# Sugarcane Stem Node Recognition in Field by Deep Learning Combining Data Expansion
## 4 Result and Discussion  
### The Recognition Effect of Different Algorithms  
Four object detection algorithms, Faster R-CNN, SSD300 [27], RetinaNet [28] and YOLO v3, were selected and compared with the YOLO v4 algorithm to verify the recognition effect of the algorithm. The backbones of these four algorithms were ResNet50, VGG16, ResNet50 and Darknet53.  
The training set was applied to the above five algorithms, and the test set was employed to evaluate the performance of the different detection algorithms. The P-R curves of the different algorithms are shown in Figure 7, which is a two-dimensional curve with precision and recall as the vertical and horizontal coordinates. When the P-R curve of one algorithm was surrounded by another algorithm, the latter performed better than the former. In Figure 7, except for the YOLO v3 algorithm and YOLO V4 algorithm, the curves of the other three algorithms all approached the coordinate point (1,0) at the end. Combined with Formulas (10) and (11), it was clear that the false detection rates (FP) of the Faster-RCNN, SSD300 and RetinaNet algorithms on the test set were all fairly high.  
The F1 scores varying with the confidence threshold score are shown in Figure 8. The F1 scores are a harmonic average between the results of precision and recall, and range from 0 to 1, where 1 represents the best output of the model and 0 represents the worst output of the model. When the threshold value was set as 0.5 in this paper, the F1 value of YOLO v3 and YOLO v4 achieved the highest scores, indicating that the optimal output of the algorithm can be obtained when the two methods simultaneously meet the requirements of high precision and high recall rates.  
Table 5 was the statistical results of different algorithms. The results showed that the AP of these five object detection algorithms were 78.87%, 88.98%, 90.88%, 92.69% and 95.17%; the detection speeds were 11 f/s, 62.5 f/s, 40.18 f/s, 72 f/s and 69 f/s, respectively.  
In terms of detection speed, although YOLO v3 was slightly faster than YOLO v4, both of them far exceeded the real-time detection requirement of 30 f/s. As for the AP results, YOLO v4's AP is 16.3%, 6.19%, 4.29% and 2.48% higher than the other four algorithms, respectively. Through the analysis of the test results, it can be seen that the detection accuracy of YOLO v4 for sugarcane stem nodes was higher than the other four algorithms, and the detection accuracy was very close to the fastest algorithm. It was clearly more in line with the requirements of sugarcane stem node recognition in the complex natural environment.  
Figure 8: The F1 score curves of different algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Algorithm** & **Precision (\%)** & **Recall (\%)** & **AP (\%)** &
\begin{tabular}{c} **Detection** \\ **Speed (f/s)** \\ \end{tabular} & **F1** \\ \hline Faster R-CNN & 85.46 & 85.60 & 78.87 & 11 & 0.69 \\ SSD300 & 93.17 & 81.20 & 88.98 & 62.5 & 0.87 \\ RetinaNet & 92.34 & 85.33 & 90.88 & 40.18 & 0.89 \\ YOLO v3 & 94.40 & 85.31 & 92.69 & 72 & 0.90 \\ YOLO v4 & 95.12 & 84.90 & 95.17 & 69 & 0.90 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Statistical results of different algorithms.  
Figure 7: The P-R curves of different algorithms.