## 5 Conclusions  
In this paper, a new binocular location method based on an improved YOLOv4 was proposed to solve the problem of the difficulty of sugarcane nodes spatial location due to the complex form characteristics in the field environment and the high demand for computational capability. 1. The deep learning algorithm of YOLOv4 was improved by the Channel Pruning Technology in network slimming, so as to ensure the high recognition accuracy of the deep learning algorithm and to facilitate transplantation to embedded chips. The experimental results showed that the _Params_, _FLOPs_ and model size were all reduced by about 89.1%, while the average precision (_AP_) decreased by only 0.1% (from 94.5% to 94.4%). To be specific, the _Params_ decreased from 63,937,686 to 6,973,211, the _FLOPs_ decreased from 29.88 G to 3.26 G, and the model size decreased from 244 M to 26.7 M, which greatly reduced the computational demand. 2. Compared with other deep learning algorithms, the improved YOLOv4 algorithm also has great advantages. Specifically, the improved algorithm was 1.3% and 0.3% higher than SSD and YOLOv3 in average precision (_AP_). In terms of parameters, _FLOPs_ and model size, the improved YOLOv4 algorithm was only about 1/3 of SSD \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Number** & **Identification Time (ms)** & **Other Time (ms)** & **Total Time (ms)** \\ \hline Before move—1 & 32 & 1533 & 1565 \\ Before move—2 & 33 & 1537 & 1570 \\ Before move—3 & 30 & 1539 & 1569 \\ After move—1 & 32 & 1538 & 1570 \\ After move—2 & 31 & 1540 & 1571 \\ After move—3 & 34 & 1535 & 1569 \\ Average time & 32 & 1537 & 1569 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The execution time spent in the stem node spatial location. and 1/10 of YOLOv3. The above data sufficiently demonstrated the superiority of the improved YOLOv4 algorithm model. 3. The SIFT algorithm, with its strong anti-light interference ability, was used to extract feature points from complex form sugarcane pictures. Furthermore, the SIFT feature points were optimised by the RANSAC algorithm and epipolar constraint, which effectively reduced the mismatching problem caused by the similarity between stem nodes and sugarcane leaves. 4. The optimised matching point pairs were used to solve the homography transformation matrix, so as to obtain the three-dimensional spatial coordinates of the complex form stem nodes and verify their spatial location accuracy. The experimental results showed that the maximum error of the \(Z\) coordinate in the spatial location of complex form stem nodes was 2.86 mm, the minimum error was 1.13 mm, and the average error was 1.88 mm, which totally meet the demand of sugarcane harvesting robots in the next stage. Considering the requirements for extended application on extreme environments, further research investigating effects of the stem node recognition and location on the night time with supplementary light may be beneficial. The parameters of the supplementary lights, including the angle of supplementary lights, the number of lights, the change of illuminance and the interference of surrounding light source, are worth being discussed. Conceptualization, C.Z. ; methodology, C.Z. ; software, C.Z. ; validation, S.H., H.G., Y.L. and C.W. ; formal analysis, C.Z. and Y.L. ; investigation, C.Z. and S.H. ; resources, S.H., Y.L. and H.G. ; data curation, C.Z. ; writing--original draft preparation, C.Z. ; writing--review and editing, S.H., Y.L. and C.W. ; visualization, C.Z. ; supervision, S.H. and H.G. ; project administration, S.H. and H.G. ; funding acquisition, S.H., Y.L. and H.G. All authors have read and agreed to the published version of the manuscript. This study was supported by the National Nature Science Foundation of China (No. 51965004). Not applicable. The authors would like to thank [PERSON] for his contribution to this article, and thank [PERSON], [PERSON] and [PERSON] for their contributions to data set labeling.