# A Smart and Mechanized Agricultural Application: From Cultivation to Harvest
## 4 Results and Discussion
### Analysis and Evaluation (Scenario 1, Crop Cultivation)  
In this section, the performance of the proposed method at the planting stage of the crops is analyzed. Sowing, which is the first phase of smart and sustainable agriculture, has an important role. Here, the working mechanism and performance of each algorithm are presented in Table 4. The results of the proposed method at the planting stage are analyzed and also compared with other methods in the literature. Due to the nature of the problem type, the methods in the metaheuristic approach were used for comparison. The lower the error rate, the higher the performance of the relevant mechanism. In this study, the places where tomatoes should be planted were based on practical and analytical information (Table 3).  
The least amount of Mean Square Error (MSE) belongs to the SCSO based method. Here, data is presented in meters. According to the results, the method that could complete the placement task with the least error rate was our method based on the SCSO algorithm. It has 5.02 \(\times\) 10\({}^{-3}\) error, which is the minimum error rate between the algorithms used. The MSE values gained from various metaheuristics go down, while the iterations increase. The MSE convergence of each method is presented in Figure 4. The SCSO outperforms the other metaheuristic approaches, and its MSE value is 5.02 \(\times\) 10\({}^{-3}\). In other words, SCSO shows very good results when the MSE value is around 4.17 \(\times\) 10\({}^{0}\) in the first iteration and converges at 5.02 \(\times\) 10\({}^{-3}\) in the final iteration. Among all the metaheuristic approaches, MFO has the worst performance, starting at 6.78 \(\times\) 10\({}^{0}\) in the first iteration and converging at 2.56 \(\times\) 10\({}^{-2}\) in the final iteration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **MSE** & **Algorithm** & **MSE** \\ \hline SCSO & **0.005022 m** & MFO & 0.025599 m \\ GWO & 0.018713 m & PSO & 0.010692 m \\ \hline \hline \end{tabular} Note: The best values of algorithms are written in bold.  
\end{table}
Table 4: The overall performance of each algorithm in the cultivation phase.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{3}{*}{\begin{tabular}{c} **Growing** \\ **Method** \\ **Seeds (Day)** \\ \end{tabular} } & \begin{tabular}{c} **First** \\ **Flowering** \\ **Time (Day)** \\ \end{tabular} & \begin{tabular}{c} **Time from** \\ **Planting to** \\ **First Harvest** \\ **(Day)** \\ \end{tabular} & \begin{tabular}{c} **Starting** \\ **Harvest** \\ **(Day)** \\ \end{tabular} & \begin{tabular}{c} **End of the** \\ **Last Harvest** \\ **(Day)** \\ \end{tabular} & \begin{tabular}{c} **Ripening** \\ **Stages** \\ **(Day)** \\ \end{tabular} & \
\begin{tabular}{c} **Average Root** \\ **Medium** \\ **Temperature** \\ **(Centigrade)** \\ \end{tabular} \\ \hline Greenhouse & 2–7 & 30 & 65 & 81 & 210 &
\begin{tabular}{c} Breaker, pink, \\ and red \\ \end{tabular} & 20–35 \\ \hline \hline \end{tabular}
\end{table}
Table 3: A typical example of a tomato growth cycle [52; 53].