# A Vision Servo System for Automated Harvest of Sweet Pepper in Korean Greenhouse Environment
## 4 Conclusions  
In this study, the shape of the manipulator for the automated robot system to harvest sweet peppers was cylindrical (consisting of a rotation axis, a horizontal axis, and a vertical axis), because it was efficient to carry out harvesting works in a narrow space. Based on the external dimension of a sweet pepper, the end-effector was designed not to damage the stem but to approach it from below, thus harvesting it without damage. It was also designed not to check the peduncle additionally but to use information on the fruit only, and its cutting portion was designed to be vertical, thus preventing the cutting of the stem. The study used three color CCD cameras, consisting of a stereo vision system and an eye-in-hand image system for the control of the end-effector's vision servo. In addition, using laser light sources, it was equipped with a light system that could add feature points to the sweet pepper. Using a camera installed at the end-effector, it could also detect a sweet pepper, and using the upper and bottom center of the ROI, it could extract the position and pose of the sweet pepper. Based on the information extracted, the three-step harvest algorithm was developed. In order to confirm the performance of the sweet pepper recognition in this study, 269 sweet pepper images were used to extract fruits. Of 269 sweet pepper images, 82.16% were recognized successfully. The result of the experiment with 60 sweet peppers presented the fact that its approach rate to peduncle was about 86.7%, and via four sessions of repetitive harvest experiment it achieved a maximal 70% harvest rate, and its average time of harvest was 51.1 s. According to a study by [PERSON] et al. [33], the average number of harvest days for red sweet pepper 'cupra' in South Korea was 81.9 days and the fruit yield per 10 are was 18,848 kg. In order to do that, when one farmer works eight hours a day, 143 red sweet peppers (200 g per fruit) should be harvested in one hour. In order to replace the manpower by using this study, where the harvesting success rate is 70% and the one-time working time is 51.1 s, and by assuming that the harvesting success of 70% on 205 paprika is obtained and assuming the \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{2}{c}{**0–3 mm**} & \multicolumn{3}{c}{**3–6 mm**} \\ \hline
**Try** & **Success** & **Rate** & **Try** & **Success** & **Rate** \\ \hline
2 & 0 & 0\% & 5 & 1 & 20\% \\ \hline \multicolumn{5}{c}{**6–9 mm**} & \multicolumn{3}{c}{**-9 mm**} \\ \hline
**Try** & **Success** & **Rate** & **Try** & **Success** & **Rate** \\ \hline
15 & 9 & 60\% & 8 & 6 & 75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Field experiment results of harvesting algorithm according to length of peduncle. system works eight hours a day like manpower, the three systems must perform. However, since the system does not require rest, unlike humans, it is assumed that if the system works 24 hours a day, it will get the same daily yield as the manpower. Compared with the study by [PERSON] et al. [19] in the Netherlands, their sweet pepper harvesting robot showed 85% sweet pepper recognition rate and 70% harvest success rate. The recognition rate was 2.84% lower than that of [PERSON] et al. who detected fruits of sweet pepper using the convolution neural network. There were no differences in the harvest success rate though environmental differences [19; 22] such as spacing distance of plant row and crop density in South Korea and the Netherlands. If the volume of the front portion of the end-effector of this study decreased, and if the design of the cutting portion of this study improved, and if the pneumatic cylinder of this study was placed at the back of the end-effector, then the rate of harvest would increase. In addition, if the motor RPM (Revolutions per minute) of the manipulator of this study increased, the time of harvesting would seemingly decrease. All five authors contributed almost equally to all aspects of this research. Conceptualization, B.L., D.K., B.M., J.H. and S.O.