## 4 Conclusions  
As a pre-processing step for plant part classification, a method of separating stem and leaf regions from the background using the NDVI based on the difference between a 735 nm band image and a 660 nm band image was applied. In addition, the color of the region of interest separated from the background was classified using k-means clustering. Through this, it was possible to segment the image of the region of interest. To find local features in an image in which the interested region was segmented using k-means  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Parts**} & & \multicolumn{5}{c}{**True Condition**} \\  & & **Fruit** & **Node** & **Leaf** & **Sucker** \\ \hline \multirow{3}{*}{Predicted condition} & Fruit & 398 & 0 & 2 & 0 \\  & Node & 0 & 351 & 0 & 49 \\ \cline{1-1}  & Leaf & 9 & 0 & 362 & 0 \\ \cline{1-1}  & Sucker & 0 & 51 & 0 & 349 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Result of the CNN algorithm.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Performance** & **Fruit (\%)** & **Node (\%)** & **Leaf (\%)** & **Sucker (\%)** \\ \hline BP Algorithm & 94.44 & 84.73 & 69.97 & 84.34 \\ CNN & 99.5 & 87.75 & 90.50 & 87.25 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Classification performance comparison between the BP algorithm and CNN.  
clustering, morphological skeletonization was performed for the image to find the skeleton of the region of interest. As a result of extracting local features using SIFT and SURF, it was confirmed that local feature extraction using SURF could lead to more local features. Therefore, the SURF method was selected to detect local features of fruits, stems, leaves, and suckers in this study.  
To classify SURF local features of fruits, stems, leaves, and suckers, local features were classified using SVM and BP algorithms with the radial basis function kernel. Their performances were then compared. Results confirmed that the accuracy of the BP algorithm was 95.96%, which was higher than that of the SVM at 63.75%.  
Fruits, stems, leaves, and suckers were classified using the CNN (Convolutional Neural Network), a kind of deep neural network. Its performance was then compared to that of the BP algorithm. As a result of comparing the performances for classifying each part of sweet pepper, it was confirmed that the performance of the CNN was superior to that of the BP algorithm. If further research, such as stereo vision, to calculate distance and the mechanism of a robot-arm to perform agricultural works is carried out, the proposed method in this study could be applied to an image processing algorithm for the multipurpose cultivation robot system.  
Conceptualization, B.L., D.K., Y.C., D.-C.K. and D.-H.L.; Methodology, B.L. and D.-H.L.; Software, B.L.; Validation, B.L., D.K. and Y.C.; Formal analysis, B.L.; Investigation, B.L., D.K. and Y.C.; Resources, B.L., D.K. and Y.C.; Data curation, Y.C.; Writing--original draft preparation, B.L., D.K. and Y.C.; Writing--review and editing, B.L., Y.C. and D.-H.L.; Visualization, Y.C.; Supervision, Y.C.; Project administration, Y.C.; Funding acquisition, Y.C. All authors contributed almost equally to all aspects of this research. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through the Agriculture, Food and Rural Affairs Convergence Technologies Program for Educating Creative Global Leader Program funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (320018-2). It was also supported by a grant (NRF-2021R1G1A1012778) of the National Research Foundation of Korea (NRF) funded by the Korean government (MSIT, Ministry of Science and ICT). This research was supported by the \"Research Base Construction Fund Support Program\" funded by Jeonbuk National University in 2020.  
Not applicable.  
The authors have no conflict of interest to disclose relevant to this study.