# Method for Recognizing Disordered Sugarcane Stacking Based on Improved YOLOv8n
## 4 Analysis of Experimental Results
### Public Dataset Experiment Analysis  
To further validate the research methods, two public datasets were obtained from CSDN for experimentation. The first dataset was a single-category apple detection dataset containing 2500 images; the second dataset included 12 types of crop leaf diseases, comprising 5483 images. These datasets were divided into training, testing, and validation sets in a 7:2:1 ratio, resulting in 1750, 500, and 250 images for the apple detection dataset, and 3839, 1096, and 548 images for the crop leaf disease dataset, respectively. During the model training process, both the YOLOv8n model and the improved VIEM-YOLOv8n model were utilized for comparison.  
Experimental results on the apple detection dataset, as presented in Table 3, show that the YOLOv8n and VIEM-YOLOv8n models exhibit similar precision. However, the VIEM-YOLOv8n model achieves a 0.8% increase in recall and a 2.8% improvement in mAP0.5, demonstrating enhanced accuracy in correctly identifying targets. The F1 scores are comparable, indicating that both models achieve a similar balance between precision and recall, with a slight advantage for VIEM-YOLOv8n.  
For the crop leaf disease dataset, as shown in Table 4, the VIEM-YOLOv8n model outperforms the YOLOv8n across all performance metrics. Notably, it achieves a precision of 81.3%, which is 6.4 percentage points higher than the 74.9% recorded by YOLOv8n, reflecting greater accuracy in predicting positive classes. In terms of recall, VIEM-YOLOv8n also leads with 69.6%, an increase of 3.8 percentage points over YOLOv8n, indicating more effective identification of all positive samples. Under the mAP criterion with an IoU threshold of 0.5, VIEM-YOLOv8n achieves 70.1%, surpassing YOLOv8n's 68.1% by 2 percentage points. The F1 score, at 75.0%, is also 4.9 percentage points higher, further confirming a better balance between precision and recall achieved by VIEM-YOLOv8n.  
Combining the experimental outcomes from both the apple detection and crop leaf disease datasets, it is evident that the VIEM-YOLOv8n model significantly surpasses the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Detection** & **Precision** & **Recall** & **mAP0.5** & **mAP0.50.9** & **F1 Score** & **Parameter/10\({}^{6}\)** \\
**Network** & **\%** & **\%** & **\%** & **\%** & **\%** & \\ \hline YOLOv8n & 74.9 & 65.8 & 68.1 & 55.6 & 70.1 & 3.0 \\ VIEM-YOLOv8n & 81.3 & 69.6 & 70.1 & 54.3 & 75.0 & 3.9 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Experimental Results on the Crop Leaf Disease Dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Detection** & **Precision** & **Recall** & **mAP0.5** & **mAP0.50.9** & **F1 Score** & **Parameter/10\({}^{6}\)** \\
**Network** & **\%** & **\%** & **\%** & **\%** & **\%** & \\ \hline YOLOv8n & 88.3 & 82.6 & 89.2 & 75.6 & 85.4 & 3.0 \\ VIEM-YOLOv8n & 88.3 & 83.4 & 92.0 & 74.4 & 85.8 & 3.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental Results on the Apple Detection Dataset.  
Figure 9: Variations in DFL-Loss Curve.  
YOLOv8n in target detection performance, validating the effectiveness of the proposed VIEM-YOLOv8n model in enhancing detection precision.