# A Survey: Future Smart Cities Based on Advance Control of Unmanned Aerial Vehicles (UAVs)
## 7 Future of Smart Cities  
This section emphasizes the importance of exploring innovative approaches for UAV energy harvesting and novel materials for batteries to achieve extended mission capabilities. It highlights the need for researchers to investigate lightweight and efficient batteries that can enhance flight time for UAVs [128, 129]. Additionally, it emphasizes the importance of addressing efficient power control mechanisms and energy consumption in future studies. Overall, the paragraph stresses the significance of advancing energy harvesting, battery technology, power control, and energy management for UAVs. * The importance of power-efficient algorithms for real-time processing of UAV data and highlights the need for algorithm development in various areas related to UAV operations, including search and rescue, swarm coordination, optimization, collision avoidance, and trajectory planning [130]. * UAV technology continues to progress in areas such as standardization, privacy regulations, image processing algorithms, sensor affordability, extended flight time, and larger payload capacity, and there is a growing need to integrate UAVs into various applications. Specifically, the integration of UAVs in field crop phenotyping is highlighted as a significant opportunity. UAVs offer advantages in capturing high-resolution images, collecting data efficiently, enabling precision agriculture practices, and automating data collection processes. However, further development in the mentioned areas is crucial to fully leverage the potential of UAVs in diverse applications, including field crop phenotyping. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Strategy** & **Merits** & **Demerits** \\ \hline \multirow{6}{*}{} & \(\bullet\) & Adaption and estimation of \\  & varying system parameters & \\ \cline{1-1}  & \(\bullet\) & No need for precise model \\ \cline{1-1}  & \(\bullet\) & Improved performance \\ \cline{1-1}  & \(\bullet\) & Provides adaption \\ \cline{1-1}  & \(\bullet\) & Increases quality factor when \\ \cline{1-1}  & \(\bullet\) & integrated with other control techniques \\ \cline{1-1}  & \(\bullet\) & Flexible control strategy \\ \cline{1-1}  & \(\bullet\) & Coverage of wide-ranging \\ \cline{1-1}  & \(\bullet\) & Operating conditions \\ \cline{1-1}  & \(\bullet\) & Human-like decisions \\ \cline{1-1}  & \(\bullet\) & unlike Booleans \\ \cline{1-1}  & \(\bullet\) & Can reject unknown disturbances \\ \cline{1-1} Hybrid & \(\bullet\) & balances and provides adaption \\ \cline{1-1} Control & \(\bullet\) & for uncertain \\ (adaptive & \(\bullet\) & model parameters \\ \cline{1-1} control, & \(\bullet\) & Learning ability \\ \cline{1-1} intelligent & \(\bullet\) & Adaption and estimation of \\ \cline{1-1} control) & \(\bullet\) & varying parameters \\ \cline{1-1}  & \(\bullet\) & Enhanced robustness \\ \cline{1-1}  & \(\bullet\) & Improved performance \\ \cline{1-1}  & \(\bullet\) & Faster convergence \\ \cline{1-1}  & \(\bullet\) & Improved disturbance rejection \\ \cline{1-1}  & \(\bullet\) & Controller can be trained to \\ \cline{1-1}  & \(\bullet\) & provide tolerance against cyber threats, injected faulty \\ \cline{1-1}  & \(\bullet\) & data, wireless communication \\ \cline{1-1}  & \(\bullet\) & attacks, and sensor \\ \cline{1-1}  & \(\bullet\) & spoofing issues \\ \cline{1-1}  & \(\bullet\) & Increases quality factor when \\ \cline{1-1}  & \(\bullet\) & integrated with other \\ \cline{1-1}  & \(\bullet\) & control techniques \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Merits and Demerits of Nonlinear Hybrid Control Strategies. * The critical issues faced by imaging processing techniques for UAVs, include varying image orientation, higher overlaps, variable scales, and varying altitudes. These challenges pose obstacles to effectively processing and analyzing UAV-captured images.