# YOLOv8-GO: A Lightweight Model for Prompt Detection of Foliar Maize Diseases
## 3 Results
### Experimental Analysis
#### 3.3.5 YOLOv8-GO Training and Evaluation  
After 300 epochs of training, YOLOv8-GO could process 275.1 frames per second (FPS) at a batch size of 1. The network had 211 layers and 5.23 M parameters. The generated.pt file was 10.3 Mb and the. ONNX file was 20.3 Mb. Compared with the basic model YOLOv8n, the performance in each category is shown in the following Table 10.  
Compared to YOLOv8n, YOLOv8n-GO shows improved detection performance across most categories, particularly in terms of Precision and Recall. This indicates that YOLOv8n-GO is more accurate and reliable in detecting corn leaf diseases. The only exception is in the AP metric for Gray Leaf Spot, where YOLOv8n-GO slightly underperforms YOLOv8n. This may be due to overfitting or instability in detecting this specific disease.  
Figure 16 shows the confusion matrix, which reflects the degree of confusion in each category. The darker the main diagonal color block, the more accurate the detection is. The darker the non-main diagonal color block, the easier it is to confuse the two items in the horizontal and vertical directions. The figure shows the PR curve, which is the mAP value obtained by integrating each curve. Combining the two figures, the PR curve of the large spot disease is far away from the upper right of the image due to the confusion between the large spot disease and the background.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multicolumn{3}{c}{**YOLOv8n**} & \multicolumn{3}{c}{**YOLOv8n-GO**} \\ \cline{2-7}  & **P** & **R** & **AP** & **P** & **R** & **AP** \\ \hline \multirow{2}{*}{Blight} & 0.764 & 0.683 & 0.757 & 0.803 & 0.746 & 0.822 \\  & 0.894 & 0.793 & 0.882 & 0.919 & 0.812 & 0.866 \\ \multirow{2}{*}{Common\_Rust} & 0.951 & 0.918 & 0.953 & 0.978 & 0.932 & 0.963 \\ \multirow{2}{*}{ALL} & 0.870 & 0.798 & 0.864 & 0.900 & 0.830 & 0.884 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison of indicators between the basic model and YOLOv8-GO on three diseases.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Precision** & **Recall** & **mAP@50** & **mAP@50:90** & **GFLOPs** \\ \hline YOLOv8n & 0.870 & 0.798 & 0.864 & 0.66 & 8.1 \\ YOLOv8n + ODConv & 0.879 & 0.805 & 0.868 & 0.643 & 7.8 \\ YOLOv8n + GAM & 0.902 & 0.813 & 0.872 & 0.667 & 9.4 \\ YOLOv8n-GO & 0.900 & 0.830 & 0.884 & 0.665 & 9.1 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Ablation experiment.  
Figure 17 illustrates the loss functions, Precision, Recall, mAP@50, and mAP@50:90 during the training and validation process. As the number of epochs increases, the various metrics continuously improve, indicating that the model is gradually converging, and its performance is enhancing.  
The following images display the training results. Figure 18 shows the outcome after training is completed, where sixteen images have been stitched together and recognized as a whole. As can be seen, each small image is accurately identified, demonstrating good performance. Figure 19 illustrates the recognition results for each category individually using the test set images. Figure 20 presents the recognition performance in a field environment, which also shows satisfactory results.  
Figure 16: (**a**) Confusion matrix; (**b**) Precision-Recall Curve.  
Figure 17: Changes in various indicators during training.  
Figure 19: The individual inference results on the validation set. (**a**) Rust; (**b**) Northern Leaf Blight; (**c**) Gray Leaf Spot.  
Figure 20: Field photo inference results. (**a**) Northern Leaf Blight; (**b**) Rust; (**c**) Gray Leaf Spot.