Utilization of Agricultural Drones in Farming by Harnessing the Power of Aerial Intelligence

Abstract

Agricultural drones, sometimes called uncrewed aerial vehicles (UAVs) or unmanned aerial systems (UAS), have become a game-changing innovation in today's farming practices. These airborne gadgets equipped with sophisticated sensors and imaging capabilities have made agricultural techniques of the past obsolete. This article explores the concept of airborne intelligence and serves as a lens through the many facets of farm drones and their impact on farming. We delve into how drones transform the farm scene, from crop monitoring and precision agriculture to data-driven decision-making, and we look at how drones enable these approaches.

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Review Article
Malays. j. med. biol. res.
Utilization of Agricultural Drones in Farming by
Harnessing the Power of Aerial Intelligence
Ravikiran Mahadasa1*, Pavani Surarapu2, Vishal Reddy Vadiyala3, Parikshith Reddy Baddam4
1Senior ETL Lead, Data Inc., Charlotte, NC 28262, USA
2Senior Java Full Stack Developer, Ohio Department of Job and Family Services, State of Ohio, Columbus, Ohio, USA
3.Net Developer, AppLab Systems, Inc., South Plainfield, NJ 07080, USA
4Software Developer, Data Systems Integration Group, Inc., Dublin, OH 43017, USA
*Email for Correspondence: ravikiranmahadasa1985@gmail.com
ABSTRACT
Agricultural drones, sometimes called uncrewed aerial vehicles (UAVs) or unmanned aerial systems (UAS),
have become a game-changing innovation in today's farming practices. These airborne gadgets equipped with
sophisticated sensors and imaging capabilities have made agricultural techniques of the past obsolete. This
article explores the concept of airborne intelligence and serves as a lens through the many facets of farm drones
and their impact on farming. We delve into how drones transform the farm scene, from crop monitoring and
precision agriculture to data-driven decision-making, and we look at how drones enable these approaches.
Keywords: Agricultural Drones, UAVs, UAS, Precision Agriculture, Aerial Intelligence, Crop Monitoring
INTRODUCTION
Technological innovation has become a driving force for change in the ever-evolving landscape of agriculture, where
the demand for food production continually increases to meet the demands of a growing worldwide population
(Baddam, 2017). This is because the demand for food production to meet the needs of a rising global population is
constantly on the rise. Amidst the plethora of innovations, agricultural drones have emerged as a game-changing
technology that heralds the beginning of a new age in farming techniques. Farmers can monitor, evaluate, and manage
their crops with outstanding precision thanks to uncrewed aerial vehicles (UAVs) outfitted with advanced sensors and
imaging technology (Dekkati et al., 2016).
Farmers now have access to a degree of control and understanding previously unfathomable because of the integration
of agricultural drones, representing a paradigm change from the conventional farming practices used for centuries
(Deming et al., 2018). This article investigates the varied functions that agricultural drones play in airborne intelligence.
It examines the historical development of farming drones, the various types that cater to specific pastoral needs, and
the vital contributions of agricultural drones to essential aspects of modern farming. We will discover that agricultural
drones are not merely flying devices but are sophisticated data hubs that provide information that pushes data-driven
decision-making as we navigate the topic's historical roots and technological complexities. These aerial gadgets are
rewriting the playbook for sustainable and effective farming in various ways, including crop monitoring and precision
agriculture, as well as analyzing massive amounts of information.
In addition, this investigation digs into the difficulties accompanying this technological revolution. These difficulties
range from the complexities of the legal environment to the economic issues that farmers must face. We gain insights
into how the agriculture business may ethically use the power of drone technology to benefit farmers and the
ecosystem by addressing these difficulties head-on. Taking a look into the future, this article provides a look into some
Manuscript Received: 27 August 2020 - Revised: 20 October 2020 - Accepted: 29 October 2020
This article is is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Attribution-NonCommercial (CC BY-NC) license lets others remix, tweak, and build upon work non-commercially, and although the new works must also
acknowledge and be non-commercial.

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of the upcoming trends and technologies that promise to shape the trajectory of agricultural drones. The future
promises intriguing possibilities that have the potential to reshape the landscape of precision agriculture further. These
possibilities range from technological improvements in the swarming field to the incorporation of cutting-edge
imaging techniques. Our journey through this essay will serve as a comprehensive guide to understanding how
agricultural drones, loaded with aerial intelligence, are both tools and catalysts for a more sustainable, productive, and
technologically advanced future in agriculture.
HISTORICAL EVOLUTION OF AGRICULTURAL DRONES
It is possible to trace the origins of agricultural drones back to their first applications, which were in the surveillance and
military spheres. The idea of uncrewed aerial vehicles, or UAVs, was first implemented during the early to middle of the
20th century. At that time, both World Wars I and II saw the use of aircraft similar to drones to conduct surveillance.
 Military Origins: The advancements made in the military led to the creation of agricultural drones. Radio-
controlled aircraft were initially developed for use in military surveillance during the early part of the 20th
century. These early uncrewed aerial vehicles (UAVs) were crucial in developing the technology that would later
be adapted for use in civilian settings.
 Transformation for Civilian Use: The decade of the 1980s marked the beginning of the transfer from military to
civilian use, which was driven by advances in electronic and remote sensing technology. Researchers and
scientists realized these unmanned systems' potential in agriculture and envisioned a future where these systems
may transform farming operations (Vadiyala et al., 2016).
 Early Agricultural Applications: The 1990s were the decade that saw the introduction of agricultural drones into
the farming scene for the first time. Those who were early technology users conducted experiments to see how
well it would adapt to crop monitoring and aerial surveying. These trailblazing efforts paved the way for the
development of agricultural drones specifically designed for their tasks (Shaporova & Tsvettsykh, 2019).
 Emergence of Purpose-Built Drones: UAVs developed expressly for use in agricultural settings began to appear
around the turn of the millennium. Farmers could collect crucial data on crop health, soil conditions, and
irrigation demands with these purpose-built drones fitted with modern sensors (Surarapu & Mahadasa, 2017).
These sensors included multispectral and infrared cameras.
 Advancements in Precision Agriculture: Around the middle of the 2000s, there was a surge in precision
agriculture, increasing the demand for more advanced drone technology. Agricultural drones have become
essential to precision farming systems, enabling farmers to more accurately manage resources, increase yields,
and lessen their adverse environmental effects.
 Commercialization and Accessibility: In the second part of the 2010s, there was a boom in the commercialization
of agricultural drones and increased accessibility. This technology is now available to a wider variety of farmers
due to the proliferation of businesses specializing in drone production and technology and the introduction of
user-friendly, off-the-shelf solutions.
 Current Landscape: As we get closer and closer to the present day, agricultural drones have developed into
equipment essential for modern farmers. The technology is continually improving, and today's drones come
equipped with cutting-edge features such as the capacity to transmit data in real-time, artificial intelligence for
data analysis, and the integration of other developing technologies such as blockchain to provide traceability in
agricultural production.
The historical development of agricultural drones illustrates a transition from their beginnings in the military to their current
roles as purpose-built, precision agriculture instruments. Farmers may gain new insights and control over their fields due
to continual improvements to this technology, demonstrating its role in defining the present agricultural landscape.
TYPES OF AGRICULTURAL DRONES
Agricultural drones are available in a wide variety of configurations, each of which is customized to particular farming
requirements. Farmers have a variety of alternatives at their disposal for more effective and accurate data collection,
thanks to the numerous varieties of agricultural drones. These options range from fixed-wing UAVs that can cover
considerable expanses to nimble quadcopters that can maneuver through delicate crop rows (Kaluvakuri & Vadiyala,
2016). It is essential to have a solid understanding of the features of each of these distinct types of drones to choose the
appropriate instrument for various agricultural jobs.

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 Fixed-Wing Drones: Drones with fixed wings are modeled after toy airplanes and are intended for efficient and
effective long-distance flight. A single flying with one of these drones can cover a significant amount of land, so
they are ideally suited for large-scale agricultural operations. The high-resolution cameras and sensors mounted
on fixed-wing drones make conducting thorough aerial surveys, crop mapping, and terrain analysis possible.
 Quadcopters: The four rotors and the ability to take off and land vertically are quadcopters' defining
characteristics. They are also known as multirotor drones. These drones excel in agility and are the perfect choice
for precision jobs in areas that are either smaller or have an irregular shape. Quadcopters can hover at low
altitudes, allowing for the collection of detailed data on crop health, pest infestations, and soil conditions, as well
as the capture of high-resolution photographs.
 Hexacopters and Octocopters: Hexacopters, which have six rotors, and octocopters, which have eight rotors, offer
enhanced stability and payload capacity compared to quadcopters, which only have four rotors. Because of this,
they can carry greater sensor payloads or additional equipment without any problems. Hexacopters and
octocopters are frequently selected for more specialized duties, such as transporting sophisticated imaging
equipment or applications that demand extended flight periods.
 Hybrid Drones: Drones classified as hybrids combine elements of multirotor and fixed-wing designs. These
multipurpose UAVs can take off vertically and then transition to a horizontal flight pattern for more effective and
comprehensive coverage over a greater distance. The versatility of hybrid drones lies in their ability to adapt to
various agricultural jobs and environments thanks to the advantages offered by both types.
 Single-Rotor Helicopter Drones: The enormous rotor span of single-rotor helicopter drones makes them well-
suited for lifting big loads and maintaining flight for extended periods. Although they are less commonly used
in agriculture, they find applications where extended endurance and the capability to carry considerable payloads
are needed, such as in large-scale aerial spraying.
 Swarm Drones: Swarm drones are designed to work together in groups, maximizing their productivity and area
of coverage by utilizing swarm intelligence. These drones are ideal for duties such as crop monitoring, pollination,
and pest management since they can cooperate to cover large regions in a short amount of time. There is potential
for additional progress to be made in the field of precision agriculture thanks to swarm technologies.
 Vertical Take-off and Landing (VTOL) Drones: VTOL drones give the flexibility of vertical take-off and landing
in addition to horizontal flight efficiency. This is made possible by combining the advantages of fixed-wing and
multirotor designs. Because these drones can switch between modes without any noticeable hiccups, they are
ideally suited for use in environments with restricted space.
It is crucial for farmers who want to incorporate agricultural drone technology into their operations to have a solid
understanding of the benefits and drawbacks associated with each type of agricultural drone. When it comes to optimal
decision-making in precision agriculture, the type of drone used relies on several criteria, including the size of the
farm, the terrain, the types of crops being grown, and the specific data requirements.
AERIAL INTELLIGENCE FOR CROP MONITORING
The integration of airborne intelligence and crop monitoring has emerged as an essential component of the concept of
precision farming in the context of contemporary agriculture. How farmers view, analyze, and manage their crops has
fundamentally altered by introducing agricultural drones fitted with cutting-edge sensors and imaging technologies.
In the following section, we will investigate how agricultural monitoring procedures are being revolutionized by aerial
intelligence, which is made possible by uncrewed aerial vehicles (UAVs).
 High-Resolution Imagery: Farmers now have access to high-resolution imagery of their farms that is not
available any other way, thanks to aerial intelligence. Farmers can now detect minute differences in crop health,
recognize possible problems, and make more educated decisions regarding irrigation, fertilization, and pest
control thanks to the comprehensive images captured by drones outfitted with cutting-edge cameras.
 Multispectral and Infrared Imaging: Drones used in agriculture frequently have cameras that can gather data
outside of the visible spectrum. These cameras can be multispectral or even infrared. These sensors provide
significant insights regarding aspects of the crop's health that are not visible to the naked eye. Monitoring changes
in plant reflectance enables early diagnosis of stress, disease, or nutritional deficits, allowing farmers to take
preventative action.
 Vegetative Index Mapping: Generating vegetative index maps is one of the most critical applications of airborne
intelligence in crop monitoring. Images taken by drones can be used to calculate indices such as the Normalized

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Difference Vegetation Index (NDVI). These maps evaluate plant vigor, locate areas of concern, and assist farmers
in accurately targeting interventions, which optimizes resource use and maximizes crop yields.
 Real-Time Monitoring: The capacity of agricultural drones to deliver data in real-time is one of the most
significant benefits offered by these machines (Baddam & Kaluvakuri, 2016). Farmers can now watch their crops
in real-time thanks to drones' ability to relay live feeds to ground stations. This immediacy makes it easier to
make quick decisions, which is especially helpful in circumstances in which prompt interventions might reduce
the likelihood of crop losses.
 Terrain and Topography Analysis: Analysis of terrain and topography is also included in aerial intelligence and
crop inspection. Detailed elevation models can be generated by drones equipped with LiDAR or other forms of
3D mapping technology. This knowledge helps determine which regions are more likely to become waterlogged,
improve drainage systems, and develop more effective land management strategies.
 Automated Data Analysis: Because of the vast amount of data that can be captured by agricultural drones,
practical analysis tools are essential. Machine learning techniques and artificial intelligence are frequently used
in aerial intelligence systems to automate data analysis. This not only makes the processing of enormous datasets
go more quickly, but it also improves the accuracy of crop assessments by making it easier to spot minor patterns
and trends (Velloso et al., 2018).
 Early Detection of Pests and Diseases: The early detection and effective treatment of both pests and diseases are
essential to maintaining crop health. Farmers can execute more targeted interventions thanks to rapidly
identifying areas damaged by illnesses or pests, made possible by aerial intelligence. This preventative method
limits the application of pesticides, reducing the negative impact on the environment and maximizing the
effectiveness of crop protection techniques.
 Seasonal Monitoring and Growth Analysis: Farmers can track the growth and development of their crops
throughout time because of the facilitation provided by aerial intelligence, which makes complete seasonal
monitoring possible. The results of this longitudinal study provide valuable insights into the phenology of the
crop, which in turn helps farmers plan and optimize the timing of planting and harvesting.
Incorporating airborne intelligence for crop monitoring implies a paradigm shift in the agricultural practices that are
now in use. Nowadays, where accuracy and productivity are of the utmost importance, the deep insights provided by
agricultural drones allow farmers to make decisions based on the data collected, maximize available resources, and
increase total farm output.
PRECISION AGRICULTURE AND DRONE TECHNOLOGY
The transformational method of farming, known as precision agriculture, uses cutting-edge technology to maximize
output while simultaneously lowering an operation's adverse effects on the surrounding environment. Drone
technology is one of the most essential technological enablers for precision agriculture, and it can potentially transform
the industry entirely. This section investigates the mutually beneficial link between precision agriculture and drones,
illuminating how uncrewed aerial vehicles (UAVs) redefine conventional agricultural techniques.
 Site-Specific Crop Management: Managing crops uniquely unique to each field is fundamental to precision
agriculture. Farmers are now able to collect precise, location-specific data on their farms with the use of drones
that are equipped with advanced sensors and imaging technologies. This information, which can range from the
soil's composition to the crop's health, allows farmers to modify their agricultural methods to meet the specific
requirements of various locations within a field.
 Optimized Resource Use: Drones used in agriculture considerably contribute to the efficient utilization of
available resources. Drones allow farmers to fertilize and irrigate their crops with pinpoint accuracy by delivering
real-time data on the soil conditions, the levels of nutrients, and the water requirements of their crops. This
tailored method helps limit the amount of waste produced, lessens the influence on the surrounding
environment, and ensures that crops receive the elements required for maximum growth.
 Variable Rate Application: Precision agriculture aims to move away from the standard farming practice of
treating all crops the same way. The implementation of inconsistent rate application, in which inputs such as
fertilizers, insecticides, and water are applied at variable rates based on the individual requirements of different
locations within a field, is greatly aided by using drones as a critical component. This method, which has been
fine-tuned, makes the most efficient use of resources while cutting down on unnecessary applications (Soliva et
al., 2007).

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 Crop Health Monitoring: Drones are particularly effective in precision agriculture's essential function: to keep
an eye on how the crops are doing. Drones, which often come equipped with high-resolution cameras,
multispectral sensors, and infrared imaging capabilities, can collect extensive data regarding the state of crops.
Because of this data, farmers can recognize the early warning signs of stress, illnesses, or nutritional deficiencies,
which enables them to implement timely and focused interventions.
 Efficient Pest Management: Agricultural drones are helpful in the execution of specific pest management tactics,
which contributes to efficient pest management. Drones assist farmers in quickly identifying regions that are
afflicted by insect infestations by providing them with real-time imagery and data on the infestations. This makes
it possible to apply pesticides selectively, only in the areas where they are required, lowering the overall amount
of chemicals used and the negative impact on the environment.
 Data-Driven Decision-Making: The vast volumes of data that agricultural drones can capture call for
sophisticated data processing methods. Drones contribute to the development of precision agriculture by
supplying essential data inputs (Hosen et al., 2019). This type of agriculture is dependent on data-driven decision-
making. Data collected by drones is fed into machine learning algorithms and artificial intelligence systems,
which extract meaningful insights from the data. These insights help farmers make more educated decisions
regarding their crops.
 Time-Sensitive Operations: Time is of the essence in completing particular agricultural tasks, such as planting
and harvesting. Farmers can improve the efficiency of their operations with the assistance of drones with real-
time monitoring capabilities. Drones enable farmers to schedule crucial activities more precisely, increasing
production and quality. They do this by delivering up-to-the-minute information about the readiness of their
crops and the conditions of their fields.
 Economic and Environmental Sustainability: The combination of precision agriculture and drone technology is
beneficial to the sustainability of the economy and the environment (Dekkati & Thaduri, 2017). Farmers can attain
a farming model that is more sustainable and resilient by maximizing resource utilization, eliminating waste, and
minimizing the environmental impact of agricultural methods. This will allow farmers to balance long-term
ecological stewardship and productivity in their operations.
The marriage of precision agriculture with drone technology has the potential to bring about a paradigm shift in
standard agricultural methods. Farmers are given the capacity to take a proactive and precise approach to crop
management thanks to the capability of drones to collect high-resolution data in real-time. This, in turn, leads to greater
levels of productivity, resource efficiency, and sustainability in the agricultural sector (Kosutic & De Wrachien, 2012).
DATA ANALYSIS AND DECISION-MAKING
High-resolution photography, multispectral sensors, and the ability to perform real-time monitoring all contribute to
the massive volumes of data that agricultural drones create. The analysis of this data, which provides farmers with
actionable insights that allow for educated decision-making, is where the actual value of this data lies. In this section,
we will discuss the fundamental significance of data analysis within the framework of agricultural drone technology
and how this aspect of modern farming influences the decision-making processes involved.
 Data Collection and Variety: A wide variety of data, such as visual images, multispectral and infrared data,
elevation models, and real-time monitoring feeds, can be collected by agricultural drones. Managing a wide range
of data is essential to understanding the state of the crops, the soil, and other factors that significantly determine
agrarian output (Baddam et al., 2018).
 Big Data Challenges: The vast amount of data that is acquired by agricultural drones, as well as the intricacy of
that data, is a hurdle. Significant computational capabilities are required to manage and analyze large amounts
of data in real-time or very close to real-time (Vadiyala & Baddam, 2018). Computing solutions based in the cloud
and those found at the data's edge are becoming more utilized to process and analyze the data effectively.
 Machine Learning and Artificial Intelligence: Machine learning (ML) and artificial intelligence (AI) algorithms
are critical components in extracting meaningful insights from data sets (Dekkati et al., 2019). These technologies
are used to discern patterns, find anomalies, and make predictions based on data from the past and data being
collected in real-time. Machine learning and artificial intelligence improve the accuracy and speed of data
processing, giving farmers information that can be used.
 Crop Health Monitoring and Disease Detection: Analyzing collected data makes it easier to recognize patterns
related to crop health. Algorithms can identify the early warning symptoms of stress, sickness, and nutrient
shortages through multispectral and infrared data analysis (Deming et al., 2018). Because of this, farmers can
quickly take action and put in place specific measures to lessen the impact of any prospective crop losses.

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 Precision Application of Resources: The insights gleaned from analyzing data make it possible to perform
precision agriculture. Farmers can accurately identify where and when to apply fertilizers, insecticides, and
water. This allows farmers to maximize the utilization of resources while reducing their impact on the
environment. By focusing on specific areas, this technique improves the overall productivity and viability of the
farm.
 Decision Support Systems: Data analysis is the essential component that underpins decision support systems in
the agricultural industry. These systems offer farmers recommendations that can be put into action depending
on the data that has been evaluated. For instance, a decision support system can recommend particular
interventions for pest management, irrigation timing, or crop rotation. This allows farmers to make informed
decisions that align with the most effective agricultural methods.
 Yield Prediction and Planning: Predictive analytics built on top of previous data contribute to yield prediction
models. Farmers can estimate the potential yields of a given season by assessing various elements, including
weather patterns, soil conditions, and crop performance. This foresight helps in strategic planning, enabling
farmers to optimize their plans for logistics, storage, and marketing (Patrick et al., 2019).
 Real-Time Monitoring and Response: The instant monitoring of field conditions is made possible using real-
time data analysis. Farmers can receive fast warnings and information regarding changes in the health of their
crops, occurrences related to the weather, or prospective problems. This ability to respond in real-time boosts the
agility of farmers, allowing them to respond more quickly to newly developing difficulties.
 Continuous Improvement and Adaptation: An iterative process of never-ending development in agricultural
methods is bolstered by data analysis. Farmers can improve their methods for the next growing seasons by analyzing
the results of earlier choices and actions taken on their farms. This flexible strategy, which is informed by the insights
provided by data, helps the continuing improvement of farm management (Mandapuram et al., 2019).
The combination of data analysis with agricultural drone technology is a force that has the potential to revolutionize
farming in the current era completely. Farmers who can process and interpret data in real-time gain the ability to make
proactive, informed decisions, which eventually leads to increased productivity, improved resource efficiency, and
more environmentally responsible agricultural practices (Thaduri et al., 2016). The combination of data analysis with
agricultural drones holds the potential to further enhance the efficiency of farming operations in the years to come.
This possibility is made possible by the ongoing development of technology.
CHALLENGES AND CONSIDERATIONS
Although the widespread use of agricultural drones presents several opportunities for improvement in today's
farming, several obstacles must first be overcome. To ensure the appropriate and productive application of drone
technology in agriculture, it is essential to understand these problems and work to find solutions to them. This section
will discuss some of the most significant difficulties and factors to consider while using agricultural drone technology.
 Regulatory Hurdles: When it comes to the widespread use of agricultural drones, navigating the complicated and
ever-changing regulatory landscapes is one of the most significant obstacles. There are a variety of regulations
concerning the use of drones, which differ from country to country. These regulations can include airspace limits,
licensing requirements, and privacy issues. Farmers and others who operate drones must be knowledgeable about
and compliant with these restrictions, which may affect the breadth and depth of drone activities (Ting, 2010).
 Cost of Technology Adoption: The required initial investment might be a significant barrier for some farmers
when adopting agricultural drones and the technology that goes along with them. While drones have become
more affordable over time, the expense of high-quality, specialized sensors and tools for data analysis may still be
prohibitive for certain people (Surarapu et al., 2018). Farmers must thoroughly analyze the return on investment
and consider the potential long-term advantages to justify the initial costs.
 Data Security and Privacy: The collecting of enormous volumes of data, especially high-resolution photography
of agricultural landscapes, raises concerns over the data's privacy and the protection of their confidentiality
(Vadiyala & Baddam, 2017). Farmers are responsible for safeguarding the safety of the data stored and transferred
after being acquired by drones. In addition, there is a requirement for clear standards and legislation to handle the
privacy concerns of persons who live near farmlands that are scanned by drones.
 Skill and Training Requirements: A considerable amount of technical knowledge is required to operate and
maintain agricultural drones successfully. Farmers might need training to use drones safely, understand the data
they collect, and solve any technological problems that arise. To keep up with technological breakthroughs and
make the most of the opportunities presented by these instruments, farmers need to continue their education if
they want to incorporate drone technology into their existing farming operations (Surarapu, 2017).

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 Limited Endurance and Range: The technology behind drone batteries limits how long an agricultural drone can
stay in the air and how far it can travel (Mahadasa, 2016). Drones may need to make numerous trips back to a
charging station, restricting their ability to cover more considerable agricultural expanses. To overcome these
restrictions, it will either be necessary to create more power-efficient batteries or to put in infrastructure, such as
docking stations for drones, which can increase their operational range.
 Weather Sensitivity: Both the efficiency and the safety of agricultural drones can be negatively impacted by
adverse weather conditions such as high winds, heavy rain, or extremely high temperatures. The unpredictability
of the weather might reduce the dependability of drone operations, which is especially problematic at crucial times
like planting and harvesting (Fadziso et al., 2019). Considerations as important as developing weather-resistant
drones and implementing backup procedures in unfavorable conditions include.
 Integration with Existing Farming Practices: Planning is essential to ensuring that agricultural drones may be
successfully incorporated into already established farming processes. Farmers need to consider how drones will
fit into their overall workflow to ensure that they will integrate smoothly with traditional farming practices and
other precision agriculture technologies. The ability to overcome opposition to change and cultivate a culture open
to adopting new technologies are two essential components of successful integration.
 Standardization and Interoperability: Challenges arise due to the need for established protocols and interoperability
across various drone platforms, sensors, and data analysis tools. Realizing smooth communication and compatibility
among multiple technologies is vital to developing a cohesive and interoperable ecosystem (Mahadasa, 2017). Efforts
made across a whole industry to set standards can make integration and collaboration more seamless.
 Public Perception and Acceptance: The public's reaction to using drones in agricultural settings might range from
worries about invasions of privacy to concerns about the spread of surveillance technology. Raising awareness
and encouraging community engagement is necessary to win over the public. Transparent communication about
the advantages of using drone technology in agriculture and doing so ethically is essential for reducing worries.
 Environmental Impact: Drones used in agriculture have the potential to contribute to more precise farming, but
there are concerns about the influence they could have on the environment. The production and disposal of
individual drone parts, in addition to the amount of energy needed for their operation, are all factors that contribute
to the overall environmental footprint (Surarapu, 2016). It is necessary to use green responsible methods while
designing drones, maximizing energy efficiency, and properly disposing of drones at the end of their useful lives.
Although agricultural drones have enormous potential for transforming farming methods, addressing these issues and
considerations is vital to realize their full benefits (Vadiyala, 2017). Integrating drone technology into agricultural
settings must be done responsibly and efficiently. This can only be achieved via the concerted efforts of all relevant
stakeholders, including farmers, regulators, technology developers, and the general public.
FUTURE TRENDS AND INNOVATIONS
The future of agricultural drone technology promises exciting innovations that will revolutionize farming. Future
agricultural drone trends and advancements are covered here.
 Swarming Technology: Swarming techniques may become common in agricultural drones. Cooperative drone
swarms can cover huge areas faster, speeding up data collection and analysis (Mahadasa & Surarapu, 2016). This
technology could streamline and improve crop monitoring, pollination, and pest control.
 Artificial Intelligence for Autonomous Decision-Making: AI will be crucial to agricultural drones' autonomous
decision-making. AI algorithms will improve real-time data analysis, allowing drones to make independent
resource allocation, intervention, and farm management decisions.
 Advanced Imaging Techniques: Advanced imaging techniques like hyperspectral and multispectral imaging are
coming. These technologies will enable unparalleled early identification of agricultural stress, pathogens, and
nutritional deficits. Improved imaging will improve precision agriculture.
 Beyond Visual Line of Sight (BVLOS) Operations: Future legislation may allow agricultural drones to operate
BVLOS. BVLOS would enable drones to reach isolated regions and traverse longer distances, helping farmers
monitor enormous fields. This growth could promote precision agriculture in varied terrains.
 Energy Harvesting and Extended Endurance: Energy-gathering technology like solar-powered drones may solve
flight endurance issues. Energy-harvesting drones could work longer without recharging, improving their
efficiency in large-scale agriculture.

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 Blockchain for Traceability: Agricultural drone systems may use blockchain technology for traceability. This
innovation might protect and transparently track crop production data from planting to delivery. Blockchain
would ensure drone data security and authenticity via an immutable, decentralized ledger (Paoletti & Pimentel,
2000).
 Automated Precision Application Systems: Future drones may have automated precision application systems.
These systems might automatically administer fertilizers, herbicides, and other inputs based on real-time data.
Automated precision application would improve resource use and reduce agricultural environmental impact.
 Interconnected Ecosystems: Agricultural drones may become part of networked ecosystems. Drones, sensors, and
other agricultural technologies will create a seamless network. Effective data sharing, collaborative decision-
making, and holistic farm management will result from this integration.
 Enhanced Durability and Weather Resistance: Future agricultural drones may be more durable and
weatherproof. This enhancement would allow drones to function dependably in lousy weather, assuring constant
performance throughout planting and harvesting.
 Increased Affordability and Accessibility: Manufacturing improvements and economies of scale should lower
agricultural drone prices. Precision agriculture will be more widely adopted and democratized as this technology
becomes more affordable.
The future of agricultural drone technology is bright, with a convergence of innovations that will change farming.
These trends and improvements will improve agricultural efficiency, sustainability, and scalability, making the farm
industry more resilient and technologically advanced.
CONCLUSION
Finally, adopting agricultural drone technology is a turning point in agriculture. From military use to precision
agriculture, drones are essential for farmers striving to maximize productivity, save resources, and practice
sustainability. Stakeholder collaboration addresses regulatory compliance, early investment costs, and data security
problems. As technology improves, swarming technologies, enhanced imagery, and AI-driven autonomous decision-
making are coming. These advancements and possible energy harvesting and endurance breakthroughs envision a
farming environment where drones function smoothly across interconnected ecosystems. Drones could democratize
precision agriculture for farmers of all sizes as they become cheaper and more accessible. The future of farming is
being reshaped by data-driven decision-making, sustainability, and efficiency through agricultural drone technology.
With continuing innovation and careful evaluation of issues, agricultural drones will help feed our growing global
population while reducing environmental effects.
REFERENCES
Baddam, P. R. (2017). Pushing the Boundaries: Advanced Game Development in Unity. International Journal of
Reciprocal Symmetry and Theoretical Physics, 4, 29-37. https://upright.pub/index.php/ijrstp/article/view/109
Baddam, P. R., & Kaluvakuri, S. (2016). The Power and Legacy of C Programming: A Deep Dive into the
Language. Technology & Management Review, 1, 1-13. https://upright.pub/index.php/tmr/article/view/107
Baddam, P. R., Vadiyala, V. R., & Thaduri, U. R. (2018). Unraveling Java’s Prowess and Adaptable Architecture in
Modern Software Development. Global Disclosure of Economics and Business, 7(2), 97-
108. https://doi.org/10.18034/gdeb.v7i2.710
Dekkati, S., & Thaduri, U. R. (2017). Innovative Method for the Prediction of Software Defects Based on Class
Imbalance Datasets. Technology & Management Review, 2, 1–5.
https://upright.pub/index.php/tmr/article/view/78
Dekkati, S., Lal, K., & Desamsetti, H. (2019). React Native for Android: Cross-Platform Mobile Application
Development. Global Disclosure of Economics and Business, 8(2), 153-164. https://doi.org/10.18034/gdeb.v8i2.696
Dekkati, S., Thaduri, U. R., & Lal, K. (2016). Business Value of Digitization: Curse or Blessing?. Global Disclosure of
Economics and Business, 5(2), 133-138. https://doi.org/10.18034/gdeb.v5i2.702
Deming, C., Baddam, P. R., & Vadiyala, V. R. (2018). Unlocking PHP’s Potential: An All-Inclusive Approach to Server-
Side Scripting. Engineering International, 6(2), 169–186. https://doi.org/10.18034/ei.v6i2.683
Deming, C., Dekkati, S., & Desamsetti, H. (2018). Exploratory Data Analysis and Visualization for Business
Analytics. Asian Journal of Applied Science and Engineering, 7(1), 93–100. https://doi.org/10.18034/ajase.v7i1.53

Volume 7, No 2/2020 ISSN 2313-0008 (Print); ISSN 2313-0016 (Online)
Copyright ©
CC-BY-NC
, i-Proclaim |
MJMBR
Page 143
Fadziso, T., Vadiyala, V. R., & Baddam, P. R. (2019). Advanced Java Wizardry: Delving into Cutting-Edge Concepts
for Scalable and Secure Coding. Engineering International, 7(2), 127–146. https://doi.org/10.18034/ei.v7i2.684
Hosen, M. S., Ahmmed, S., & Dekkati, S. (2019). Mastering 3D Modeling in Blender: From Novice to Pro. ABC Research
Alert, 7(3), 169–180. https://doi.org/10.18034/ra.v7i3.654
Kaluvakuri, S., & Vadiyala, V. R. (2016). Harnessing the Potential of CSS: An Exhaustive Reference for Web
Styling. Engineering International, 4(2), 95–110. https://doi.org/10.18034/ei.v4i2.682
Kosutic, S., De Wrachien, D. (2012). A Report on the 40th International Symposium Actual Tasks on Agricultural
Engineering, 21-24 February 2012, Opatija, Croatia. Journal of Agricultural Engineering, 43(1).
https://doi.org/10.4081/jae.2012.notice2
Mahadasa, R. (2016). Blockchain Integration in Cloud Computing: A Promising Approach for Data Integrity and
Trust. Technology & Management Review, 1, 14-20. https://upright.pub/index.php/tmr/article/view/113
Mahadasa, R. (2017). Decoding the Future: Artificial Intelligence in Healthcare. Malaysian Journal of Medical and
Biological Research, 4(2), 167-174. https://mjmbr.my/index.php/mjmbr/article/view/683
Mahadasa, R., & Surarapu, P. (2016). Toward Green Clouds: Sustainable Practices and Energy-Efficient Solutions in
Cloud Computing. Asia Pacific Journal of Energy and Environment, 3(2), 83-
88. https://doi.org/10.18034/apjee.v3i2.713
Mandapuram, M., Mahadasa, R., & Surarapu, P. (2019). Evolution of Smart Farming: Integrating IoT and AI in
Agricultural Engineering. Global Disclosure of Economics and Business, 8(2), 165-
178. https://doi.org/10.18034/gdeb.v8i2.714
Paoletti, M. G., Pimentel, D. (2000). Environmental Risks of Pesticides versus Genetic Engineering for Agricultural
Pest Control. Journal of Agricultural and Environmental Ethics, 12(3), 279-303.
https://doi.org/10.1023/A:1009571131089
Patrick, K., Thomson, S., Rein, B. (2019). Estimating the Number of Agricultural Fatal Injuries Prevented by
Agricultural Engineering Developments in the United States. Safety,
5(4), 63. https://doi.org/10.3390/safety5040063
Shaporova, Z. E., Tsvettsykh, A. V. (2019). Model of the Agricultural Engineering Enterprise Innovation Program
Development. IOP Conference Series. Materials Science and Engineering, 537(4). https://doi.org/10.1088/1757-
899X/537/4/042063
Soliva, M., Bernat, C., Gil, E., Martínez, X., Pujol, M. (2007). Education and Research Related to Organic Waste
Management at Agricultural Engineering Schools. International Journal of Sustainability in Higher Education,
8(2), 224-233. https://doi.org/10.1108/14676370710726670
Surarapu, P. (2016). Emerging Trends in Smart Grid Technologies: An Overview of Future Power Systems. International
Journal of Reciprocal Symmetry and Theoretical Physics, 3, 17-
24. https://upright.pub/index.php/ijrstp/article/view/114
Surarapu, P. (2017). Security Matters: Safeguarding Java Applications in an Era of Increasing Cyber Threats. Asian
Journal of Applied Science and Engineering, 6(1), 169–176. https://doi.org/10.18034/ajase.v6i1.82
Surarapu, P., & Mahadasa, R. (2017). Enhancing Web Development through the Utilization of Cutting-Edge
HTML5. Technology & Management Review, 2, 25-36. https://upright.pub/index.php/tmr/article/view/115
Surarapu, P., Mahadasa, R., & Dekkati, S. (2018). Examination of Nascent Technologies in E-Accounting: A Study on
the Prospective Trajectory of Accounting. Asian Accounting and Auditing Advancement, 9(1), 89–100.
https://4ajournal.com/article/view/83
Thaduri, U. R., Ballamudi, V. K. R., Dekkati, S., & Mandapuram, M. (2016). Making the Cloud Adoption Decisions:
Gaining Advantages from Taking an Integrated Approach. International Journal of Reciprocal Symmetry and
Theoretical Physics, 3, 11–16. https://upright.pub/index.php/ijrstp/article/view/77
Ting, K. C. (2010). Development and Perspectives of Agricultural Engineering Towards Biological/Biosystems
Engineering. Journal of Agricultural Engineering, 41(1), 1-5. https://doi.org/10.4081/jae.2010.1.1
Vadiyala, V. R. (2017). Essential Pillars of Software Engineering: A Comprehensive Exploration of Fundamental
Concepts. ABC Research Alert, 5(3), 56–66. https://doi.org/10.18034/ra.v5i3.655

Mahadasa
et al.
: Utilization of Agricultural Drones in Farming by Harnessing the Power of Aerial Intelligence (135-144)
Page 144 Malaysian Journal of Medical and Biological Research ● Volume 7, No 2/2020
Vadiyala, V. R., & Baddam, P. R. (2017). Mastering JavaScript’s Full Potential to Become a Web Development
Giant. Technology & Management Review, 2, 13-24. https://upright.pub/index.php/tmr/article/view/108
Vadiyala, V. R., & Baddam, P. R. (2018). Exploring the Symbiosis: Dynamic Programming and its Relationship with
Data Structures. Asian Journal of Applied Science and Engineering, 7(1), 101–112.
https://doi.org/10.18034/ajase.v7i1.81
Vadiyala, V. R., Baddam, P. R., & Kaluvakuri, S. (2016). Demystifying Google Cloud: A Comprehensive Review of
Cloud Computing Services. Asian Journal of Applied Science and Engineering, 5(1), 207–218.
https://doi.org/10.18034/ajase.v5i1.80
Velloso, N. S., Costa, G. A. L., Magalhães, R. R., Santos, F. L., de Andrade, E. T. (2018). The Finite Element Method
Applied to Agricultural Engineering: A review. Current Agriculture Research Journal, 6(3), 286-299.
https://doi.org/10.12944/CARJ.6.3.08
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