## 6 Conclusions  
In summary, this paper proposes a new niche adaptive elite evolutionary algorithm (NAEEA) to solve the network clustering problem in intelligent unmanned AUSCNs, which can effectively reduce the network energy consumption of AUSCNs. The new niche strategy can greatly expand the search space of the population and improve the convergence speed of the algorithm through group evolutions of the population. In addition, the new adaptive strategy and elite strategy can effectively prevent the algorithm from falling into local optimality and causing evolutionary stagnation by adaptively adjusting the algorithm parameters and preventing the loss of local optimal solutions. By combining actual environmental parameters, the NAEEA was compared with the EA, SA and SFLA in simulation experiments in different scale scenarios. The results showed that the NAEEA performed very well and significantly reduced the energy consumption of the AUSCN. Therefore, the NAEEA is able to find high-quality clustering solutions for AUSCNs while maintaining a high convergence speed, greatly reducing the network energy consumption, which also provides a beneficial help for the application of UAVs in the field of agricultural monitoring.  
This paper only considers the goal of energy consumption when studying the energy transmission optimization of AUSCNs. There are certain limitations in fully verifying that the NAEEA can reduce energy consumption. Therefore, in future research, we will deeply explore the performance of the proposed NAEEA in specific agricultural monitoring scenarios, including complex scenarios such as AUSCNs that are not limited to large scales, as well as 3 DAUSCNs. In addition, in terms of performance verification, we will also  
\begin{table}
\begin{tabular}{c c c c} \hline  & **EA** & **SA** & **SFLA** \\ \hline Figure 13a & 8.89\% & 6.46\% & 15.75\% \\ Figure 13b & 16.47\% & 17.44\% & 22.83\% \\ Figure 13c & 20.97\% & 19.67\% & 25.76\% \\ Figure 13d & 21.43\% & 25.00\% & 25.00\% \\ \hline \end{tabular}
\end{table}
Table 13: Compared with the EA, SA and SFLA, the NAEEA reduces the percentage of network energy consumption in Figure 13.  
consider factors such as network energy consumption, drone life and data security. To this end, we plan to conduct in-depth research on the trust-aware mechanism introduced in the clustering routing protocol to ensure the stable operation of the network and the reliable transmission of data. At the same time, we will further improve the stability of the algorithm, which will help to bring more reliable quality services to the application of drones in the field of agricultural monitoring, thereby promoting the advancement and development of intelligent unmanned technology in practical applications.  
Q.Z.: conceptualization, methodology, software, writing--original draft, visualization, validation. J.Z.: data curation, software, writing--original draft, visualization, validation. Y.Z.: supervision, writing--review and editing, funding acquisition, validation. All authors have read and agreed to the published version of the manuscript.  
This article is supported by the \"National Key Research and Development Program for Group Intelligent Autonomous Operation Smart Farming\" project, grant number 2022 ZD0115800, the National Key Research and Development Program of the Corps \"Key Technology Research and Application for High Penetration New Energy Grid Dispatch\", grant number 2023 AB010, and the Eighth Division Shhiezi City Science and Technology Plan Project--Research and Application of Intelligent Inspection Platform for Substation Equipment, grant number: 2023 CY06.  
Not applicable.