Over the passage of time Unmanned Autonomous Vehicles (UAVs), especially
 Autonomous flying drones grabbed a lot of attention in Artificial Intelligence.
 Since electronic technology is getting smaller, cheaper and more efficient, huge
 advancement in the study of UAVs has been observed recently. From monitoring
 floods, discerning the spread of algae in water bodies to detecting forest trail, their
 application is far and wide. Our work is mainly focused on autonomous flying
 drones where we establish a case study towards efficiency, robustness and accuracy
 of UAVs where we showed our results well supported through experiments.
 We provide details of the software and hardware architecture used in the study. We
 further discuss about our implementation algorithms and present experiments that
 provide a comparison between three different state-of-the-art algorithms namely
 TrailNet, InceptionResnet and MobileNet in terms of accuracy, robustness, power
 consumption and inference time. In our study, we have shown that MobileNet has
 produced better results with very less computational requirement and power consumption.
 We have also reported the challenges we have faced during our work
 as well as a brief discussion on our future work to improve safety features and
 performance.
case study on optimal deep learning model for UAVs