# A Smart and Mechanized Agricultural Application: From Cultivation to Harvest
## 4 Results and Discussion
### Analysis and Evaluation (Scenario 2)
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Profit (\%)** & \begin{tabular}{c} **Time** \\ **Consumed by Robot** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **Traveled** \\ **Distance** \\ **Distance** \\ **(m)** \\ \end{tabular} & \begin{tabular}{c} **Energy** \\ **Consumed** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Simulation** \\ **Time (s)** \\ \end{tabular} & \begin{tabular}{c} **Path List** \\ **20, 16, 15, 6, 22, 18, 31, 3, 36, 9, 19, 11, 10, 35, 33, 23, 30, 1, 2, 34, 4, 27, 7, 17] \\ \hline MAP-ACO & 67.82 & 34.17 & 441.41 & 23.05 & 92.48 & \begin{tabular}{c} [33, 23, 35, 6, 32, 5, 14, 12, 13, 30, 10, 8, 4, 24, 34, 9, 1, 11, 28, 36, 3, 19, 21, 7, 18, 25, 17, 29, 26, 27, 31, 22, 2, 16, 15, 20] \\ \hline CS & 72.59 & 34.11 & 464.31 & 22.56 & 97.22 & \begin{tabular}{c} [14, 15, 36, 18, 34, 7, 4, 12, 11, 30, 10, 9, 2, 25, 23, 3, 8, 1, 20, 28, 35, 3, 19, 22, 23, 5, 21, 24, 26, 32, 16, 27, 31, 29, 6, 13, 17] \\ \hline FA & 70.13 & 34.91 & 457.13 & 22.59 & 93.19 & \begin{tabular}{c} [5, 4, 13, 27, 20, 30, 36, 28, 12, 35, 16, 31, 25, 17, 32, 19, 21, 6, 29, 34, 14, 8, 7, 23, 3, 2, 9, 15, 26, 10, 11, 18, 33, 22, 24, 1] \\ \hline GA & 68.99 & 37.66 & 521.12 & 26.51 & 96.03 & \
\begin{tabular}{c} [27, 15, 16, 11, 12, 13, 9, 8, 33, 30, 21, 14, 10, 1, 5, 32, 17, 26, 20, 31, 19, 22, 25, 29, 23, 18, 4, 28, 3, 36, 34, 24, 2, 6, 35, 7] \\ \hline GSO & 62.88 & 42.22 & 689.44 & 33.12 & 96.02 &
\begin{tabular}{c} [16, 15, 9, 4, 32, 10, 8, 24, 1, 36, 5, 13, 2, 25, 30, 19, 34, 7, 6, 35, 14, 22, 11, 23, 12, 26, 33, 17, 28, 20, 27, 31, 3, 21, 18, 29] \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Simulation results of each algorithm (best) based on phases 2 and 3. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Algorithm** & **Profit (\%)** & \begin{tabular}{c} **Time** \\ **Consumed by Robot** \\ **(m)** \\ \end{tabular}  & \begin{tabular}{c} **Traveled** \\ **Distance** \\ **(m)** \\ \end{tabular}  & \begin{tabular}{c} **Energy** \\ **Consumed** \\ **(\%)** \\ \end{tabular}  & \begin{tabular}{c} **Simulation** \\ **Time (s)** \\ \end{tabular}  & \
\begin{tabular}{c} **Path List** \\ **20, 16, 15, 6, 22, 18, 31, 3, 36, 9, 19, 11, 10, 35, 33, 23, 30, 1, 2, 34, 4, 27, 7, 17] \\ \hline MAP-ACO & 67.82 & 34.17 & 441.41 & 23.05 & 92.48 &
\begin{tabular}{c} [33, 23, 35, 6, 32, 5, 14, 12, 13, 30, 10, 8, 4, 24, 34, 9, 1, 11, 28, 36, 3, 19, 21, 7, 18, 25, 17, 29, 26, 27, 31, 22, 2, 16, 15, 20] \\ \hline CS & 72.59 & 34.11 In Tables 5 and 6, the performances of all algorithms are presented in detail. At this stage, a route with the least cost was chosen for the robot as the optimal path. When electing this, we aimed for the robot to consume the least power in the shortest time and to collect products at a high rate. In this regard, the performance values of the best path chosen by each algorithm are given. Based on the results, all three goals were achieved, with the highest rate on the route chosen by RACO. Finally, the results obtained from this subsection (scenario 2) are ordered comparatively and presented in Table 7. According to Table 7, the RACO method took the first order in the \"profit\" parameter. The second place is the CS-based method. In the \"Time Consumed by Robot\" parameter, the results of the RACO method found the best solution according to the results from the first phase of our study. Other ranks have been determined by the numbers written in relevant columns of this table. In the \"Traveled Distance\" parameter, the RACO-based method found the best result. The minimum amount in this parameter is the best answer. The worst performance was observed to be the GSO method. The amount of energy to be used by the robots, whose task is defined thanks to the operation of each method, is the fourth evaluation parameter. In this analysis, the RACO method performed better. The last parameter is the \"Simulation Time\", and it is the running time of the simulation. This indicates how efficiently another resource was used.