# Sugarcane Stem Node Recognition in Field by Deep Learning Combining Data Expansion
## 5 Conclusions  
The object detection algorithm for sugarcane stem node recognition based on YOLO v4 in the natural environment was introduced in this paper for the first time and achieved rapid and accurate recognition of sugarcane stem nodes during harvest in the natural environment, while the robustness and generalisation ability of the algorithm were improved by the dataset expansion method to simulate different illumination conditions. The images were collected in different lighting conditions of side light, forward light and back light. The impact of the data expansion and lighting conditions at different times of the day on the detection results of sugarcane stem nodes was discussed, and the superiority of YOLO v4, which performed best in the experiment, was verified by comparison with four different deep learning algorithms, namely Faster R-CNN, SSD300, RetinaNet and YOLO v3. The main conclusions are as follows.  
In the absence of a large amount of data, a data expansion method was adopted by simulating different illumination conditions and different shooting angles to train the detection model of sugarcane stem node recognition based on YOLO v4. The 1600 original images were expanded to 8000 images using data expansion to generate the training dataset and testing dataset. Through this method, the robustness of the model was effectively improved.  
The AP of the object detection algorithm based on YOLO v4 was the highest, at 95.17%. Although the detection speed of YOLO v3 (72 f/s) was slightly faster than that of YOLO v4 (69 f/s), both of them far exceeded the real-time detection requirement of 30 f/s.  
By comparison with the previous studies on sugarcane stem node recognition, the object detection algorithm based on YOLO v4 in a complex natural environment can detect sugarcane stem nodes wrapped by leaves more than 10 times faster than machine vision technology in a pre-processed single-colour background environment. Meanwhile, after fully considering the influence of light conditions, sugarcane leaves and biological characteristics in a complex environment, the detection speed of this paper's method was twice as fast as the previous method using YOLO v3 in a pre-processed single-colourbackground, and the accuracy was 4.74% higher too. The result indicated that the detection method based on YOLO v4 was feasible for fast and accurate detection of sugarcane stem nodes in the complex natural environment. This method provides effective visual technical support for the intelligent sugarcane harvester.  
Conceptualisation, W.C.; methodology, W.C.; software, W.C.; validation, S.H., C.J., Y.L. and X.Q.; formal analysis, W.C.; investigation, W.C. and S.H.; resources, S.H.; data curation, W.C.; writing--original draft preparation, W.C.; writing--review and editing, S.H., Y.L. and X.Q.; visualisation, W.C.; supervision, S.H., C.J. and X.Q.; project administration, S.H. and C.J.; funding acquisition, S.H. All authors have read and agreed to the published version of the manuscript.  
Not applicable. Not applicable.  
The author would like to thank the financial support provided by the National Nature Science Foundation of China, and [PERSON], [PERSON] and [PERSON] for writing advice. Most of all, [PERSON] wants to thank the first corresponding author [PERSON] for the constant support.  
The authors declare no conflict of interest.