# Sugarcane Stem Node Recognition in Field by Deep Learning Combining Data Expansion
## 4 Result and Discussion
### The Recognition Effect of Different Data Expansion Methods  
As mentioned above, four data expansion methods were used in this article: Rotation, mirror flip, random colour processing and brightness enhancement. In order to verify the effect of these four data expansion methods on the performance of the algorithm, the variable control method was applied to delete the image data corresponding to each data expansion method in the training set, then the testing set was used to test the trained algorithm model and the YOLO v4 algorithm detection results were obtained. The results are shown in Table 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Times** & **Algorithm** & **Precision (\%)** & **Recall (\%)** & **AP (\%)** & **F1** \\ \hline \multirow{4}{*}{\begin{tabular}{c} Oblique \\ strong light \\ \end{tabular} } & Faster R-CNN & 55.12 & 81.71 & 70.83 & 0.66 \\  & SSD300 & 94.35 & 84.44 & 90.62 & 0.89 \\  & RetinaNet & 90.46 & 84.82 & 88.33 & 0.88 \\  & YOLO v3 & 94.96 & 87.94 & 93.36 & 0.91 \\  & YOLO v4 & 97.32 & 89.34 & 95.70 & 0.93 \\ \hline \multirow{4}{*}{\begin{tabular}{c} Direct strong \\ light \\ \end{tabular} } & Faster R-CNN & 59.38 & 87.14 & 81.24 & 0.71 \\  & SSD300 & 95.99 & 81.25 & 93.90 & 0.88 \\  & RetinaNet & 93.04 & 86.32 & 91.86 & 0.90 \\  & YOLO v3 & 94.67 & 86.74 & 93.15 & 0.91 \\  & YOLO v4 & 96.93 & 84.46 & 95.41 & 0.90 \\ \hline \multirow{4}{*}{\
\begin{tabular}{c} Oblique \\ weak light \\ \end{tabular} } & Faster R-CNN & 58.56 & 86.41 & 79.88 & 0.70 \\  & SSD300 & 95.98 & 78.10 & 92.76 & 0.86 \\  & RetinaNet & 92.44 & 85.61 & 91.12 & 0.89 \\  & YOLO v3 & 94.51 & 85.91 & 92.94 & 0.90 \\  & YOLO v4 & 97.74 & 84.64 & 95.04 & 0.91 \\ \hline \multirow{4}{*}{
\begin{tabular}{c} Average \\ \end{tabular} } & Faster R-CNN & 57.69 & 85.09 & 77.32 & 0.69 \\  & SSD300 & 95.44 & 81.26 & 92.43 & 0.877 \\  & RetinaNet & 91.98 & 85.58 & 90.44 & 0.89 \\  & YOLO v3 & 95.05 & 86.86 & 93.15 & 0.907 \\  & YOLO v4 & 97.33 & 86.15 & 95.38 & 0.913 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Statistical detection results of five algorithms on stem nodes of sugarcane at different times. From Table 7, the method of rotation was very helpful to improve the detection accuracy. By deleting the images produced by the rotation method, the AP of the YOLO v4 detection model was reduced by 16.69%, and the F1 score was reduced by 0.11. The method of flipping had the least impact on improving the detection accuracy. After removing the mirror flip images, the performance of the training model was only slightly lower than that of the complete dataset. The AP of the YOLO v4 detection model was reduced by 5.73%, and the F1 score was reduced by 0.03. Compared to the dataset without the images processed by a random colour, the model trained with the complete dataset had higher detection accuracy. After the removal of the random colour processing from the training set, the AP of the YOLO v4 detection model decreased by 9.27% and the F1 score decreased by 0.82. This indicated that random colour processing was very beneficial to improve the robustness of the model. The recognition model without images processed by brightness enhancement was worse than that of the model trained with the complete dataset. The AP of the YOLO v4 detection model was reduced by 9.27% and the F1 score was reduced by 0.04. Bright \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Data Expansion Methods** & **Precision (\%)** & **Recall (\%)** & **AP (\%)** & **F1** \\ \hline Dataset after expansion & 95.12 & 84.90 & 95.17 & 0.9 \\ Remove rotation & 91.41 & 69.80 & 78.48 & 0.79 \\ Remove mirror flip & 91.65 & 83.51 & 89.44 & 0.87 \\ Remove random colour processing & 90.78 & 74.33 & 85.90 & 0.82 \\ Remove brightness enhancement & 90.65 & 81.59 & 85.93 & 0.86 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: The recognition effect of different data expansion methods. Figure 9: Image detection results of five algorithms on sugarcane stem nodes in different time periods.