# BHI-YOLO: A Lightweight Instance Segmentation Model for Strawberry Diseases
## 4 Experiments and Analysis
### Comparison of Results  
To validate the performance differences between BHI-YOLO and the baseline model across nine categories of strawberries, we used precision-recall (P-R) curves to compare the segmentation results of the two algorithms. The P-R curve for strawberry disease segmentation is presented in Figure 13. It can be noted that the AP for anthracose fruit rot shows the most significant improvement, increasing by 9.4%. The significant improvement is mainly due to the relatively large-scale variation in anthracose fruit rot, where the disease can affect a small part or even the entire fruit. Therefore, compared to other categories, segmenting it is more challenging. With the optimized model, it can better capture global contextual information by adjusting feature weights to effectively adapt to targets of different scales and positions, thus enhancing segmentation performance. In other categories, the improved model shows enhanced segmentation performance across the board. Overall, the improved model has shown an overall 2.3% increase in mAP, outperforming the baseline model. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Parameters (M)** & **GFLOPs** & **Mask mAP50** & **mIoU (\%)** & **Model Size (Mb)** \\ \hline YOLOv8n-seg (baseline) & 3.26 & 12.0 & 90.7 & 74.9 & 6.8 \\ +C2f\_UIB & 2.83 & 10.8 & 91.7 & 76.1 & 6.0 \\ +HSFPN & 2.14 & 10.7 & 91.7 & 76.3 & 4.6 \\ +C2f\_iRNA & 3.29 & 12.1 & 92.4 & 76.8 & 6.9 \\ +C2f\_UIB + HSFPN & 1.68 & 9.3 & 92.4 & 77.1 & 3.7 \\ +C2f\_UIB + HSFPN + C2f\_iRNA & 1.72 & 9.6 & 93.0 & 77.5 & 3.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Ablation experiment results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Parameters (M)** & **GFLOPs** & **Mask mAP50** & **Model Size (Mb)** \\ \hline YOLOv3 tiny-seg & 14.10 & 32.7 & 88.0 & 28.4 \\ YOLOv5s-seg & 7.42 & 25.8 & 91.3 & 15.2 \\ YOLOv6-seg & 4.40 & 15.2 & 86.8 & 9.1 \\ YOLOv9c-seg & 13.8 & 55.7 & 92.9 & 28.6 \\ Mask R-CNN & 44.0 & 190 & 86.2 & 370.2 \\ Cascade-Mask R-CNN & 77.1 & 1713 & 84.5 & 686.0 \\ YOLACT & 35.3 & 68.4 & 82.6 & 278.8 \\ YOLOv8n-seg & 3.26 & 12.0 & 90.7 & 6.8 \\ BHI-YOLO & 1.72 & 9.6 & 93.0 & 3.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Comparison results of different algorithms. To visually compare the segmentation performance before and after model improvements, predictions were made on the test set employing both the baseline model and BHI-YOLO. Figure 14 depicts the instance segmentation results of the YOLOv8n-seg and BHI-YOLO models. The first row in the figure shows manually created truth labels. The instance segmentation outputs of YOLOv8 are presented in the second row, and the results of BHI-YOLO are displayed in the third row. Each column represents different disease characteristics of strawberries. Figure 14 shows that in the segmentation results of YOLOv8, columns 1, 5, 6, and 9 mistakenly detect the background of the images as strawberry diseases. In columns 4 and 8, strawberry diseases are incorrectly identified as another disease with similar features. Columns 2, 3, and 7 exhibit cases of missed detection. After improvement, the model can accurately identify various diseases in strawberries. This is because in complex backgrounds of strawberry disease images, the improved model has enhanced feature extraction capabilities and the ability to express features at different scales. As a result, the improved model exhibits superior adaptability to complex scenarios and performs better in multi-scale instance segmentation. The experimental findings indicate that the BHI-YOLO achieves ultimate lightweighting while possessing superior instance segmentation performance. This fully proves that the improved model not only exhibits stronger robustness but also effectively addresses the challenges of lightweight deployment on mobile terminals, thereby reducing maintenance costs. However, adverse weather conditions like rain and snow may lead to poor lighting, in which case YOLOv8 might perform better.