Assyfa Journal of Farming and Agriculture, vol. 1(2), pp. 54 - 65, 2024
Received 20 February 2024 / published 17 May 2024
ISSN: 3062-8393

Future Technology: AI and Drones for
Sustainable Pest Control in Saudi
Arabia and Indonesia
Silvia Intan Puspitasari 1 , Miftachul Amin 2 , and Anouar Ben Mabrouk 3
1. State Polytechnic of Malang, Indonesia
2. PC PERGUNU Kota Pasuruan, Indonesia
3. University of Kairouan-Monastir Tunisia, Tunisia
E-mail correspondence: silviaintan29@gmail.com

Abstract
Sustainable agriculture faces significant challenges from increasing pest
threats, especially in regions such as Saudi Arabia and Indonesia. This
article examines innovative strategies in pest control through the
integration of advanced technologies, namely artificial intelligence (AI)
and drones, to reduce dependence on chemical pesticides and
strengthen crop resilience. This study aims to identify and analyze
trends and effectiveness of pest control using technological approaches
from 2020 to 2025. The method used in this study is a Systematic
Literature Review (SLR) that collects and evaluates peer-reviewed
studies related to pest control, AI, and drones. Data were analyzed using
thematic analysis techniques to reveal in-depth information about the
application and results of the integration of these technologies. The
results of the analysis show that the use of AI and drones in pest control
is a good thing, with some studies showing a reduction in chemical use
of up to 40% and an increase in crop yields of up to 20%. AI technology
allows for more accurate pest detection and prediction of pest attacks,
while drones effectively distribute biological control agents and monitor
fields in real time. In conclusion, the integration of AI and drone
technology improves the effectiveness of pest control and contributes
to the development of more environmentally friendly and sustainable
agriculture. The application of this technology is expected to be a model
for other countries facing similar pest control challenges.
Keywords: Pest Control, Artificial Intelligence, Drones, Sustainable
Agriculture, Significant challenges

Introduction
Sustainable agriculture is one of the important pillars in
ensuring global food security. However, pest infestation is a
significant challenge that must be overcome to achieve this goal. In
regions such as Saudi Arabia and Indonesia, pests have caused
substantial declines in crop yields, which have a direct impact on
the economic and social well-being of local communities. The
urgency of this research lies in the urgent need to find solutions that
reduce dependence on chemical pesticides that are known to have
negative impacts on the environment and human health (Sharma
et al., 2021).

Previous studies have shown that excessive use of chemical
pesticides can lead to soil degradation, pest resistance, and
environmental pollution (Kumar & Pimental, 2020). Furthermore,
reliance on these conventional methods is often ineffective in the
long run, prompting the need for more environmentally friendly,
innovative approaches. A gap in existing research is the lack of focus
on the application of advanced technologies such as artificial
intelligence (AI) and drones in the context of pest control in
developing countries, including Saudi Arabia and Indonesia (Jensen
et al., 2019).
Research related to the application of technology in pest
control has been widely conducted by various experts in the last
five years. For example, studies by Smith et al. (2020) and Johnson
et al. (2021) explore the use of AI in analyzing pest attack patterns
and providing more accurate treatment recommendations.
Meanwhile, studies by Lee et al. (2020) and Chen et al. (2021)
highlight the effectiveness of drones in distributing biological
control agents in the field. In addition, Brown et al. (2022) and Kim
et al. (2023) examine the integration of these two technologies in
the context of sustainable agriculture, which shows a reduction in
the use of chemical pesticides and a significant increase in crop
yields. However, these studies generally focus on developed
countries with established technological infrastructure.
However, in-depth studies on the application of this technology
in developing countries, such as Saudi Arabia and Indonesia, are still
very limited. This study aims to fill this gap by highlighting the
unique adaptations and challenges faced in implementing AI and
drone technology in regions with highly diverse agricultural
conditions. This article emphasizes the advantages of this
technology in detecting and predicting pest attacks in real-time, as
well as the ability of drones in the efficient distribution of control
agents. Thus, this study is expected to serve as a guideline for other
countries facing similar challenges, thereby encouraging the
transformation of agriculture towards more sustainable and
environmentally friendly practices.

© 2024 Puspitasari al., (s). This is a Creative Commons License. This work is licensed under a Creative Commons AttributionNonCommertial 4.0 International License.

Silvia Intan Puspitasari, Miftachul Amin and Anouar Ben Mabrouk, Future technology: AI and Drone for sustainable ...Assyfa
Journal of Farming and Agriculture, 1 (1), 08-13, 2023
, 1 (1), 29-34, 2023

The application of AI in agriculture, especially in pest control,
offers novelty by providing more accurate detection and prediction
capabilities. Through machine learning algorithms, AI can analyze
weather data, soil conditions, and pest attack patterns to provide
timely action recommendations (Li et al., 2022). Drones, on the
other hand, offer innovative solutions in the distribution of
biological control agents and real-time field monitoring, which were
previously not possible with high efficiency (Smith & Green, 2021).

these pesticides can lead to immediate reductions in pest numbers,
which is essential for protecting crops and ensuring high yields.
However, reliance on these substances carries significant
drawbacks. The widespread use of chemical pesticides has been
linked to serious environmental problems, including soil
degradation and water pollution. Jensen et al. (2019) highlight that
these chemicals can leach into water bodies, leading to
contamination of drinking water and damage to aquatic
ecosystems. Furthermore, the health risks associated with pesticide
exposure have raised concerns among farm workers and
consumers, leading to calls for safer alternatives.

According to research by Zhang et al. (2021), the integration of
AI and drones has been shown to reduce the use of chemical
pesticides by up to 40% and increase crop yields by 20% in several
test areas. This shows the great potential of this technology to be
applied on a wider scale. Another innovation offered is a holistic
approach to pest control that does not only focus on technical
aspects, but also takes into account ecological and socio-economic
variables, which are often overlooked in previous studies (Anderson
et al., 2020).

Additionally, the overuse of chemical pesticides has resulted in
a disturbing trend of pest resistance. Anderson et al. (2020) noted
that as pests are repeatedly exposed to these substances, they can
adapt and develop resistance, making chemical control methods
less effective over time. This cycle not only complicates pest
management but also leads to greater reliance on increasingly
potent chemicals, exacerbating environmental and health
concerns. Given these challenges, there is an urgent need for
innovative approaches to pest management. Emerging
technologies, such as artificial intelligence (AI) and drone
technology, offer promising alternatives. These tools can improve
pest monitoring and management, enabling farmers to adopt more
targeted and sustainable practices. By integrating these advanced
technologies into agricultural systems, the negative impacts of
chemical pesticides can be mitigated while maintaining effective
pest control, leading to healthier ecosystems and a safer food
supply.

In addition, this study adopted the Systematic Literature
Review (SLR) method to collect and evaluate peer-reviewed studies
related to pest control, AI, and drones. This method allows for a
more systematic and comprehensive filtering of information,
resulting in a more in-depth analysis of the applications and
outcomes of the integration of these technologies (Tranfield et al.,
2003). The findings of this study are expected to serve as a guideline
for policy makers and agricultural practitioners in implementing
more sustainable and effective pest control strategies.
In the context of Saudi Arabia and Indonesia, where climate and
agricultural conditions vary widely, the implementation of this
technology requires careful adjustment and adaptation. Issues such
as limited technological infrastructure, high initial costs, and lack of
technical skills among farmers are barriers to successful
implementation (Nurmi et al., 2021). However, the potential longterm benefits of this technology in supporting sustainable
agriculture make it a worthy investment.

2.2 Technological Innovation in Pest Control
2.2.1

Artificial Intelligence in Pest Control

The application of AI in agriculture, particularly in pest control,
has gained attention in recent years. AI technology facilitates realtime data analysis, which enables accurate pest detection and
prediction. As shown by Smith et al. (2020), AI systems can analyze
large datasets, including weather patterns and soil conditions, to
identify potential pest outbreaks before they occur. This predictive
capability significantly reduces the need for chemical pesticides by
enabling targeted interventions.

Thus, this study not only contributes to the scientific literature
on technology-based pest control, but also provides practical
insights that can be implemented in the field. It is hoped that the
results of this study can serve as a model for other countries facing
similar challenges, encouraging agricultural transformation
towards more sustainable and environmentally friendly practices.

Advantages: AI’s ability to provide timely and accurate
recommendations helps minimize crop losses and reduce chemical
use (Li et al., 2022). Integration of machine learning algorithms
offers a dynamic approach to pest management that adapts to
changing environmental conditions (Johnson et al., 2021).

Literature Review
2.1 Overview of Pest Control Challenges in Saudi Arabia and
Indonesia

Disadvantages: Despite its many advantages, AI adoption is
often hampered by high initial costs and the need for technical
expertise, which can be a barrier in developing regions (Nurmi et
al., 2021). In addition, there is a risk of technology dependency,
where farmers may rely solely on AI recommendations without
understanding the underlying farming processes (Brown et al.,
2022).

Saudi Arabia and Indonesia face unique challenges in pest
control due to differences in climate, agricultural practices, and
economic conditions. According to Al-Turki et al. (2020), Saudi
Arabia’s dry climate and reliance on imported food increase the
vulnerability of its agricultural sector to pest attacks. In contrast,
Indonesia, with its tropical climate, faces a different set of
challenges, including high humidity and diverse pest species
(Prasetyo et al., 2021). Both countries have traditionally relied on
chemical pesticides, which, although effective in the short term,
pose long-term risks such as environmental degradation and pest
resistance (Kumar & Pimental, 2020).

2.2.2

Drone Technology in Pest Control

Drones have emerged as a versatile tool in modern agriculture,
offering unparalleled access to crop fields. Studies by Lee et al.
(2020) and Chen et al. (2021) highlight the effectiveness of drones
in distributing biological control agents and monitoring large areas
of land. These capabilities allow for more efficient pest
management and resource allocation. Drones provide a costeffective solution for pest surveillance and control, enabling precise
application of treatments and reducing labor costs (Smith & Green,
2021). Their ability to cover large areas quickly is particularly
beneficial in large agricultural settings (Zhang et al., 2021).
However, drones require significant investment in equipment and
maintenance. There are also regulatory constraints and privacy
concerns regarding their operation, which may limit their use in
some areas (Kim et al., 2023).

Advantages and Disadvantages
Previous studies have highlighted the effectiveness of chemical
pesticides in rapidly reducing pest populations (Sharma et al.,
2021). However, excessive use of pesticides leads to soil
degradation, water pollution, and health risks (Jensen et al., 2019).
Furthermore, pests can develop resistance, making this solution
unsustainable (Anderson et al., 2020). This underscores the need
for innovative approaches such as AI and drone technology.
The use of chemical pesticides has long been a staple in
agricultural practices due to their effectiveness in rapidly
controlling pest populations. According to Sharma et al. (2021),

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, 1 (1), 29-34, 2023

2.3 Empirical Evidence of AI and Drone Integration

SLR was conducted by following the stages of literature search,
screening, analysis, and data synthesis. This process was designed
to ensure that all information relevant to the research topic was
effectively integrated. This study also adopted a thematic approach
in data analysis to identify trends and patterns emerging from
previous studies.

The integration of AI and drones in pest control has shown
promising results in pilot studies. Zhang et al. (2021) reported a 40%
reduction in chemical pesticide use and a 20% increase in crop
yields in a test area utilizing this technology. This empirical evidence
supports the hypothesis that combining AI and drones can lead to
more sustainable agricultural practices. The synergistic use of AI
and drones enables holistic pest management that takes into
account ecological and socio-economic factors, which are often
overlooked in traditional methods (Anderson et al., 2020). This
approach not only increases the efficiency of pest control but also
supports environmental conservation efforts. Despite these
successes, scalability of such systems in developing countries
remains a challenge. Infrastructure limitations, high costs, and the
need for skilled personnel are significant barriers that require
strategic planning and investment (Nurmi et al., 2021).

3.2 Research Design
3.2.1

Research Stages

Research Questions and Data Collection
To explore the effectiveness of AI and drones in pest control in Saudi
Arabia and Indonesia, the first step involves identifying a central
research question. This inquiry focuses on evaluating the impact and
success rate of these technologies in managing pests within the
agricultural sectors of these countries. Following this, data collection
becomes crucial. Researchers should gather peer-reviewed journal
articles from reputable databases such as Scopus, Web of Science,
and ScienceDirect. The inclusion criteria are strict, focusing on
studies published between 2020 and 2025 that specifically address
the use of AI and drones in pest control. This approach ensures that
the gathered data is both recent and relevant, providing a robust
foundation for subsequent analysis.

2.4 Implications for Sustainable Agriculture
The transition to advanced technologies such as AI and drones
in pest control presents transformative opportunities for
sustainable agriculture in Saudi Arabia and Indonesia. As Tranfield
et al. (2003) noted, a systematic literature review approach helps
to synthesize existing research to provide clear insights into the
practical applications of these technologies.

Data Screening and Analysis

Advantages and Disadvantages

Once the data is collected, the next step involves meticulous data
screening. This process uses a set of inclusion and exclusion criteria
to filter out irrelevant articles, ensuring only pertinent studies are
considered. Articles that do not meet these criteria or fail to address
the research question are disregarded. Following this, a thematic
analysis is conducted on the selected literature. This analysis aims to
uncover key themes and trends, providing insights into how AI and
drones have been utilized in pest control. By focusing on recurring
patterns and findings, researchers can better understand the current
landscape and effectiveness of these technologies in the specified
regions.

By reducing reliance on chemical pesticides, AI and drones
contribute to healthier ecosystems and long-term agricultural
productivity (Johnson et al., 2021). However, achieving widespread
adoption requires overcoming technological and economic
barriers, as well as encouraging collaboration between
stakeholders (Prasetyo et al., 2021).
In summary, while the integration of AI and drone technology
in pest control offers significant benefits (Kopalle et al., 2022), it is
not without challenges (Bankins & Formosa, 2023; Qazi et al.,
2022). The literature suggests that these technologies can improve
pest management practices, reduce environmental impacts, and
enhance crop resilience. However, successful implementation
depends on addressing infrastructure, economic, and educational
barriers. This study aims to fill this gap by providing a detailed
analysis of the technology’s application in Saudi Arabia and
Indonesia, offering potential models for other regions facing similar
challenges.

Data Synthesis and Recommendations
The final phase involves synthesizing the gathered data to draw
meaningful conclusions and propose recommendations. By
integrating information from various sources, researchers can
develop a comprehensive understanding of the role AI and drones
play in pest control in Saudi Arabia and Indonesia. This synthesis not
only highlights the benefits and limitations of these technologies but
also identifies potential areas for improvement and further research.
The ultimate goal is to offer well-informed recommendations that
can guide future developments and applications, ensuring that AI
and drones are utilized to their fullest potential in enhancing pest
management strategies and supporting sustainable agricultural
practices.

Penel Method
3.1 Research Paradigm
The research paradigm used in this study is a qualitative
approach with the Systematic Literature Review (SLR) method. This
approach was chosen because of its ability to provide an in-depth
and comprehensive analysis of the application of AI and drone
technology in pest control. According to Tranfield et al. (2003), SLR
allows researchers to filter and evaluate information from various
published studies, resulting in a more structured and systematic
synthesis.

3.2.2

Research Instruments

The main instrument in this study was the SLR protocol which
included inclusion and exclusion criteria, search tools, and analysis
methods. This protocol was designed to ensure transparency and
replication of the study.

Table 1: Research Instruments and Indicators
Instrument
Indicator
Description
SLR
Protocol

Inclusion/Exclusion
Criteria

Search
Tools

Database

Analysis
Method

Thematic
Approach

56

Determine relevant
articles based on
topic,
year
of
publication,
and
type of study.
Scopus, Web of
Science,
ScienceDirect
Identifying major
themes in literature

3.2.3

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Data Validation

1.
2.
3.

Data validation was conducted through source triangulation,
where data obtained from the literature was compared with
findings from other studies to ensure consistency and reliability.

3.4 Data analysis

3.3 Data collection

Data analysis was conducted using a thematic approach, where
data from various studies were coded and categorized based on
relevant themes. This analysis aims to identify trends and patterns
in the use of AI and drones in pest control.

Data collection was conducted systematically using the SLR
protocol. Articles collected must meet the inclusion criteria,
namely:
Stages
Codification
Categorization
Interpretation

Table 2: Data Analysis Stages
Description
Identify and code emerging themes in the
data
Grouping codes into broader categories
Summarize trends and patterns that
emerge from the data

3.5 Data Synthesis

has used machine learning algorithms to process diverse data,
including weather information, soil conditions, and pest attack
patterns. This analysis allows the AI system to identify potential
pest attacks before they occur, giving farmers enough time to plan
preventive measures. With the right prediction capabilities, farmers
can reduce their dependence on chemical pesticides that often
have negative impacts on the environment and human health.

Data synthesis was conducted by integrating findings from
multiple studies to provide a holistic view of the effectiveness of AI
and drones in pest control. The synthesis also took into account
ecological and socio-economic factors relevant to the research
context. Conclusions and recommendations are based on data
synthesis, which provides guidance for policy makers and
agricultural practitioners in applying AI and drone technologies for
more sustainable pest control. With this approach, this research is
expected to provide significant contributions to scientific literature
and agricultural practices, especially in the context of developing
countries such as Saudi Arabia and Indonesia.

Furthermore, AI provides benefits not only in terms of
predictions, but also in providing more specific and accurate
recommendations for action. Machine learning algorithms are able
to process large amounts of data in real-time, generating insights
that help farmers determine the most effective preventive
measures. For example, based on data analysis conducted by AI,
farmers can be directed to use more environmentally friendly pest
control methods, such as biological control agents or planting
barrier crops. Thus, the integration of AI in this process not only
improves the efficiency of pest control but also plays a role in
supporting more sustainable and environmentally friendly
agricultural practices.

Results and Discussion
4.1 Application of AI Technology for Pest Control
4.1.1

Published between 2020 and 2025.
Focusing on the use of AI and drones in pest control.
Published in a peer-reviewed journal.

Pest Detection and Prediction

The application of AI in agriculture, especially for pest control,
has proven its superiority in detecting and predicting pest attacks
with a higher level of accuracy. In previous studies, AI technology

Table 3: Improving the Efficiency of Pest Detection and Prediction with AI
Studies
Year
Detection
Pesticide
Effectiveness
Reduction
Li et al.
2022
95%
40%
Johnson
et al.

2021

90%

35%

software, can enhance the accuracy and speed of pest detection.
By reducing the reliance on manual inspections, AI allows for more
efficient monitoring of crop health, leading to better-informed
decisions regarding pest control. Furthermore, Johnson et al.
reported a 35% reduction in pesticide application, highlighting how
AI can contribute to more judicious use of agrochemicals, thereby
promoting eco-friendly farming practices.

The study by Li et al. in 2022 highlights significant
advancements in the field of pest detection and prediction through
the use of artificial intelligence (AI). Their research demonstrated a
remarkable detection effectiveness rate of 95%, showcasing the
potential of AI to accurately identify pest infestations in various
agricultural environments. This high level of precision is crucial for
timely interventions, helping farmers to implement effective pest
control measures before infestations can cause substantial damage
to crops. The study further reveals that employing AI in pest
detection can lead to a significant reduction in pesticide usage, with
a reported decrease of 40%. This reduction not only benefits the
environment by minimizing chemical use but also contributes to the
sustainability of agricultural practices.

Both studies illustrate the transformative impact AI can have on
pest management strategies. By enhancing detection effectiveness
and reducing the need for chemical interventions, AI offers a
promising solution to some of the challenges faced by the
agricultural sector. The integration of AI into pest detection not only
helps in safeguarding crop yields but also supports environmental
conservation efforts by decreasing the dependency on harmful
pesticides. As research in this area continues to evolve, there is
potential for even greater improvements in both the efficiency and
sustainability of pest management, ultimately contributing to a
more resilient and environmentally conscious agricultural industry.

In 2021, Johnson et al. conducted a similar study that also
underscored the value of AI in pest management. Their findings
indicated a slightly lower detection effectiveness rate of 90%,
though this figure still represents a substantial improvement over
traditional methods. The study emphasized how AI technologies,
such as machine learning algorithms and image recognition

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Silvia Intan Puspitasari, Miftachul Amin and Anouar Ben Mabrouk, Future technology: AI and Drone for sustainable ...Assyfa
Journal of Farming and Agriculture, 1 (1), 08-13, 2023
, 1 (1), 29-34, 2023

Figure 1: Flowchart of the use of AI in pest detection and prediction, showing the stages from data collection to action recommendations

The flowchart in Figure 1 through the use of AI in pest detection
and prediction shows the stages from data collection to action
recommendations. The use of artificial intelligence (AI) in pest
detection and prediction has become one of the important
innovations in modern agriculture. This process begins with the
collection of data from various sources, such as satellite imagery,
soil sensors, and field reports made by farmers. This data is then
analyzed using machine learning algorithms to identify patterns and
trends related to pest attacks. For example, research by Kamilaris
and Prenafeta-Boldú (2018) showed that the use of AI can increase
the accuracy of pest prediction by up to 85% compared to
traditional methods. By using techniques such as pattern
recognition and big data analysis, farmers can gain deeper insights
into when and where pests are likely to appear, so they can take
preventive action before the problem becomes more severe.

AI which requires a stable and fast data connection. Research by
Nurmi et al. (2021) shows that limited technological infrastructure
is one of the main obstacles to the adoption of new technologies in
the agricultural sector. Therefore, increasing access to technology
and the internet is a fundamental step that must be taken to
support the application of AI in pest control.
In addition, the need for training for farmers is also a significant
challenge. According to Johnson et al. (2021), continuous training is
essential to improve the technical skills of farmers, especially in less
developed areas. Structured and inclusive training programs are
needed so that farmers can understand and utilize AI technology
effectively.
Potential Benefits
Despite these challenges, the potential benefits of AI
applications are enormous. The use of AI can improve the efficiency
of agricultural production by providing more accurate and timely
recommendations for pest control. AI can analyze weather data,
soil conditions, and pest attack patterns to provide more precise
solutions, reducing reliance on chemical pesticides that have
negative impacts on the environment.

After analyzing the data, the AI system provides specific
recommendations for actions to address the pest problem. These
recommendations could be the use of specific pesticides, planting
barrier crops, or implementing integrated pest management
methods. In a study by Yang et al. (2020), it was found that
implementing an AI-based system in pest management not only
reduced crop losses but also minimized the use of hazardous
chemicals, which had a positive impact on the environment. This
approach also helped farmers save time and resources, thereby
increasing the overall efficiency of agricultural production. Thus,
the integration of AI in pest detection and prediction is a significant
step forward in the efforts to improve food security and sustainable
agricultural practices.
4.1.2

Research by Li et al. (2022) shows that AI can reduce the use of
chemical pesticides by up to 40% and increase crop yields by 20%
in several test areas. This shows that with proper implementation,
AI technology can have a significant positive impact on the
environment and agricultural economy in Saudi Arabia and
Indonesia.
Social and Economic Implications

Implementation in Saudi Arabia and Indonesia

The implementation of AI also has the potential to improve
farmers' welfare by reducing production costs and increasing crop
yields. Thus, AI not only supports environmental sustainability but
also provides equitable economic benefits to rural communities.

The implementation of AI technology in Saudi Arabia and
Indonesia faces unique challenges, such as limited technological
infrastructure and the need for training for farmers. However, the
potential benefits are enormous, with AI being able to increase the
efficiency of agricultural production and reduce negative
environmental impacts. Several previous studies provide insights
into the challenges and opportunities in this context.

In order to achieve these potential benefits, strong policy
support from governments, as well as collaboration between the
public and private sectors, is needed to accelerate the adoption of
AI technologies in agricultural practices. These initiatives should
also include training and education programs designed to improve
the technical capacity of farmers so that they can fully exploit the
benefits of these technologies.

Infrastructure and Training Challenges
One of the main challenges is the limited technological
infrastructure that is still an obstacle in both countries. Limited
internet and technology access in rural areas can hinder the use of

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Journal of Farming and Agriculture, 1 (1), 08-13, 2023
, 1 (1), 29-34, 2023

Overall, despite the challenges that need to be overcome, the
implementation of AI in pest control in Saudi Arabia and Indonesia
offers a promising solution for sustainable agriculture, with the
potential to significantly increase production efficiency and reduce
negative environmental impacts.

drones an economically and ethically appealing option for modern
agriculture.
Empirical Support and Future Prospects
The advantages of drone distribution are supported by a growing
body of empirical research that underscores its efficacy and
sustainability. Studies by Smith and Green (2021), Lee et al. (2020),
and Chen et al. (2021) provide evidence of the positive impacts
drones have on agricultural practices, reinforcing their role as a
transformative technology in pest management. As drone
technology continues to advance, it is likely to become even more
integral to agricultural operations worldwide. Future developments
could include enhanced sensor capabilities, greater automation,
and improved integration with other smart farming technologies.
These advancements promise to further increase efficiency and
precision, solidifying drones as a cornerstone of sustainable
agriculture and helping to meet the challenges of food production
in a changing world.

4.2 Use of Drones in Pest Management
4.2.1

Distribution of Biological Control Agents

The use of drones in the distribution of biological control agents
is a very important innovation in modern pest management. Drones
offer significant advantages in terms of coverage and speed,
allowing for widespread and rapid distribution of control agents
that were previously difficult to achieve with conventional
methods. This technology is especially effective in areas that are
difficult to reach or have challenging terrain, which often hinder
traditional agricultural practices.
In addition, drones are equipped with advanced GPS and sensor
technology, which allows for high-precision spraying. This not only
reduces the waste of control agents, but also minimizes the
negative impact on the environment. Drones can spray accurately
and precisely, reducing the use of chemicals and increasing the
efficiency of pest control. This makes drone technology an
invaluable tool in more sustainable and environmentally friendly
pest management.

Implementation Challenges:
The Impact of Regulations and Policies on Drone Usage in
Agriculture
The use of drones in agriculture holds significant promise for
enhancing productivity, crop monitoring, and efficiency. However,
one of the major barriers to widespread adoption is the strict
aviation regulations imposed by various countries. These
regulations are primarily designed to ensure safety, security, and
privacy, but they can also stifle innovation and limit the potential
benefits of drone technology in agriculture. In the study by Nurmi
et al. (2021), it is highlighted that balanced and supportive policies
are crucial to facilitate the legal and safe integration of drones into
agricultural practices. Without such policies, farmers may face
challenges in obtaining the necessary permissions and licenses,
thereby preventing them from fully leveraging drone technology.
Collaborative efforts between policymakers, industry stakeholders,
and agricultural communities are essential to create an enabling
environment that supports the safe and effective use of drones in
agriculture.

Advantages of Drone Distribution:
Efficiency and Speed
Drones are revolutionizing the agricultural sector by enhancing the
efficiency and speed of biological control agent distribution. These
unmanned aerial vehicles can cover vast agricultural expanses
rapidly, making them particularly useful for areas with difficult-tonavigate terrains or regions that are otherwise hard to reach.
According to Smith and Green (2021), drones provide a significant
advantage due to their capability to respond promptly to sudden
pest outbreaks. This swift response is crucial in mitigating the risk
of crop losses, as it ensures that biological control agents are
applied quickly and effectively. The ability of drones to deliver these
agents in real time allows for a proactive approach to pest
management, which is essential for maintaining healthy crops and
optimizing agricultural productivity.

Addressing Cost Concerns: Initial and Maintenance Expenses
The financial aspect of adopting drone technology is another
significant challenge, particularly for smallholder farmers. The
initial costs of purchasing drones, along with the ongoing
maintenance expenses, can be prohibitive. This financial barrier is
often cited as a key reason for the slow adoption of drones in
agriculture. However, with rapid technological advancements and
increasing competition in the drone industry, these costs are
expected to decrease over time. According to Kim et al. (2023), as
the technology matures and becomes more widely adopted,
economies of scale and improved manufacturing processes are
likely to reduce costs, making drones more accessible to small-scale
farmers. Additionally, government subsidies and financial
incentives could play a pivotal role in alleviating the financial
burden on farmers, encouraging more widespread use of drones in
the agricultural sector.

Accuracy and Precision
The precision with which drones operate is another significant
advantage, setting them apart from traditional distribution
methods. Equipped with GPS technology and advanced sensors,
drones are capable of spraying biological control agents with
remarkable accuracy. This precision minimizes the waste of
valuable resources and reduces the potential negative
environmental impacts associated with the over-application of
chemicals. Lee et al. (2020) highlight that conventional methods
often result in uneven distribution and excessive chemical use,
which can harm the environment and lead to inefficient pest
control. By contrast, drones ensure that control agents are applied
uniformly and only where needed, promoting sustainable
agricultural practices and preserving the surrounding ecosystem.

Enhancing Technical Skills through Training Programs

Labor Cost Savings

Operating drones effectively requires a certain level of technical
skill and knowledge. Many farmers, particularly those from older
generations or those with limited access to technology, may not
possess the necessary skills to operate drones. As highlighted by
Brown et al. (2022), training programs are essential to equip
farmers with the skills needed to manage drone technology
effectively. These programs should cover a wide range of topics,
including drone operation, data analysis, and maintenance. By
providing hands-on training and support, farmers can become more
confident and proficient in using drones, ultimately leading to
better adoption rates and more effective use of the technology.
Furthermore, partnerships between educational institutions,
agricultural organizations, and technology companies can facilitate

The adoption of drones for the distribution of biological control
agents also offers substantial savings in labor costs. Traditionally,
pest control in agriculture requires significant manual labor, which
can be both costly and risky. By utilizing drones, farmers can reduce
their reliance on manual labor, thereby cutting labor expenses.
Furthermore, this technological shift decreases the health risks for
workers who would otherwise be exposed to hazardous chemicals
during manual application. Chen et al. (2021) emphasize that this
reduction in health risks is a critical benefit, enhancing worker
safety and contributing to a healthier work environment. The
overall reduction in labor costs and improved worker safety make

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the development and implementation of these training programs,
ensuring they are accessible and tailored to the needs of farmers.

sustainability. With continued research and collaboration among
stakeholders, the future of drones in agriculture looks promising,
offering new opportunities for innovation and growth.

In conclusion, while the integration of drones into agriculture
presents numerous benefits, several barriers must be addressed to
realize their full potential. Regulations and policies need to be
supportive and balanced to promote safe and legal drone use. The
initial and maintenance costs must be reduced to make the
technology more accessible, particularly to smallholder farmers.
Additionally, comprehensive training programs are crucial to equip
farmers with the necessary technical skills. By addressing these
challenges, the agricultural sector can fully embrace drone
technology, leading to increased productivity, efficiency, and

Research shows that the use of drones in the distribution of
biological control agents can improve the efficiency and
effectiveness of pest management, while reducing negative
impacts on the environment. Although there are challenges in
terms of regulation and costs, the long-term benefits make this
technology a valuable investment for sustainable agriculture in the
future. Successful implementation requires strong policy support,
technical training, and collaboration between the public and private
sectors.

Table 4: Effectiveness of Drones in Distribution of Biological Control Agents
Studies
Year
Distribution
Reduction of
Coverage
Operational
Costs
Lee et al.
2020
80%
30%
Chen et al.
2021
85%
25%

Figure 2: Visualization of the use of drones in the distribution of biological control agents in agricultural land.

4.2.2

Challenges and Opportunities

friendly agricultural practices, such as precision farming that
reduces the use of pesticides and chemical fertilizers. Research by
Anderson and Baird (2019) shows that the application of drone
technology in precision agriculture can reduce pesticide use by up
to 30%. In addition, by utilizing data generated by drones, farmers
can make more precise and informed decisions, thereby increasing
agricultural yields without damaging the environment. Thus,
despite the various challenges that must be faced, the potential of
drones to support sustainability and efficiency in agriculture
remains a hope worth fighting for.

The use of drones in agriculture has become one of the most
talked about innovations due to its great potential in increasing
productivity and efficiency. On the one hand, drones can help
farmers monitor agricultural land in real-time, identify areas that
need more attention, and optimize the use of resources such as
water and fertilizers. A study by Zhang et al. (2020) showed that the
use of drones in land mapping can increase crop yields by up to 20%
by minimizing waste. However, on the other hand, the application
of this technology is not without challenges. Strict flight regulations,
especially in densely populated areas, often limit the freedom of
drone use. In addition, the high initial cost of purchasing and
maintaining drones, as well as limited technical knowledge among
farmers, are significant barriers to the widespread adoption of this
technology.

4.3 AI and Drone Integration
4.3.1

Synergy Between Technologies

The integration of AI and drones opens up new opportunities
for more holistic and effective pest control. AI can guide drones in
pest control missions, increasing the precision and efficiency of
operations. Empirical data shows a reduction in chemical pesticide
use of up to 40% and an increase in crop yields of 20%.

Amid these challenges, the opportunity to improve agricultural
sustainability through the use of drones remains very promising.
This technology can help farmers implement more environmentally

Table 5: Impact of AI and Drone Integration on Pest Control
Pesticide
Increased
Studies
Year
Reduction
Crop Yield
Zhang
2021
40%
20%
et al.

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Figure 3: Diagram of AI and drone integration in a pest control system, showing the workflow from detection to intervention.

Based on the image above, the integration of AI and drones in
the pest control system shows a holistic approach that combines
various stages from detection to intervention. In the early stages,
AI plays an important role in analyzing data collected through
various sensors and images taken by drones to detect the presence
of pests. Machine learning algorithms allow AI to identify pest
attack patterns based on weather data, soil conditions, and pest
infestation history. Research by Kamilaris and Prenafeta-Boldú
(2018) shows that the use of AI can increase the accuracy of pest
attack detection and prediction by up to 85%, accelerating farmers'
response in taking preventive measures. Thus, AI helps reduce
dependence on chemical pesticides by providing more precise and
efficient intervention recommendations.

helps reduce the environmental impact of pesticide use and
increases food security in vulnerable areas. The combined use of
artificial intelligence (AI) technology and drones in pest control has
been shown to increase the effectiveness of agriculture in a more
sustainable way. By using drones equipped with sensors and AI
algorithms, farmers can monitor crop conditions in real time,
identify pest-infested areas, and apply appropriate measures more
efficiently. According to a study published in the journal "Precision
Agriculture", the use of data collected by drones allows farmers to
reduce pesticide use by up to 30% by targeting applications only to
areas that really need treatment (Zhang et al., 2019). This not only
reduces operational costs but also minimizes negative impacts on
the environment and human health, and increases the
sustainability of agricultural practices.

In the next stage, drones are used to implement interventions
recommended by the AI system. Drones are capable of distributing
biological control agents with high precision and in a short time,
which was previously difficult to do with conventional methods.
According to research by Lee et al. (2020), the use of drones in the
distribution of biological agents can increase efficiency by up to 30%
compared to manual methods. Drones are equipped with GPS
technology and sensors that enable accurate spraying and minimize
negative environmental impacts. By utilizing real-time data and
high maneuverability, drones can reach difficult-to-access areas
and provide treatments according to the specific needs of each
field. This combination of AI and drones, as proven in a study by
Zhang et al. (2021), has been shown to reduce the use of chemical
pesticides by up to 40% and increase crop yields by 20%, making it
a highly effective solution to support sustainable agriculture. This
integration shows great potential in addressing pest control
challenges efficiently and environmentally friendly.
4.3.2

In addition, the application of AI in data analysis allows for
faster and more accurate decision-making in pest control. By using
predictive models supported by historical data and current weather
conditions, farmers can predict potential pest attacks and plan
preventive measures better. According to a report from the Food
and Agriculture Organization (FAO), the application of this
technology contributes to increasing food security, especially in
areas vulnerable to climate change and pest attacks (FAO, 2021). By
integrating AI and drones, agriculture can transform to be more
efficient, environmentally friendly, and able to meet the increasing
food needs in the future.
4.4 Implications for Agricultural Policy and Practice
4.4.1

Agricultural Technology Policy

To maximize the benefits of this technology, policies that
support infrastructure development and training for farmers are
essential. Collaboration between government, the private sector,
and educational institutions can accelerate the adoption of this
technology in agricultural practices.

Contribution to Sustainable Agriculture

The combined use of AI and drones not only increases the
effectiveness of pest control but also supports more
environmentally friendly and sustainable agriculture. This approach

Table 6: Policy Recommendations for Implementation of AI and Drone Technology
Policy Aspects
Recommendation
Infrastructure
Increased internet access and technology
Training
Educational program for farmers
Regulation
Policies that support innovation

Policy Aspects and Recommendations

technology in pest control. In countries such as Saudi Arabia and
Indonesia, where technological infrastructure is still in the
development stage, adequate internet access is a prerequisite for
running AI systems that require stable and fast data connections.
Research by Nurmi et al. (2021) shows that limited technological

Infrastructure: Improving Internet Access and Technology
Improving internet access and technology is a fundamental step
that must be taken to support the implementation of AI and drone

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infrastructure can be a major barrier to the adoption of new
technologies in the agricultural sector. By expanding the internet
network to rural areas and improving its quality, farmers can more
easily access the latest information and technology, which in turn
increases agricultural efficiency and productivity. In addition,
investment in technologies such as soil and weather sensors will
provide the data needed for AI systems to predict pest attacks more
accurately. The government can play an active role in building this
infrastructure through public-private partnerships, which will not
only accelerate the development process but also ensure the
sustainability of these projects.

technologies. Research by Johnson et al. (2021) highlights the
importance of ongoing training in improving farmers’ technical
capabilities, especially in less developed areas. Through structured
training programs, farmers can learn how to operate drones,
interpret data generated by AI systems, and understand more
sustainable pest control practices. These programs should be
designed to be inclusive, covering farmers from different
backgrounds and business scales, and tailored to local conditions to
be more relevant and practical. Governments and educational
institutions can work together to develop comprehensive training
curricula, while the private sector can provide the necessary
technology and resources. In this way, these education programs
not only improve farmers’ technical capacity but also empower
them to contribute to food security and better environmental
management.

Training: Educational Program for Farmers
Education and training programs are essential to ensure that
farmers have the skills needed to effectively utilize AI and drone

Figure 4: Agricultural technology policy diagram, showing the relationship between policy, implementation, and outcomes.

4.4.2

Socio-Economic Impact

CONCLUSION

The implementation of modern agricultural technology has
great potential to improve the welfare of farmers, especially in
developing countries such as Saudi Arabia and Indonesia. By
utilizing more efficient technology, farmers can reduce production
costs through the use of more sophisticated tools and machines and
precision farming practices. For example, the application of sensor
technology to monitor soil moisture and plant nutrient needs can
help farmers determine the right time and amount of fertilizer
application, which in turn can increase crop yields. A study
conducted by the Food and Agriculture Organization (FAO) showed
that the use of information and communication technology in
agriculture has increased productivity by up to 30% in some rural
areas, strengthening the argument that technological innovation
can bring significant benefits to farmers' welfare (FAO, 2020).

The study revealed that the integration of artificial intelligence
(AI) and drone technology in pest control offers a promising
solution for sustainable agriculture, especially in developing
countries such as Saudi Arabia and Indonesia. Literature analysis
shows the great potential of this technology in reducing the use of
chemical pesticides by 40% and increasing crop yields by 20%. AI
enables more accurate pest detection and prediction by utilizing
weather data, soil conditions, and pest attack patterns. On the
other hand, drones provide advantages in the distribution of
biological control agents and real-time field monitoring, which were
previously not possible with conventional methods.
However, the implementation of this technology is not without
challenges, including limited technological infrastructure, high
initial costs, and lack of technical skills among farmers. To overcome
these obstacles, strong policy support from the government,
investment in infrastructure and training, and collaboration
between the public and private sectors are needed. This
collaboration will accelerate the adoption of AI and drone
technologies in agricultural practices, bringing wider benefits to
society.

Furthermore, inclusive agricultural technology development
can have a positive impact on rural communities as a whole. By
increasing farmers’ access to technology and information, and
providing adequate training, communities can become more
independent in managing their agricultural resources. Research by
the International Fund for Agricultural Development (IFAD) shows
that when smallholder farmers are given access to new
technologies and training, they can significantly improve their food
security and incomes. This can also serve as a model for other
countries facing similar challenges in pest control and increasing
food production. Thus, collaboration between governments,
research institutions, and the private sector is essential to create an
ecosystem that supports technological innovation in agriculture,
which can ultimately drive economic growth and the well-being of
rural communities.

In addition, it is important to ensure that the adoption of these
technologies is inclusive, so that the benefits can be felt by all levels
of society, especially smallholder farmers who are most vulnerable
to the impacts of climate change and market uncertainty. Training
and education programs should focus on improving the technical
capacity of farmers to operate and maintain these technologies, so
that they can fully benefit from their benefits.

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On the research side, there is an urgent need for further studies
that explore the unique adaptations and challenges faced in
implementing these technologies in different local contexts. Future
research should consider the ecological and socio-economic
variables that may impact the effectiveness of these technologies,
as well as develop sustainable business models to support the
deployment of these technologies in resource-constrained areas.

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