# Empirical Trials on Unmanned Agriculture in Open-Field Farming: Ridge Forming
## 4 Conclusions  
While data technology has revolutionized unmanned agriculture in protected systems, field agriculture faces challenges posed by external factors, necessitating the consideration of replacing manual labor in every operational step. This study focused on analyzing the replaceability of a fundamental step, ridge forming, by comparing operation data and expenses between conventional and autonomous driving robots and evaluating the linearity of autonomous driving.  
In terms of operation data, ridge forming was executed with a maintained PTO revolution speed, revealing less torque damage when using the developed robot compared to the conventional method. The conventional method exhibited higher torque due to deeper-than-targeted soil depth during operation, highlighting limitations in precision when performed manually. The linearity of autonomous driving demonstrated reasonable performance, with a distance error of 0.267 m relative to the baseline. Further enhancements in autonomous driving performance could be achieved by addressing the discussed external factors. Moreover, using the developed robot for ridge forming resulted in a significant 50.81% reduction in expenses, amounting to 896,780 KRW/ha in field agriculture.  
Based on the findings of reduced costs, improved operational precision, and demonstrated linearity in autonomous driving, the replaceability of ridge forming in field agriculture has been substantiated. Future endeavors should focus on advancing unmanned agriculture across all operational facets.  
Conceptualization, S.K., J.H. and S.W.; methodology, H.P. and Y.K.; formal analysis, S.K. and J.S.; investigation, S.K., Y.K., H.P. and Y.H. ([PERSON]); writing--original draft preparation, S.K.; writing--review and editing, S.K.; supervision, Y.H. ([PERSON]); project administration, Y.H. ([PERSON]); funding acquisition, Y.H. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Conventional Method** & **Developed Robot** \\ \hline Fixed cost (A) & 7413.33 & 7413.33 \\ \hline Depreciation & 1583.33 & 1583.33 \\ \hline Repair & 1200.00 & 1200.00 \\ \hline Interest & 4410.00 & 4410.00 \\ \hline Tax & - & - \\ \hline Insurance & 20.00 & 20.00 \\ \hline Housing & 200.00 & 200.00 \\ \hline Variable cost (B) & 10,228.75 & 8675.00 \\ \hline Annual operating & 100 & 100 \\ time, h & 13.51 & - \\ \hline Lubrication & 2.03 & - \\ \hline Labor cost & 18.75 & 18.75 \\ \hline Usage cost per hour & 68.00 & 68.00 \\ \hline Sum of A and B & 17,642.08 & 16,088.33 \\ \hline Workable burden area, ha & 17.94 & 17.94 \\ \hline Annual usage cost, thousand KRW/ha & 983.39 & 896.78 \\ \hline Assistant employ cost & 781.24 & - \\ \hline Total, thousand KRW/ha & 1764.63 & 896.78 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of cost analysis.  
**Funding:** This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry (IPET) through the Open Field Smart Agriculture Technology Short-term Advancement Program, funded by the Ministry of Agriculture, Food and Rural Affairs (MAFRA) (322038031 SB010).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflicts of interest.