# A Lightweight YOLOv8 Model for Apple Leaf Disease Detection
## 3 Results
### Evaluation of Various Target Detection Algorithms  
Table 6 compares various target detection models based on several performance metrics, including precision, recall, mean average precision at an IoU threshold of 0.5 ([EMAIL_ADDRESS]%), floating point operations per second (FLOPs), the number of parameters, and model weight size. Our model demonstrates substantial improvements across precision and recall rates compared to YOLOv7-Tiny, YOLOv6, YOLOv5s, and YOLOv3-Tiny. Specifically, we achieve a 11.9%, 13.1%, 8.4%, and 14.2% increase in precision and a 5.8%, 4.3%, 6.4%, and 8.9% increase in recall, respectively, over these models. Moreover, our model achieves these performance gains while significantly reducing computational demands. It operates with  
Figure 14: Test results of YOLOv8n and YOLOv8n-GGi: (**a**) original image, (**b**) YOLOv8n detection results, and (**c**) YOLOv8n-GGi detection results.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **[EMAIL_ADDRESS] (\%)** & **FLOPs (G)** & **Parameters (M)** & **Weight Size (MB)** \\ \hline YOLOv8n & 83.1 & 77.9 & 83.5 & 8.2 & 3.2 & 6.3 \\ YOLOv8n-GhostNet & 80.5 & 75.0 & 82.1 & 7.7 & 2.8 & 5.4 \\ YOLOv8n-GhostNet-GGhost & 80.0 & 73.1 & 80.7 & 6.2 & 2.3 & 4.9 \\ YOLOv8n-GhostNet-GAM & 84.9 & 77.9 & 83.4 & 6.7 & 2.4 & 5.1 \\ YOLOv8n-GhostNet-GAM- & 86.3 & 80.2 & 86.9 & 5.5 & 1.7 & 3.8 \\ improved BiFPN & \multicolumn{1}{c}{} & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: Ablation experiment results.  
only 5.5 G FLOPs, which is notably lower compared to YOLOv7-Tiny by 13.2 G, YOLOv6 by 11.8 G, YOLOv5 s by 23.8 G, and YOLOv3-Tiny by 18.9 G. This efficiency makes our model highly suitable for real-time applications and resource-constrained environments. YOLOv10 has significant improvements from YOLOv8 in terms of accuracy, inference speed, and the model's light weight. Although our model is 1.8% lower in accuracy and 5.0% lower in recall than YOLOv10, our model has about one-fourth of the floating-point operations of YOLOv10. The model size is much smaller than other models. These results show that our model significantly reduces performance while maintaining high accuracy by introducing GhostConv and C2 fGhost into the backbone network, introducing GAM (Ghost Attention Mechanism), and adding improved BiFPN (Bidirectional Feature Pyramid Network) to optimize feature fusion. It reduces the computational cost and model complexity and is suitable for realizing efficient real-time target detection tasks in resource-limited hardware environments.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **Precision (\%)** & **Recall (\%)** & **[EMAIL_ADDRESS] (\%)** & **FLOPs (G)** & **Parameters (M)** & **Weight Size (MB)** \\ \hline YOLOv3-Tiny & 72.1 & 71.3 & 76.4 & 18.9 & 12.1 & 24.4 \\ YOLOv5s & 77.9 & 73.8 & 78.8 & 23.8 & 9.1 & 18.5 \\ YOLOv6 & 73.2 & 75.9 & 79.7 & 11.8 & 4.2 & 8.7 \\ YOLOv7-Tiny & 74.4 & 74.4 & 78.0 & 13.2 & 6.0 & 12.3 \\ YOLOv8n & 83.1 & 77.9 & 83.5 & 8.2 & 3.2 & 6.3 \\ YOLOv10s & 88.1 & 85.2 & 88.7 & 21.3 & 7.1 & 14.1 \\ Ours & 86.3 & 80.2 & 86.9 & 5.5 & 1.7 & 3.8 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative experiments with different models.  
Figure 15: Heat map of YOLOv8n and YOLOv8n–GGi: (**a**) original image and (**b**) YOLOv8n, (**c**) YOLOv8n–GGi.