## 8 Conclusions  
This research investigates the three essential aspects of UAV technology that present the most technical challenges: energy sources, computational vision utilizing NNs, and applications. The classification of UAVs can be a complex endeavor due to various factors such as dimensions, weight, power supply, and intended use. It is crucial to take a systematic approach when determining the appropriate power source for a specific application. The amalgamation of computer vision, artificial intelligence, and neural networks are crucial advancements in the advancement of smart processes, therefore rendering unmanned aerial vehicles (UAVs) a valuable resource in military as well as civilian domains.  
Our work examined thoroughly different power supply technologies for drones and machine vision applications. We have gained valuable insights into the technical challenges by studying energy sources, computational vision with neural networks, and their practical uses in UAVs. Classifying UAVs is complex due to various factors like size, weight, power requirements, and intended purposes. Therefore, it is crucial to approach power-source selection systematically, considering the specific needs of each application. Our comparative analysis deepened our understanding of the strengths and limitations of different power supply technologies. Moreover, the integration of computer vision, artificial intelligence,  
Figure 7: Gravimetric energy density of Li-S batteries compared to the state-of-the-art alternative battery technology (Reprint with permission from Ref. [15]. Copyright 2022 IEEE).  
and neural networks has greatly advanced intelligence processes. This combination has made UAVs highly valuable in both military and civilian uses. The use of computational vision and neural networks has enhanced UAV capabilities in surveillance, object detection, and autonomous navigation, among other applications. Looking ahead, ongoing advancements in power supply technologies will continue driving innovation in the field of drones and machine vision. Exploring new power sources like fuel cells and solar panels is crucial to improve the endurance and efficiency of UAVs. In summary, this comparative analysis has contributed to our understanding of power supply technologies for drones and machine vision applications. It emphasizes carefully considering specific requirements when selecting a power source. The integration of computational vision and neural networks has created new possibilities, making UAVs indispensable tools in various industries. With continued research and development, the future of drone power supply technologies looks promising, shaping the potential and capabilities of these autonomous systems.  
Conceptualization, A.P. and G.F.F.; methodology, A.P.; validation, F.N.K. and M.G.T.; writing--original draft preparation, A.P.; writing--review and editing, A.P., G.S.M., M.G.T., F.N.K. and G.F.F.; supervision, M.G.T., F.N.K. and G.F.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Data Availability Statement: Data sharing not applicable.  
The authors declare no conflict of interest.