## 4 Conclusions  
Due to the high sensitivity of agricultural robots to various operations such as fruit harvesting, spraying, etc., the proper training of its software is crucial. This study focused on a new segmentation algorithm based on a combination of video processing and majority voting rule to provide an identification system for harvester robots of plums in orchard conditions. Three hybrid ANNs were used to provide the voting process. These hybrids were ANN-BA, ANN-BBO, and ANN-FA. Video processing was based on the extracted color features of each pixel, which led to highly efficient predictions when compared with object analysis. Since there were different types of objects in the orchard, the use of different pixels related to different objects in each frame extracted from different videos captured in different light intensities throughout the day ensures that the identification system was properly trained to detect the plum fruits on the trees.  
Although it was not possible to use all of the extracted features of the identification system due to the time consumption, however, the greater the number of extracted features, the more the effective features were selected. Among the 33 extracted features, the hybrid ANN-HS selected four optimal features as inputs of classifiers that included the third channel of HSL, the second channel of LCH, the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **t** & **df** & **Sig.** & **Mean Accuracy** & \multicolumn{2}{c}{**95\% Confidence Interval**} \\ \cline{4-5}  & & & & **Lower** & **Upper** \\ \hline MV & 4420 499 &.000 & 97.20 & 97.16 & 97.24 \\ ANN-FA & 4739 499 &.000 & 96.91 & 96.87 & 96.95 \\ ANN-BBO & 4174 499 &.000 & 96.46 & 96.41 & 96.51 \\ ANN-BA & 3813 499 &.000 & 96.47 & 96.42 & 96.52 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Statistical differences between accuracy of majority voting (MV) and other method in 500 iterations.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Number of Samples** & **Correct Classification Rate (\%)** \\ \hline Proposed in this study & 7200 & 98.01 \\ [PERSON] et al. [32] & 210,752 & 96.80 \\ [PERSON] et al. [20] & 152 & 95.72 \\ [PERSON] et al. [19] & 182 & 97 \\ [PERSON] et al. [18] & 380 & 84.58 \\ \hline \hline \end{tabular}
\end{table}
Table 15: Comparison of different studies in the field of segmentation with the proposed method in this article.  
first channel of L*a*b*, and the first channel of the HIS color space. The single classification methods, namely hybrid ANN-BA, hybrid ANN-BBO, and hybrid ANN-FA had accuracies of 97.86%, 97.59%, and 97.77%, respectively. The majority voting method performed classification with an accuracy over 98% and was more accurate than the other classification methods.  
Conceptualization, S.S. and R.P.; Methodology, S.S., R.P. and D.K.; Validation, S.S., R.P., and D.K.; Formal analysis, S.S.; Investigation, S.S.; Writing--original draft preparation, S.S., T.P., and R.P.; Writing--review and editing, T.P.; Visualization, T.P.; Supervision, T.P, R.P., and D.K. All authors have read and agreed to the published version of the manuscript.  
This project was also supported by the European Union (EU) under Erasmus+ project entitled \"Fostering Internationalization in Agricultural Engineering in Iran and Russia\" [FARmER] with grant number 585596-EPP-1-2017-1-DE-EPPKA2-CBHE-JP.  
This paper was supported by the Research Center on Electronics, Optoelectronics, and Telecommunications (CEOT) project NINAP--Nucleo de Investigacao em Biotecnologia e Agricultura de Precisao FEDER ALG-01-0247-FEDER-037303.  
The authors declare no conflicts of interest.