# YOLOv8-GO: A Lightweight Model for Prompt Detection of Foliar Maize Diseases
## 3 Results
### Experimental Analysis
#### 3.3.4 Ablation Experiment  
After the above comparison, this paper uses the GAM and ODConv to optimize the basic model. To fully reflect the performance of the model, 300 epochs of training were carried out for each item. The results are shown in Table 9.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Convolution Optimization** & **Precision** & **Recall** & **mAP@50** & **mAP@50:90** & **GFLOPs** \\ \hline YOLOv8n & 0.871 & 0.759 & 0.839 & 0.623 & 8.1 \\ SCConv-N 1 & 0.877 & 0.750 & 0.834 & 0.585 & 7.8 \\ SCConv-B 2 & 0.841 & 0.766 & 0.836 & 0.619 & 7.8 \\ ODConv-N 1 & 0.895 & 0.769 & 0.856 & 0.612 & 7.8 \\ ODConv-B 2 & 0.819 & 0.784 & 0.844 & 0.589 & 6.9 \\ AKConv-B 2 & 0.851 & 0.766 & 0.837 & 0.623 & 7.8 \\ \hline \hline \end{tabular}  
* \({}^{1}\) “-N” indicates that the convolution structure of the Neck part has been modified; 2 “-B” indicates that the convolution structure of the Backbone part has been modified.  
\end{table}
Table 6: Comparison of convolution optimization solutions.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modify Location** & **Precision** & **Recall** & **mAP@50** & **mAP@50:90** & **GFLOPs** \\ \hline YOLOv8n & 0.871 & 0.759 & 0.839 & 0.623 & 8.1 \\ Conv1 & 0.852 & 0.771 & 0.839 & 0.593 & 7.8 \\ Conv2 & 0.895 & 0.76 & 0.856 & 0.602 & 7.8 \\ Conv1 + 2 & 0.847 & 0.755 & 0.833 & 0.610 & 7.4 \\ \hline \hline \end{tabular}  
* Previous experiments have shown that modifying only the convolution module of the exit position yields better results. On this basis, to verify the impact of the location of the ODConv-C2f module on network performance, this paper compares using ODConv-C2f only in the Backbone part, using ODConv-C2f only in the Neck part, and using ODConv-C2f in both the Backbone part and the Neck part. The results are shown in Table 8.  
\end{table}
Table 7: Bottleneck optimization position comparison.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Modify Location** & **Precision** & **Recall** & **mAP@50** & **mAP@50:90** & **GFLOPs** \\ \hline YOLOv8n & 0.871 & 0.759 & 0.839 & 0.623 & 8.1 \\ Backbone & 0.840 & 0.783 & 0.834 & 0.609 & 7.5 \\ Neck & 0.895 & 0.760 & 0.856 & 0.602 & 7.8 \\ Backbone + Neck & 0.848 & 0.756 & 0.833 & 0.591 & 7.2 \\ \hline \hline \end{tabular}  
* Experiments have shown that placing the ODConv-C2f module in the Neck part yields better results.  
\end{table}
Table 8: ODConv-C2f optimization position comparison.  
The introduction of ODConv on top of YOLOv8 reduced the computational complexity by 0.3 GFLOPs while Precision improved by 0.9% and Recall improved by 0.7%, which indicated that the introduction of ODConv reduced the redundant computation of the model, but the average accuracy of the recognition decreased in the case of high localization accuracy requirements. After introducing the GAM based on YOLOv8, the computational complexity increased by 1.3 GFLOPs, but it was still within the allowable range. Precision increased by 3.2%, Recall increased by 1.5%, mAP@50 increased by 0.8%, and mAP@50:90 increased by 0.7%. After both mechanisms were introduced, GFLOPs reduced compared with the introduction of the GAM alone. Compared with the basic model, the computational complexity increased by 1.0 GFLOPs, Precision is basically equivalent to the GAM, and compared with the basic model, it increased by 3.0%; Recall increased by 3.2%, mAP@50 increased by 2.0%, and mAP@50:90 is slightly inferior to the GAM, and compared with the basic model, it increased by 0.5%.  
In summary, YOLOv8-GO improves the overall performance of the model in all aspects while balancing the performance of individual aspects to achieve the overall optimization of the model with an acceptable increase in computational complexity.