## 6 Conclusions  
To detect _Camellia oleifera_ fruits in complex environments and solve the problem of the accurate positioning of _Camellia oleifera_ fruit picking robots, this research proposes a method based on the fusion of deep learning/visual perception algorithms and classic image processing, which can adaptively and actively locate the _Camellia oleifera_ fruit recognition and picking points.  
After the testing model and method performance, the following conclusions are obtained:  
1. The _precision_ of YOLOv7 and YOLOv5s for model performance is basically similar, and the _mean average precision_ of YOLOv7 is higher than that of YOLOv5s.
2. On the same dataset, YOLOv7 takes less time to detect images than YOLOv5s does. Compared with the YOLOv5s algorithm, the YOLOv7 algorithm can detect some occluded _Camellia oleifera_ fruits, which significantly improves _Camellia oleifera_ fruit detection.
3. The pixel value of the position deviation between the image extracted centroid point and the center of the YOLO recognition frame obtained by image processing is less than 10. Thus, the center of the YOLO recognition frame is approximately consistent with the image-extracted centroid point.  
Although the YOLOv7 network is applied to _Camellia oleifera_ fruit detection and has achieved good results, the detection accuracy still needs to be improved. In future research, we will further optimize the network model structure and continue to improve the identification and detection performance and missing detection performance of the _Camellia oleifera_ fruit model. In addition, when designing the _Camellia oleifera_ fruit picking robot, it is necessary to consider the problem of the pixel fault tolerance value of the end mechanism to compensate for the pixel error. As the _Camellia oleifera_ fruit recognition and detection has to be realized during picking, the influence of camera motion on the detection  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**NO**} & \multicolumn{2}{c}{**The Center of the YOLO**} & \multirow{2}{*}{**Centroid**} & \multirow{2}{*}{**Deviation (Pixel)**} \\  & & & \\
1 & (199,116) & (205,116) & 6 \\
2 & (227,114) & (225,110) & 4.5 \\
3 & (199,81) & (194,80) & 5.1 \\
4 & (203,92) & (206,92) & 3 \\
5 & (83,53) & (84,54) & 1.4 \\
6 & (68,40) & (66,40) & 2 \\
7 & (222,100) & (221,99) & 1.4 \\
8 & (132,81) & (132,80) & 1 \\
9 & (71,34) & (73,34) & 2 \\
10 & (209,107) & (210,105) & 2.2 \\ Average value (pixel) & & & 2.86 \\ Maximum (pixel) & & & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Positioning deviation.  
accuracy needs to be studied subsequently. In addition, the possibility of using multiple cameras to better position the objects also needs to be studied.  
Conceptualization and methodology, X.Z. and Y.Z. ([PERSON]); validation, Y.Z. ([PERSON]), X.Z., Y.T., M.W. and H.K.; data curation, Y.Z. ([PERSON]); writing--original draft preparation, Y.Z. ([PERSON]), Y.T. and X.Z.; writing--review and editing, X.Z., Y.T. and M.W.; visualization, Y.Z. ([PERSON]), W.T., F.M. and Y.Z. ([PERSON]); supervision, X.Z. and Y.T.; funding acquisition, X.Z. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the Foshan Science and Technology Innovation Project (No. 2120001008424) and Guangdong Dongguan Smart Aquaculture and Unmanned Processing Equipment Technology Innovation Platform Project (No. 20211800400092).  
Not applicable.  
We sincerely acknowledge the project funding support. We are also grateful for the efforts of all our teachers and friends. I would like to thank teachers [PERSON], [PERSON], [PERSON] and [PERSON] for their guidance, [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and other students for their help in my research.  
The authors declare that they have no conflict of interest.