# Intelligent Control System to Irrigate Orchids Based on Visual Recognition and 3D Positioning
## 4 Conclusions  
This work successfully develops an automated orchid irrigation system, which proves an accurate watering process on the stem-root junction of seedling, to reduce the possibility of diseases of Phalaenopsis plants. This research employs YOLOv3 for the object detection, to detect and to frame the centers of orchid seedlings. Then, in accordance with the derived depth map from the SGBM algorithm, the prediction frame of YOLOv3 is used to compute the actual 3D coordinates of the stem-root junction to be watered. Finally, the three-axis motion platform is controlled to move the spraying nozzle to accurately water the orchid seedlings. A list of vital conclusions are made as follows.  
Under the total number of 950 training photos, the accuracy of the validation and test sets for the learning model of YOLOv3 are 92.63% and 86.23%, respectively, and hence the feasibility of object detection in this work is guaranteed.  
The experiments show that the error components of the coordinates (X, Y, Z) for measuring the seedling center are (0.18, 0.2, 0.32) cm maximally, and (0.1, 0.12, 0.18) cm minimally.  
According to the experiments, the slice from 4 to 11 s of the duty time for opening the spraying valve is linearly related to the watering amount for irrigation, which proves to reliably control the accurate watering amount to irrigate the orchid seedlings.  
In the validation experiments for the automated irrigation, the successful watering rates of the single-pot and multiple-pot orchid seedlings are 82% and 83.3%, respectively. Moreover, the perfect watering rates of the single-pot and multiple-pot orchid seedlings are 70.7% and 62.9%, respectively.  
In the future, the prediction accuracy of the model for the object detection can be raised by enlarging the training sets. As well, the mis-locating of leaf vectors could be solved by alternative detection algorithms, in order to further improve the successful rate of watering. In practice, the system can be also employed to similar applications that require automatic object detection, target positioning, motor control, etc.  
Conceptualization, T.-W.C. and R.C.; methodology, T.-W.C.; software, T.-W.C.; validation, T.-W.C.; formal analysis, T.-W.C. and W.-C.W.; investigation, T.-W.C.; writing--original draft preparation, T.-W.C. and W.-C.W.; writing--review and editing, W.-C.W. and R.C.; visualization, T.-W.C. and W.-C.W.; supervision, R.C. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by Ministry of Sciences and Technology, Taiwan, under the grant numbers MOST 108-2221-E-007-097 and MOST 109-2221-E-007-041.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Indexes for Multiple-Seedling** & **Results** \\ \hline \# of watering to stem-root junction & 22 \\ \# of watering to stem-leaf junction & 13 \\ \# of improper watering position & 7 \\ \# of non-operated watering & 18 \\ Operated watering (\%) & 70 \\ Successful watering (\%) & 83.3 \\ Perfect watering (\%) & 62.9 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Multiple-seedling experimental results.