvements in all metrics, achieving a precision of 91.7%, recall rate of 83.5%, and mAP50 of 89.3%. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **Precision** & **Recall** & **mAP50** & **mAP50-95** \\ \hline YOLOv8-seg & 0.904 & 0.811 & 0.875 & 0.637 \\ BiFPN + YOLOv8-seg & 0.914 & 0.836 & 0.889 & 0.641 \\ DSConv + YOLOv8-seg & 0.912 & 0.811 & 0.887 & 0.636 \\ BiFPN + DSConv + TOLOv8-seg & 0.917 & 0.835 & 0.893 & 0.640 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance of YOLOv8-seg combined with various optimization techniques. 3.1.2. Training Results for the BFFDC-YOLOv8-Seg  
In the present study, we trained the BFFDC-YOLOv8-seg on a vegetable field weed dataset, with Figure 8 detailing various performance metrics during the training and validation processes. During model training, we observed that the Box Loss (box_loss) rapidly decreased from a high initial value to below 0.2 and converged, demonstrating high precision in detecting weed boundaries without overfitting. The Segmentation Loss (seg_loss) also showed a significant decreasing and converging trend, validating the model's effectiveness in segmentation tasks. The decrease in Classification Loss (cls-loss) indicated a gradual enhancement in the model's reliability for object classification. The reduction in the specific loss metric, Direction/Flow Loss (dfl_loss), reflected the model's learning capability in predicting object direction or flow. During model validation, all loss metrics showed a converging trend similar to training, with final stable values slightly higher than training results, indicating that the model also has good generalization capabilities on data outside the training set. Moreover, the Mask's precision (precision(M)) and recall (recall(M)) remained above 0.9, with the mean average precision at 50% IoU (mAP50) exceeding 0.9, and at a stricter 50-95% IoU (mAP50-95) also demonstrating performance above 0.8, further confirming the model's strong segmentation performance and robustness under rigorous evaluation standards.