







































Urdaneta et al./Future Technology                                                                                                 May 2022| Volume 01 | Issue 01 | Pages 07-13 

7 

 

 

 

Review 

How do drones facilitate human life? 
Gerardo Antonio Urdaneta*, Christopher Meyers, Lauren Rogalski 

Department of Mechanical Engineering, Arkansas Tech University, 1811 N Boulder Ave, Russellville, AR, 72801, USA 
 

A R T I C L E   I N F O 
 

Article history: 
Received 01 March 2022  
Received in revised form 
28 March 2022 
Accepted 31 March 2022 
 
Keywords: 
Drones, LIDAR, metaheuristics,  
heuristics, stochastic 
 
*Corresponding author 
Email address: gerar4406@gmail.com 
 
 
DOI: 10.55670/fpll.futech.1.1.2 

A B S T R A C T 
 

Drone technology can provide a more cost-effective solution for many problems 
in different industries. This paper focuses on discussing how drones facilitate 
human life in various fields. They include infrastructure inspection, agriculture, 
medium and high-valued good delivery, geographical monitoring, rescue, and 
law enforcement. These areas were chosen because they can have the greatest 
impact if drones are used. Aerial unmanned vehicles can be used to map both 
horizontal surfaces and vertical structures. This can allow for a reduction in 
maintenance costs for buildings, cranes, wind turbines, speedways, and other 
infrastructures. It was found that the inspection cost for wind turbines could be 
reduced from 0.7% to 0.21% using drones. In terms of agriculture benefits, 
drones can use 800% less pesticide to provide the same protection benefits 
against plagues when compared to more conventional electric air-pressure 
knapsack sprayer (EAP) systems. Furthermore, it was determined that drones 
could save countless police officers' and civilians' lives by providing critical 
information in highly dangerous situations such as robberies, hostage cases, 
and car chases. The main obstacle that refrained from the widespread use of 
copter drones in these industries has been their limited flight time. Flight times 
of over two hours must be constantly achieved for the system to become cost-
effective when compared to the traditional methods that are already in place.  

 
 

 
1. Introduction  

During the past two decades, there has been an increase 
in the application of unmanned aerial vehicles (UAV) for 
communication, delivery of products, and transportation. 
Aerial entertainment for the movie industry, photography, 
precision agriculture, and law enforcement are some of the 
many industries drones are currently used in [1]. Drones are 
being used for military purposes in extensive missions [2]. 
Unmanned aerial vehicles can be used to survey roads, 
inspect infrastructure projects, and scan bridges for failure 
points in conditions where remote access is crucial. 
Furthermore, container crane health monitoring is a time-
consuming and expensive process based on human visual 
inspection. Due to the high costs attributed to the different 
safety regulations for this dangerous job, automation with 
drones and image processing techniques is a viable way to 
reduce the procedure costs [3]. According to the Michigan 
Department of Transportation, an 8-hour manual inspection 
of the deck on a four-lane divided highway bridge located 
near a metropolitan with a two men crew and heavy 
equipment would take $4,600. On the other hand, conducting 
an inspection using drones with a crew of one pilot and one 
spotter would take $1,200, and it would be completed within 
an hour [4]. The agricultural industry can also employ the 

drone for precision farming by outfitting a spraying system 
for autonomous pesticide spraying [5], mounting a camera to 
track livestock [6], configuring LIDAR to map the terrain for 
crop fields [7], and structure planning. With an estimated 
increase of 70% in the global food demand projected for 2050, 
alongside a reduction in arable land the farming sector needs 
a cost-effective way to increase production by automating the 
agricultural process. UAVs can provide a solution to this 
problem for small-scale farmers whose resources are limited 
[8]. Drones can be used to provide a fast response in case a 
wildfire arises. The current techniques for wildfire early 
response are ground assessment teams, helicopter aerial 
visualization, and satellite imagery, but all of them have their 
practical limitations. Manual wildfire assessment has the 
constraint of limited visibility, while aerial evaluation through 
human-crewed vehicles is expensive, cannot be instantly 
deployed, and are especially dangerous for the pilots 
involved. Satellite photography also has its limitations due to 
limited resolution, which leads to data averaging for extensive 
areas making it difficult to have a clear picture of the 
spreading fire, and the prolonged times it takes to resurvey 
the same area [9]. An unmanned aircraft can increase 
awareness and extend law enforcement reach in different 
scenarios during perilous circumstances, for example, a 

 

 

Future Technology 

Open Access Journal 

https://doi.org/10.55670/fpll.futech.1.1.2 

 

May 2022| Volume 01 | Issue 01 | Pages 07-13 

Journal homepage: https://fupubco.com/futech 

 

ISSN 2832-0379 

mailto:gerar4406@gmail.com
https://doi.org/10.55670/fpll.futech.1.1.2
https://fupubco.com/futech


Urdaneta et al./Future Technology                                                                                                 May 2022| Volume 01 | Issue 01 | Pages 07-13 

8 

 

hostage situation, without putting human lives in any danger 
[10].  They can also be used as a method to help police patrol 
to manage traffic accidents, traffic congestion, and car chases. 
Drones are also being used in the delivery/parcel service with 
different private companies. Research has shown that it is 
inevitable that drones will become more widely used and 
accepted. Medical supplies and other extremely important 
goods can be shipped in remote areas using drones. Even 
though they are a revolutionary idea, their use is still 
restricted in urban areas due to Federal Aviation 
Administration (FAA) restrictions. Finally, they can also be 
used to assess potential pollution zones during natural and 
human disasters. Sensors can be attached to provide the 
system with the capabilities to detect radiation or cancerous 
chemicals. Their use can also be extended to recovery 
missions, one set of drones can go into the affected area and 
determine where the critical pollutants are, while another 
group of drones can scan for survivors and provide essential 
information for rescue teams. By employing drones in these 
cases manned aircrafts do not have to be used, avoiding 
putting the pilots at any risk.  

The purpose of this review paper is to deliver a 
comprehensive study about how drones facilitate human life. 
The use of drones in agriculture, infrastructure inspection, 
wildfire management, medium and high-valued goods 
delivery, geographical monitoring, rescue, and traffic 
enforcement drones will be explored since these are the 
industries that look the most promising for unmanned 
aircraft. 

2. Infrastructure inspection  

There are still significantly many homes from 1970s that 
are not efficiently built as those of today. Almost 40% of 
energy lost is due to heat transfer and air leaks in these 
residences [11]. Although there are already ways to detect the 
infiltration and exfiltration regions of houses, the idea of using 
Unmanned Aerial Systems (UAS) paired with infrared 
cameras and 3D CAD modeling has become a new topic of 
discussion based on safety, low costs, non-destructive nature, 
and efficiency [12]. The use of infrared technology has shown 
to be of effective use because almost all materials emit 
infrared energy, which can be absorbed. This helps with the 
detection of changes in temperature and as a cost-reducing 
monitoring system. The most significant benefit of using 
infrared technology, besides its non-destructive and no-
contact properties, is the stark contrast and immediate 
notification of irregular conditions [13]. There are two 
methods to audit a building: active thermography, where an 
energy source must create a thermal boundary between the 
background and the element of interest, and passive 
thermography, where the element of interest is already at a 
higher temperature than its surroundings. If using the former, 
pre-existing knowledge about the building defects must be 
known, thus why passive thermography is used on buildings 
showing suspicion of thermal defects [14]. It has been widely 
accepted to split the building audit process into three steps: 
pre-flight drone path planning, in-flight infrared 
thermography, and post-flight image processing. For the first 
step, there are many factors for flight planning, but the drone 
heavily relies on the Global Positioning System (GPS) for 
accuracy [15]. Some obstacles to drone flight are battery life, 
power output, and legal regulations of air space [16]. It is 
recommended that there is an established flight plan that 
targets all wanted areas of the building and that there are no 
outside obstacles that would prevent the drone from 
following its path. An acquired method is having “waypoints” 

that the drone uses as a reference on the GPS system. In order 
to facilitate the building mapping operation, developed three 
modes that the unmanned system (US) can operate with. The 
first mode is fully controlled by a human operator, although it 
increases the vibrations in the system due to the operator’s 
inability to completely dampen the motion, it can be used as a 
fast way to reach a point of interest. The second mode is an 
assisted autonomous hovering technique alongside human-
controlled operation. Lastly, the third mode is a fully 
autonomous flying method guided by a GPS through markers. 
To attain a highly efficient flying plan, it is preferred to use a 
hybrid combination of human operator control and 
autonomous hovering. The operator will quickly reach the 
point of interest; then the independent hovering system will 
take over to achieve stable flight so the images can be taken 
with the highest possible precision. Another approach to 
drone mapping is the use of mathematical planning. This 
planning has discovered that it is best for the drone to fly in 
strips in a “zig-zag” motion with an altitude twice the height 
of the building for best results [17]. It was proven from the 
case study that a strip pattern with at least a 70% overlap is 
suitable for gathering data to audit or visualize energy use in 
buildings. The time of the day when the drone flies is also 
considered to avoid direct radiation from the sun that would 
cause false positives; for maximum accuracy, it is preferred to 
scan the desired infrastructure before sunrise or after sunset. 
It is best to have the drone take pictures before sunrise and 
after sunset [18]. Having four combined wide-angle cameras 
helps to increase the base-height ratio and expand the angle 
of view, which also requires fewer ground control points. 
Since the determination of the shortest route between several 
points is a non-deterministic polynomial (NP) problem, the 
most efficient path will usually be determined by the shape of 
the area that wants to be mapped.  Metaheuristic methods can 
be used to find near-optimized routes in a given area. The 
benefit of using Metaheuristic methods over “zig-zag” paths is 
the reduction of flying time. It is also possible to include other 
external factors in the heuristic solution that otherwise would 
not be included in the zigzagging route, such as distance from 
the take-off platform and interference with drone paths. 
Figure 1 shows the difference between “zig-zag” and 
metaheuristic paths (scan-based area division) while using 
three drones to scan a given area [19]. As far as post-flight 
image processing goes, it depends on altitude, quality, timing, 
spectrum, and overlap [20]. Geo-referencing is greatly used 
with time-stamped data from the GPS during the flight [21]. 
However, it was found that eliminating the measurements of 
the ground control points and just using the geotags results in 
lower accuracy, but for difficult terrains, this is needed. The 
3D modeling methods can be separated into geo clusters and 
singular buildings. As for the specific 3D modeling process, it 
was found that 3D model generation software tends to be 
more successful with RGB photos. No truly autonomous 
system for 3D model generation of building geometry using 
thermal imaging has been recorded in a scholarly article [22]. 
For enough data, it is recommended to take approximately 
1000-1300 photos for one simulation. Similar to the building 
inspection, the crane inspection can be segmented into three 
steps: pre-flight drone path planning, in-flight photography, 
and post-flight image processing. Contrary to the previously 
mentioned case, the crane is both an obstruction and a target 
of interest. Additionally, the unmanned vehicle must move in 
all three directions to obtain a clear picture of the system. 
Figure 2 shows a linear pre-processed trajectory for a quay 
crane.  



Urdaneta et al./Future Technology                                                                                                 May 2022| Volume 01 | Issue 01 | Pages 07-13 

9 

 

 

 
Figure 1. Difference between scan-based (a) area division 
and (b) vertical “zig-zag” division  [19] 
 

 
 

(a) 

 
 

(b) 

Figure 2. Model of a crane unmanned system detection path. 
(a) Optimized and (b) non-optimized  

The set path from  Figure (b) does not consider the drone 
dynamics, and therefore it would be difficult and inefficient 
for the system to follow that trajectory. Through the use of a 
piecewise polynomial function by taking into consideration 
the system’s equations of motion, it is possible to observe a 
deviation from the initial trajectory that would be more fitted 
for the drone’s hovering motion. For a large enough dataset, 
it is required to have around 500 pictures of a single crane to 
create an accurate model to estimate its fatigue life [3]. These 

techniques for infrastructure inspection are not subjected to 
buildings or cranes. The same strategy can be applied to a 
variety of infrastructures such as railways, transmission lines 
[23], bridges, highways, wind farms, dams, manufacturing 
plants, and other highly dangerous areas.  It was determined 
that the manual inspection for wind farms accounted for 0.7% 
of the total turbine operational cost, and if drones were to 
fully automate the process, that cost would be reduced by 
70%. Moreover, a reduction of 90% in the lost revenue during 
the inspection could be attained [24]. 

3. Agricultural industry 

As a response to the global food crisis the world is 
heading towards in the next decades, unmanned aircraft 
technologies can soothe the disaster by providing small 
farmers in developing countries with an accessible way of 
increasing their yield production. Unmanned aircraft can be 
used as a spraying mechanism due to their ability to achieve 
long distances in single flights. Even though the amount of 
pesticide is limited by the drone’s payload capabilities, by 
increasing the propeller size and reducing the number of 
motors, it is possible to decrease the power consumed and 
thus amount superior flight times. This relies on the fact that 
by having a larger rotor, the effective area that pushes air 
down increases, and it is translated into a more efficient 
hovering. Yallappa et al. [25] was able to cover an area of 1.15 
ha/hr with an application rate of 55.15 L/ha. The work 
compared the coarse nozzle control efficacy between a 
volumetric spraying rate of 16.8 L/ha and 28.1 L/ha and 
determined that it did not differ significantly, but it was 
meaningfully higher than finer nozzles with spraying rates of 
9 L/ha. Therefore, it was found that a spraying rate of 16.9 
L/ha was optimal. It is important to note that these values 
reflected the efficacy characteristics of the systemic pesticide 
imidacloprid. The contact pesticide lambda-cyhalothrin 
showed an optimal efficiency rate of 28.1 L/ha. On the other 
hand, conventional electric air-pressure knapsack sprayer 
(EAP) had a drastically higher spraying rate of 225 L/ha and 
450 L/ha and achieved similar deposition losses compared to 
the UAV spraying methods. Furthermore, control efficacy on 
wheat aphids showed to be similar in both situations [26]. 
From the previous results, it is possible to show how 
including spraying systems on drones seems like a promising 
idea to modernize agriculture with low initial costs; these 
systems are less wasteful and more time-efficient than the 
more traditional manual EAPs.  

Huang et al. [5] used a low volumetric rate of 0.3 L/ha 
and was able to cover an area of 14 hectares. Even though it 
may not be optimized for certain applications, the lower flow 
rate allows for a faster insecticide distribution that will allow 
covering more surface area with the same amount of fuel, 
maximizing the fuel to pesticide ratio. It is estimated that the 
system will be capable of covering 0.4 hectares per minute. 
The widespread objective in the mentioned systems focuses 
on increasing the chemical payload and flight duration 
capabilities for these systems. Hydrogen can provide a 
solution to this problem; hydrogen has the highest power 
density among any elements with 120 KJ/g compared to the 
batteries 1 KJ/g. The use of a hydrogen fuel cell would allow 
the drone to achieve longer flights of up to 4 hours for copter 
configurations. Another application for drones in agriculture 
focuses on mapping extensive areas for crop cultivation. Fixed 
wings drones such as the Honeycomb AgDrone Sytem or 
EBEE SQ-SenseFly can cover over 600 acres every hour, 
making them capable of imagining crops, obtaining sunlight 
absorption rates, and soil compositions [27]. In soil sampling, 



Urdaneta et al./Future Technology                                                                                                 May 2022| Volume 01 | Issue 01 | Pages 07-13 

10 

 

the traditional practice consists in obtaining specimens from 
different soil sections and sending them to a laboratory for 
analysis. Additionally, countries’ regulations make the 
constant use of this practice unviable. In some cases, farmers 
are limited in using it once every five years and only for every 
ten hectares. Aerial images can provide a useful insight into 
where the specimens should be taken from, which would be 
translated in time and money savings. For soil pictures, an 
RGB camera is sufficient [28]. Comparable to the previous 
infrastructure section, the process can be divided into two 
sections: pre-flight path planning and post-flight image 
processing. Depending on the surface shape, the system path 
can either have a “zig-zag” shape or heuristics can be used if 
other factors besides path length must also be considered 
[19]. As far as image processing goes, the image segmentation 
is performed in two phases: the picture division into clusters 
through the simple linear iterative cluster, and their 
classification into a smaller number of color categories 
through K-mean clustering. Finally, after inputting the total 
amount of samples desired, an algorithm would map the 
location where the specimens should be taken from on the 
image. This method of localizing the place where the 
specimens should be extracted is more precise than 
estimating it through visual methods. Despite the numerous 
benefits, visual soil techniques still have their own 
drawbacks. The moisture in the soil must be the same such 
that the light reflected by the soil parallels the expected color. 
One of the most recurring problems in the farming industry, 
especially in developing countries, has been the incapability 
to track large amounts of livestock through extensive areas. 
Ranchers usually own extensive territories where their assets 
tend to be scattered around. It requires experience personnel 
to locate and count the number of cattle in a certain area. It is 
common to obtain incorrect evaluations regarding the actual 
condition of the farm. Apart from the fact that miscounting is 
a common issue, this is a costly and labor-intensive process 
that leads to missing assets.  

A potential detection system for large-scale farms can 
consist of a system containing transceivers emitting a signal 
to a receiver attached to the cattle (through a collar), several 
sensing nodes located in areas of interest, and a path 
optimization plan. The location of the cattle will be sent 
through a signal to the closest receiver, and the drone will be 
capable of picking it up after passing through a determined 
path [29]. Alternatively, it is possible to have a certain amount 
of unmanned copter systems spanning over the cattle’s 
location. The livestock will send a signal to one of the drones 
in the sky through a collar transmitter, and that drone will 
send a signal to the server cloud. The former method may be 
more useful for smaller farmers because fewer drones are 
used, in fact, only one drone is used but at the expense of 
lower accuracy and added expense for the implementation of 
local antennas. On the other hand, having multiple copters 
covering a certain area translates into more accurate 
readings, but with more drones, that also incorporates higher 
initial, operating, and maintenance costs. Therefore, the latter 
method should be used for big-scale farms with large 
disposable capital. An additional use for drones in agriculture 
could be reducing the response time necessary to combat a 
wildfire. According to Spinoni et al. [30], a 4°C increase in the 
average global temperature will result in 4.5% of the global 
land becoming arid; this is for a scenario where fossil fuels 
persist as the main source of energy in the future. This shift 
will likely result in more wildfires in regions like Africa and 
South America, causing a subsequent drop in their main 
commodities exports. Drones can be employed to alert people 

in nearby areas about any possible wildfires and get into 
action to reduce the impact. 

4. Law enforcement 

An essential part of regulating traffic crashes is traffic 

enforcement. An advantage for drones in traffic enforcement 

is that they provide an aerial view of drivers and are not 

confined to the obstacles of normal enforcement congestion 

or road network structure. The most recent areas where 

drones are being used in law enforcement are in hostage 

situations, manhunts, crime scene investigations, and traffic 

administration [31]. Other results from the survey concluded 

that traffic enforcement drones are more effective compared 

to other aerial resources like police helicopters [32]. This led 

to an experiment on the enforcement of drones on driving 

speed versus police cruisers. The results showed that drivers 

tend to slow down more for police cruisers which shows that 

drones should not be replacements for human-based traffic 

enforcement but serve more as an aid. UAVs still have many 

challenges that they must undergo related to economics, 

technology, legislature, and public acceptance. The main one, 

in this case, is that of public acceptance. A survey was 

conducted between two groups, those from the US and those 

from Israel, to better grasp the public opinion of drones in 

traffic enforcement. The survey showed that 60-70% of 

Americans support drone technology for fighting crime. The 

second most troubling concern for the public is their privacy. 

It was found from the survey that there was not much of a 

difference in the public opinion regarding drone use for civil 

or police purposes, in both cases the public showed concern 

about drones and their privacy. The study also showed that it 

would be better to start drone enforcement integration in 

interurban spaces that are more open and seen as less 

troubling. It was also acknowledged that there should be 

some official privacy-preserving policy to further help with 

public opinion [32]. Even though many problems must be 

solved to incorporate drones into daily life, their future in the 

industry is promising because they could replace people in 

dangerous jobs, such as a hostage situation, or provide 

surveillance if somebody tries to escape the police.  

5. Goods and medical supplies delivery 

Along with the increasing implementation of drones in 

society, unmanned aircraft systems can be used to deliver 

essential medical supplies in remote areas. In 2007, the 

National Health Laboratory Service (NHLS) and Denel 

Dynamics used a drone to transport biological samples from 

suburban areas to NHLS centers for testing. They are also 

being used in the delivery/parcel service with companies 

such as Amazon, Alibaba, and the DPD group in France. When 

it comes to the distribution industry, people have a choice 

model nowadays to which type of service they prefer to use; 

some customers prefer traditional methods such as trucks 

and motorcycles. Therefore, drones already have started with 

a vital disadvantage relative to the more conventional supply 

options. Drones are restricted by FAA regulations in urban 

zones, and privacy concerns among the public are a problem 

delivery companies should consider [32]. On the other hand, 

medical supplies delivery in isolated areas shows a promising 

future for remote-controlled systems because they are not 

subjected to the more strict urban airspace regulations. 

Zipline and United States Postal Service (USPS) evaluated the 



Urdaneta et al./Future Technology                                                                                                 May 2022| Volume 01 | Issue 01 | Pages 07-13 

11 

 

possibility of medication delivery in Rwanda. Pulver et al. [33] 

developed a simulation indicating that drones can reach 96% 

of the population in a minute, compared to the traditional 

4.3% ambulances could provide. Even though important 

achievements have been made, it is essential to note that 

collisions still occur, and samples do not always reach their 

destination intact. Remote systems delivery of low and 

medium-valued goods is likewise plagued by difficulties. Due 

to the stochastic behavior of delivery requests and the NP 

nature of the traveling salesman problem, creating a drone 

path for different parcels is a highly complex problem. Several 

solutions have been developed, such as utilizing drones along 

with trucks or employing drones along with recharging 

stations. Murray et al. [34] developed a method by which the 

truck would be able to operate in a certain location, and then 

the drone would be used to reach the farthest points. Another 

proposed method consists of using several drones along with 

different trucks where they can be deployed in order to 

minimize completion time. In this case, the main objective is 

to distribute as many packages as possible in the shortest 

period of time [35]. The second most important problem for 

drone delivery is its rechargeability. There are proposed 

solutions where the algorithm develops a path maximizing 

the number of packages that could be delivered while 

reducing the flight distance between recharging stations [36]. 

The drone can also be recharged by landing on mobile 

recharging stations. The algorithm proposed by Yu et al. [37] 

finds the optimal path for the drone to go to different 

locations and determines the landing times on the charging 

stations. Unmanned aircrafts not only have to overcome the 

mentioned technical problems, but they also have to be well 

perceived by the public and show their convenience over 

traditional methods. A study was produced with a choice 

model to compare drones to trucks and motorcycles in 

delivery services; different products for delivery and the 

effects of gender, age, and income were considered as 

variables [38]. The products chosen for the study were 

clothing, beauty products, and urgent documents. A 

hypothesis was then made that customers would be willing to 

use faster, more expensive delivery as the price of the product 

increased.  The study concluded that the preference for drone 

delivery depended on the price and type of commodity. 

Customers were worried about the reliability of the drone for 

expensive items. The results also showed that socio-

demographic characteristics did affect the opinion on drone 

delivery; younger people supported the use of drones more 

than older people. Finally, it is also important to note that this 

survey took data from subjects who have not used the drone 

service before and thus are only predicting how they feel 

about it [38].   

6. Geographical monitoring, discovery, and rescue 

missions 

Geographical monitoring in remotes areas can be 

performed using unoccupied aerial vehicles. For example, 

seagrass environmental monitoring can be achieved through 

the employment of UAVs with high-resolution cameras [39]. 

The advantage of using drones over satellite imagery lies in 

their finer resolution (the best satellite resolution can only 

achieve 1 m compared to drones’ 0.1 m) [40] and more 

accessible time windows; satellites usually have inflexible 

and long revisit cycles. Unmanned vehicles can be employed 

as early survey elements to assess the initial damage in 

disaster zones. During the Haiyon Hurricane in 2013,  

unmanned vehicles were used to determine initial damage 

and locate the most affected neighborhoods [41]. Remote-

controlled aircrafts can also be applied to detect harmful 

chemicals in different environments. Researchers from the 

Rochester Institute of Technology (RIT) are thinking about 

ways to measure nitrogen oxide contamination using a series 

of drones that would fly into the polluted volume with 

synchronized cameras [42]. Capolupo et al. [43] used high-

definition cameras to determine the copper content in 

agricultural areas to predict cancer risks. The recognition of 

the affected areas by chemical, biological, or nuclear 

contamination will provide valuable information if a rescue 

mission has to be planned. The service of drones in the field is 

crucial since it will avoid the use of manned aircrafts.   Several 

sensory techniques can be employed to perceive different 

electromagnetic frequencies. Some methods are scattering, 

differential absorption, fluorescent, and doppler. Sensors are 

used depending upon the electromagnetic spectrum desired 

to analyze. Some examples of commercial sensors are 

Zenmuse XT2 which is used for infrared detection, and  

DroneRad for radiation [44]. Depending on the nature of the 

disaster (chemical, nuclear…) a swarm of drones could be 

easily equipped with the precise sensor to detect the pollution 

coverage. Furthermore, an individual using virtual reality 

goggles would be able to control a drone in a first-person 

view, making him/her capable of maneuvering the system to 

detect the critical areas in the accident without putting them 

in danger. Once more, the system’s endurance is critical for 

the mission. The area spanned by the disaster will directly 

affect the UAVs effectiveness. The greater the disaster area, 

the more drones will be needed in order to cover it. This could 

mean delays in subsequent rescue missions. Additionally, the 

effects of long-term exposure to different substances and 

radiation on drone performance should be studied further. 

More research in this area will allow rescue and monitoring 

teams to have a better picture of the drone’s performance 

during the mission. 

7. Conclusions 

The importance of drones in the infrastructure, 
agriculture, medium and high-valued good delivery, 
geographical monitoring, rescue, and law enforcement 
industries was explored. The main impediments to their use 
in these industries were also discussed. In terms of 
infrastructure inspection, path flight planning can be used to 
have one or several drones mapping a certain flat region. The 
same technique can be applied to map vertical structures. It 
was determined that the “zig-zag” method was a simple path 
to use, but if more factors are considered (number of drones, 
area shape, landing site…), heuristics can be used to optimize 
the route. For wind turbines, a reduction of 90% in the lost 
revenue during the inspection could be attained. In addition, 
inspection costs could go from 0.7% to 0.21%. When it comes 
to container cranes, a reduction of cost of 70% can be 
estimated. In the agricultural industry, pesticides sprayed by 
drones can be used more efficiently compared to EAP 
spraying methods. Drone mapping can be used along with 
algorithms to determine the most efficient places to get soil 
samples from by looking at their color, causing the process to 
be time-efficient. Unmanned systems can also be used to track 



Urdaneta et al./Future Technology                                                                                                 May 2022| Volume 01 | Issue 01 | Pages 07-13 

12 

 

livestock by either picking the signal from antennas or by 
obtaining the signal from the collar the animal is wearing. 
Drones can provide help to police officers in highly dangerous 
situations by offering aerial assistance through surveillance 
and intelligence. Unmanned systems can be used to deliver 
high values goods in a timely manner, and they can also be 
employed to detect the reduction of fauna and flora or assess 
highly hazardous zones. Even though the future is promising 
for unmanned aircraft systems, there is a lot to be done in 
terms of improvements to see drones in daily activities. Flight 
endurance for copter drones must be increased to at least two 
hours, and their overall price should be decreased by around 
15%. This should provide a strong case for companies to shift 
from their conventional methods.  

Ethical issue 
The authors are aware of and comply with best practices 

in publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that submitted work is original and 
has not been published elsewhere in any language. 

Data availability statement 
Data sharing is not applicable to this article as no datasets 

were generated or analyzed during the current study. 

Conflict of interest 

The authors declare no potential conflict of interest. 

References 

[1] Zhang F, Maddy J. Investigation of the Challenges and 
Issues of Hydrogen and Hydrogen Fuel Cell 
Applications in Aviation 2021. 
https://doi.org/10.36227/TECHRXIV.14958057.V1. 

[2] Wang J, Jia R, Liang J, She C, Xu YP. Evaluation of a 
small drone performance using fuel cell and battery; 
Constraint and mission analyzes. Energy Reports 
2021;7:9108–21. 
https://doi.org/10.1016/J.EGYR.2021.11.225. 

[3] Maboudi M, Alamouri A, De Arriba López V, Bajauri MS, 
Berger C, Gerke M. DRONE-BASED CONTAINER CRANE 
INSPECTION: CONCEPT, CHALLENGES and 
PRELIMINARY RESULTS. ISPRS Ann Photogramm 
Remote Sens Spat Inf Sci 2021;5:121–8. 
https://doi.org/10.5194/ISPRS-ANNALS-V-1-2021-
121-2021. 

[4] Here’s How State DOTs are Using Drones - Operations - 
Government Fleet n.d. https://www.government-
fleet.com/332236/70-of-state-dots-use-drones-heres-
how-they-use-them (accessed March 28, 2022). 

[5] Huang Y, Hoffmann WC, Lan Y, Wu W, Fritz BK. 
Development of a Spray System for an Unmanned 
Aerial Vehicle Platform. Appl Eng Agric 2009;25:803–
9. https://doi.org/10.13031/2013.29229. 

[6] Li X, Xing L. Use of Unmanned Aerial Vehicles for 
Livestock Monitoring based on Streaming K-Means 
Clustering. IFAC-PapersOnLine 2019;52:324–9. 
https://doi.org/10.1016/J.IFACOL.2019.12.560. 

[7] Wallace L, Lucieer A, Watson C, Turner D. 
Development of a UAV-LiDAR System with Application 
to Forest Inventory. Remote Sens 2012, Vol 4, Pages 
1519-1543 2012;4:1519–43. 
https://doi.org/10.3390/RS4061519. 

[8] Hafeez A, Husain MA, Singh SP, Chauhan A, Khan MT, 
Kumar N, et al. Implementation of drone technology 

for farm monitoring & pesticide spraying: A review. Inf 
Process Agric 2022. 
https://doi.org/10.1016/J.INPA.2022.02.002. 

[9] Akhloufi MA, Couturier A, Castro NA. Unmanned Aerial 
Vehicles for Wildland Fires: Sensing, Perception, 
Cooperation and Assistance. Drones 2021, Vol 5, Page 
15 2021;5:15. 
https://doi.org/10.3390/DRONES5010015. 

[10] Murphy DW, Cycon J. Applications for mini VTOL UAV 
for law enforcement. 
Https://DoiOrg/101117/12336986 1999;3577:35–43. 
https://doi.org/10.1117/12.336986. 

[11] Detecting sources of heat loss in residential buildings 
from infrared imaging n.d. 
https://dspace.mit.edu/handle/1721.1/68921 
(accessed March 28, 2022). 

[12] Schuffert S, Voegtle T, Tate N, Ramirez A. Quality 
Assessment of Roof Planes Extracted from Height Data 
for Solar Energy Systems by the EAGLE Platform. 
Remote Sens 2015, Vol 7, Pages 17016-17034 
2015;7:17016–34. 
https://doi.org/10.3390/RS71215866. 

[13] Clark MR, McCann DM, Forde MC. Application of 
infrared thermography to the non-destructive testing 
of concrete and masonry bridges. NDT E Int 
2003;36:265–75. https://doi.org/10.1016/S0963-
8695(02)00060-9. 

[14] Fox M, Coley D, Goodhew S, De Wilde P. Thermography 
methodologies for detecting energy related building 
defects. Renew Sustain Energy Rev 2014;40:296–310. 
https://doi.org/10.1016/J.RSER.2014.07.188. 

[15] Steffen R, Wolfgang Förstner. On visual real time 
mapping for unmanned aerial vehicles. 21st Congr. Int. 
Soc. Photogramm. Remote Sens., 2008. 

[16] Volkmann W, Grenville Barnes. Virtual surveying: 
Mapping and modeling cadastral boundaries using 
Unmanned Aerial Systems (UAS). Proc. FIG Congr. 
Engag. Challenges—Enhancing Relev.,  Kuala Lumpur, 
Malaysia: 2014, p. 16–21. 

[17] Lizarazo I, Angulo V, Rodríguez J. Automatic mapping 
of land surface elevation changes from UAV-based 
imagery. 
Https://DoiOrg/101080/0143116120161278313 
2017;38:2603–22. 
https://doi.org/10.1080/01431161.2016.1278313. 

[18] González-Aguilera D, Lagüela S, Rodríguez-Gonzálvez 
P, Hernández-López D. Image-based thermographic 
modeling for assessing energy efficiency of buildings 
façades. Energy Build 2013;65:29–36. 
https://doi.org/10.1016/J.ENBUILD.2013.05.040. 

[19] Xiao S, Tan X, Wang J. A Simulated Annealing 
Algorithm and Grid Map-Based UAV Coverage Path 
Planning Method for 3D Reconstruction. Electron 
2021, Vol 10, Page 853 2021;10:853. 
https://doi.org/10.3390/ELECTRONICS10070853. 

[20] Yahyanejad S, Rinner B. A fast and mobile system for 
registration of low-altitude visual and thermal aerial 
images using multiple small-scale UAVs. ISPRS J 
Photogramm Remote Sens 2015;104:189–202. 
https://doi.org/10.1016/J.ISPRSJPRS.2014.07.015. 

[21] Rodriguez-Gonzalvez P, Gonzalez-Aguilera D, Lopez-
Jimenez G, Picon-Cabrera I. Image-based modeling of 
built environment from an unmanned aerial system. 
Autom Constr 2014;48:44–52. 
https://doi.org/10.1016/J.AUTCON.2014.08.010. 

[22] Borrmann D, Nüchter A, Dakulović M, Maurović I, 



Urdaneta et al./Future Technology                                                                                                 May 2022| Volume 01 | Issue 01 | Pages 07-13 

13 

 

Petrović I, Osmanković D, et al. A mobile robot based 
system for fully automated thermal 3D mapping. Adv 
Eng Informatics 2014;28:425–40. 
https://doi.org/10.1016/J.AEI.2014.06.002. 

[23] Deng C, Wang S, Huang Z, Tan Z, Liu J. Unmanned aerial 
vehicles for power line inspection: A cooperative way 
in platforms and communications. J Commun 
2014;9:687–92. 
https://doi.org/10.12720/JCM.9.9.687-692. 

[24] Kabbabe Poleo K, Crowther WJ, Barnes M. Estimating 
the impact of drone-based inspection on the Levelised 
Cost of electricity for offshore wind farms. Results Eng 
2021;9:100201. 
https://doi.org/10.1016/J.RINENG.2021.100201. 

[25] Yallappa D, Veerangouda M, Maski D, Palled V, 
Bheemanna M. Development and evaluation of drone 
mounted sprayer for pesticide applications to crops. 
GHTC 2017 - IEEE Glob Humanit Technol Conf Proc 
2017;2017-January:1–7. 
https://doi.org/10.1109/GHTC.2017.8239330. 

[26] Wang G, Lan Y, Qi H, Chen P, Hewitt A, Han Y. Field 
evaluation of an unmanned aerial vehicle (UAV) 
sprayer: effect of spray volume on deposition and the 
control of pests and disease in wheat. Pest Manag Sci 
2019;75:1546–55. https://doi.org/10.1002/PS.5321. 

[27] Puri V, Nayyar A, Raja L. Agriculture drones: A modern 
breakthrough in precision agriculture. 
Https://DoiOrg/101080/0972051020171395171 
2017;20:507–18. 
https://doi.org/10.1080/09720510.2017.1395171. 

[28] Huuskonen J, Oksanen T. Soil sampling with drones 
and augmented reality in precision agriculture. 
Comput Electron Agric 2018;154:25–35. 
https://doi.org/10.1016/J.COMPAG.2018.08.039. 

[29] Behjati M, Mohd Noh AB, Alobaidy HAH, Zulkifley MA, 
Nordin R, Abdullah NF. LoRa Communications as an 
Enabler for Internet of Drones towards Large-Scale 
Livestock Monitoring in Rural Farms. Sensors 2021, 
Vol 21, Page 5044 2021;21:5044. 
https://doi.org/10.3390/S21155044. 

[30] Spinoni J, Barbosa P, Cherlet M, Forzieri G, McCormick 
N, Naumann G, et al. How will the progressive global 
increase of arid areas affect population and land-use in 
the 21st century? Glob Planet Change 
2021;205:103597. 
https://doi.org/10.1016/J.GLOPLACHA.2021.103597. 

[31] Rosenfeld A, Maksimov O. Optimal cruiser-drone 
traffic enforcement under energy limitation. Artif Intell 
2019;277:103166. 
https://doi.org/10.1016/J.ARTINT.2019.103166. 

[32] Rosenfeld A. Are drivers ready for traffic enforcement 
drones? Accid Anal Prev 2019;122:199–206. 
https://doi.org/10.1016/J.AAP.2018.10.006. 

[33] Pulver A, Wei R, Mann C. Locating AED Enabled 
Medical Drones to Enhance Cardiac Arrest Response 
Times. 
Https://DoiOrg/103109/1090312720151115932 
2016;20:378–89.  

[34] Murray CC, Chu AG. The flying sidekick traveling 
salesman problem: Optimization of drone-assisted 
parcel delivery. Transp Res Part C Emerg Technol 
2015;54:86–109. 
https://doi.org/10.1016/J.TRC.2015.03.005. 

[35] Poikonen S, Wang X, Golden B. The vehicle routing 
problem with drones: Extended models and 
connections. Networks 2017;70:34–43. 
https://doi.org/10.1002/NET.21746. 

[36] Hong I, Kuby M, Murray A. A Deviation Flow Refueling 
Location Model for Continuous Space: A Commercial 
Drone Delivery System for Urban Areas. Adv Geogr Inf 
Sci 2017:125–32. https://doi.org/10.1007/978-3-319-
22786-3_12. 

[37] Yu K, Budhiraja AK, Tokekar P. Algorithms for Routing 
of Unmanned Aerial Vehicles with Mobile Recharging 
Stations. Proc - IEEE Int Conf Robot Autom 
2018:5720–5. 
https://doi.org/10.1109/ICRA.2018.8460819. 

[38] Kim SH. Choice model based analysis of consumer 
preference for drone delivery service. J Air Transp 
Manag 2020;84:101785. 
https://doi.org/10.1016/J.JAIRTRAMAN.2020.101785. 

[39] Yang B, Hawthorne TL, Hessing-Lewis M, Duffy EJ, 
Reshitnyk LY, Feinman M, et al. Developing an 
Introductory UAV/Drone Mapping Training Program 
for Seagrass Monitoring and Research. Drones 2020, 
Vol 4, Page 70 2020;4:70. 
https://doi.org/10.3390/DRONES4040070. 

[40] Colomina I, Molina P. Unmanned aerial systems for 
photogrammetry and remote sensing: A review. ISPRS 
J Photogramm Remote Sens 2014;92:79–97. 
https://doi.org/10.1016/J.ISPRSJPRS.2014.02.013. 

[41] Drones: a force for good when flying in the face of 
disaster | Humanitarian response | The Guardian n.d. 
https://www.theguardian.com/global-
development/2015/jul/28/drones-flying-in-the-face-
of-disaster-humanitarian-response (accessed March 
28, 2022). 

[42] Researchers using drones to detect noxious gas 
released by explosions | RIT n.d. 
https://www.rit.edu/news/researchers-using-drones-
detect-noxious-gas-released-explosions (accessed 
March 28, 2022). 

[43] Capolupo A, Pindozzi S, Okello C, Fiorentino N, Boccia 
L. Photogrammetry for environmental monitoring: The 
use of drones and hydrological models for detection of 
soil contaminated by copper. Sci Total Environ 
2015;514:298–306. 
https://doi.org/10.1016/J.SCITOTENV.2015.01.109. 

[44] Rabajczyk A, Zboina J, Zielecka M, Fellner R. 
Monitoring of Selected CBRN Threats in the Air in 
Industrial Areas with the Use of Unmanned Aerial 
Vehicles. Atmos 2020, Vol 11, Page 1373 
2020;11:1373. 
https://doi.org/10.3390/ATMOS11121373. 

 
         
 

 This article is an open-access article 

distributed under the terms and conditions of the Creative 

Commons Attribution (CC BY) license 

(https://creativecommons.org/licenses/by/4.0/). 


