# Review of Some Applications of Unmanned Aerial Vehicles Technology in the Resource-Rich Country
## 3 Limitations in the Process of IUAVT Adaptation in the Sectors of Economy
**New deep network architectures** are being developed to solve the problems of processing images obtained from UAVs. For example, [121] discusses methods for solvingproblems (cv3) and (cv4) using deep neural networks for processing data obtained from low-flying UAVs;
3. **Methods of processing multispectral data are being developed. ** For example, in [122], methods of processing high-resolution multispectral data for analyzing the state of agricultural crops are considered;
4. **Flight planning and management. ** Manual and semi-automatic control of individual UAVs is presented in a number of developments. Commercial software systems for manual and semi-automatic control of individual branded devices are presented by DroneDeploy [123], senseFly(eMotion) [124], Pix4D [125], and DJI (Terra) [126]. However, the software can only be used with devices of the corresponding manufacturer. The basic functionality of the commercial management systems is limited to the following tasks: * Allocation of an area within which the surface is monitored and, in some cases, inspection of buildings is performed; * Calculation of the optimal route to cover the territory. In most cases, the Zamboni method is used [127], that is, zigzag movement, which guarantees the energy-efficient coverage of the area; * Flight simulation (pre-launch test); * Receiving flight data (UAV status) in real time with the possibility of emergency termination of the flight, if necessary; * Processing (consolidation) of the collected material (the collected images form an overall picture of the inspected area, depending on the task). It is difficult or impossible to use effectively the commercial software in a heterogeneous UAV fleet. There is open-source software: ArduPilot (MissionPlanner) [128], papparazi UAV [129], microkopter [130], Dronecode [131], LibrePilot [132], and OpenDroneMap [133]. Support for the first two systems was discontinued. The remaining systems do not yet have support for piloting many UAVs, with the exception of Dronecode. FlyMASTER application has been developed for the Dronecode PX4 flight control system, and it provides piloting of a variety of UAVs [134]. Along with the ordinary tasks of flying or surface treatment (available only for DJI), it is necessary to plan the optimal movement of the UAV to solve a variety of tasks. The following tasks of UAV flight optimization are highlighted in [135]: area coverage, search operations, routing for a set of locations, data gathering and recharging in a wireless sensor network (WSN), allocating communication links and computing power to mobile devices, and operational aspects of a self-organizing network of drones. There is summarized list with highlighting the **following tasks of UAV flight control:**  
* **Area coverage problem** must be solved for many UAVs applications (PA, MoEP, EoM, MoWAL, MoHGP, and TrM). Although the standard solution is to use the Zamboni algorithm [136], however, in many cases there needed the algorithms, which optimize the UAV route for specific applications. For example, in [137], the route is optimized for the application of fertilizers and pesticides, taking into account the regions exposed to stress and identified at the preliminary stage. The optimal routes are based on the traveling salesman problem algorithm and the Voronoi diagram. In [138], a model of mixed integer linear programming (MILP) was developed to solve the problems of UAV routing and planning changing stations for checking belt conveyors with a total length of about 120 km and containing 230 control points;
* **The search task** is often mentioned in the literature in connection with the search for victims in emergency zones. In a number of modern researches, the search algorithms are being developed to rescue victims using several UAVs. The most effective algorithm is considered to be the fish-inspired algorithm for multi-UAV missions (FIAM) [139], which surpassed its competitor layered search and rescue (LSAR) [140] in terms of the number of people rescued in similar accident scenarios.