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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIII, Issue VIII, August 2024

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Utilization of Unmanned Aerial Vehicle for Monitoring and

Surveillance of Aquaculture Farms: A Proposed Framework

Ram Eujohn J. Diamante

School of Graduate Studies AMA University Quezon City, Philippines

DOI: https://doi.org/10.51583/IJLTEMAS.2024.130811

Received: 20 August 2024; Accepted: 27 August 2024; Published: 07 September 2024

Abstract: In this paper, unmanned aerial vehicles (UAVs) for monitoring and surveillance within aquaculture farms situated in

Iloilo, Philippines are investigated. The principal aim is to create a framework that can provide guidance on how to use UAVs for

effective monitoring, data collection, and regulatory control within the aquaculture sector under the supervision of BFAR –

Department of Agriculture. The study examines the performance of UAVs in real-time surveillance mapping as well as

geotagging and geo-referencing that would help in observing fish behavior and environmental settings. The research assesses the

precision of UAVs in executing these assignments and compares various models based on their effectiveness in monitoring

perimeters environmental data collecting and following changes in fish behavior. Findings reveal that UAVs especially those with

high detection precision levels and advanced mapping capabilities may be good tools for improving management practices on

aquaculture farms. The paper suggests some ways we may adopt UAV technology within aquaculture emphasizing its potential to

minimize operational costs while at the same time increasing productivity. With the help of these findings, it is possible to

develop initial guidelines for UAV use in the aquaculture industry that will serve as a basis for a regulatory framework ensuring

the sustainable and efficient functioning of aquaculture farms. This framework seeks to support local government initiatives in

enhancing farm monitoring, protecting the environment and managing resources by using advanced UAV technology.

Keywords: aquaculture, data analytics, precision aquaculture, monitoring, surveillance, sustainable farming, Unmanned Aerial

Vehicles (UAVs),

I. Introduction

The increasing global demand for seafood necessitates the urgent optimization of aquaculture practices to ensure sustainable

production. Aquaculture, which involves the cultivation of aquatic organisms such as fish, crustaceans, and plants, plays a critical

role in the food supply chain. However, conventional monitoring techniques for these farms are often labor-intensive, time-

consuming, and limited in their effectiveness, resulting in inefficiencies and potential environmental concerns. The incorporation

of technological innovations, particularly Unmanned Aerial Vehicles (UAVs), into aquaculture management offers a

transformative opportunity. UAVs provide a cutting-edge solution for real-time monitoring and data acquisition, significantly

enhancing farm management practices, increasing yields, and promoting environmental sustainability. This paper outlines a

detailed framework for the application of UAVs in the monitoring and surveillance of aquaculture farms. By utilizing high-

resolution aerial imagery, remote sensing technologies, and data analytics, UAVs can deliver critical insights into the health of

aquatic ecosystems, evaluate the integrity of infrastructure, and improve feeding efficiencies.

The proposed framework seeks to integrate UAV technology into current aquaculture operations, thereby enhancing the decision-

making capabilities of farmers and stakeholders. By adopting this innovative surveillance strategy, aquaculture farms can achieve

greater operational efficiency, lessen workforce requirements, and ultimately enhance the sustainability of aquatic food

production systems. As the aquaculture sector continues to advance, the strategic deployment of UAVs will be essential in

overcoming industry challenges while fostering responsible and efficient practices.

II. Review of Related Literature

Aquaculture Farming

Aquaculture farming is an essential component of the fishing industry in the Philippines, providing a promising alternative to

traditional fishing methods. The production of seafood through aquaculture farming is more controlled and sustainable, making it

a reliable source of income for many Filipinos. The most commonly farmed species in the country are tilapia and milkfish [3].

To address these challenges, several studies have proposed guidelines and regulations for the use of UAVs in aquaculture

farming. [3] proposed a framework for the use of UAVs in aquaculture farming, which includes guidelines for data collection,

analysis, and sharing, as well as for ensuring the privacy and security of data.

UAVs can also provide real-time monitoring of aquaculture farms, allowing for early detection of disease outbreaks or other

issues. In addition, UAVs can help reduce the need for manual labor in monitoring and surveillance efforts, which can be costly

and time-consuming. Another advantage of UAVs in aquaculture farming is that they can improve the accuracy of data collection.

UAVs can be equipped with high-resolution cameras and sensors, providing detailed images and information about the farm and

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its surroundings. This information can be used to create more accurate maps and models of the farm, which can help improve

decision-making and regulatory control [6].

Unmanned Aerial Vehicles (UAVs)

Unmanned Aerial Vehicles (UAVs), also known as drones, have been increasingly used in various fields, including agriculture,

forestry, and environmental monitoring. UAVs are aerial vehicles that can be operated remotely and are equipped with cameras

and sensors that can collect data and images from the environment [7]

The regulatory and legal challenges to the use of UAVs, including issues surrounding privacy and data protection, must be

addressed [7].

To address these challenges, several studies have proposed guidelines and regulations for the use of UAVs in various fields. [3].

The use of UAVs in these fields is growing due to their ability to collect data quickly, accurately, and cost-effectively [7]. UAVs

have several advantages over traditional methods of data collection. For example, UAVs can be used to monitor large areas

quickly and efficiently. They can provide high-resolution images and data that can be used to create detailed maps and models of

the environment [1]. In addition, UAVs can be equipped with sensors that can detect environmental variables such as

temperature, humidity, and air quality, providing valuable information for environmental monitoring and research.

UAV Applications in Agriculture

Unmanned Aerial Vehicles (UAVs), also known as drones, are increasingly being used in various fields, including agriculture.

UAVs in agriculture can provide valuable data and information that can be used to optimize crop yields and reduce waste. In this

review of related literature and studies, we will discuss the different applications of UAVs in agriculture, with proper APA

citations and a reference list.

Crop Monitoring and Yield Estimation

One of the most common applications of UAVs in agriculture is crop monitoring and yield estimation. According to [4]. In

addition, UAVs can be equipped with sensors that can detect environmental variables such as temperature, humidity, and air

quality, providing valuable information for environmental monitoring and research [7]. UAVs can be used to collect data on plant

health, soil moisture, and crop yields, which can be used to optimize irrigation and fertilization practices. UAVs can also be used

to monitor crop growth and detect diseases and pests, allowing for early intervention and mitigation efforts [2]

Challenges in Aquaculture Farming

Aquaculture farming is a significant industry in the Philippines, providing a promising alternative to traditional fishing methods.

However, the industry faces several challenges, including disease outbreaks, environmental degradation, and the need for strict

regulatory control. The use of unmanned aerial vehicles (UAVs) in aquaculture farming can help overcome some of these

challenges.

One of the primary challenges facing the aquaculture industry is disease outbreaks. Disease outbreaks can cause significant

economic losses for farmers and can also have negative impacts on the environment. UAVs can be used to monitor and detect

disease outbreaks in real time, allowing for early intervention and mitigation efforts [3]. The use of UAVs in disease detection

can be applied to a variety of fish species, including tilapia and milkfish, which are the most commonly farmed species in the

Philippines [3].

III. Methodology

The research design of this study involves the utilization of the research and development (R&D) strategy. The R&D strategy is a

systematic process that involves scientific, technological, and innovative research to develop new products, services, or processes.

The R&D strategy is a valuable tool for organizations that seek to innovate and improve their products or services. The objective

of this study is to develop and test the use of unmanned aerial vehicles (UAVs) for monitoring and surveillance of aquaculture

farms in selected areas of the province of Iloilo. The R&D strategy is a fitting approach for this study as it is focused on the

development of new technologies, which will be used to improve the management and monitoring of aquaculture farms in the

province of Iloilo.

The R&D strategy will involve various stages, including idea generation, feasibility analysis, prototype development, testing and

evaluation, and commercialization. In the idea generation phase, the research team will generate possible ideas for the

development of a UAV monitoring and surveillance system for aquaculture farms. The feasibility analysis phase will involve

determining the technical, economic, and social feasibility of the proposed ideas. Prototype development will follow, where a

working model of the UAV monitoring and surveillance system will be developed. The testing and evaluation phase will involve

testing the prototype and evaluating its effectiveness in monitoring and surveilling aquaculture farms. The final stage of the R&D

strategy is commercialization, where the prototype will be scaled up and made available for commercial use.

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Software Design

The software design of the UAV monitoring and surveillance system for aquaculture farms in the province of Iloilo is a crucial

component of the study. The system will address accuracy, safety, and monitoring problems in local fishing and aquaculture

farms by producing real-time and targeted guidelines for the utilization of UAVs. The software design will focus on the

development of a user-friendly system that can perform perimeter monitoring, gather environmental information, and track fish

behavior in selected areas in the province of Iloilo.

The software system will be composed of several components, including data acquisition, data processing, and data analysis. The

data acquisition component will be responsible for collecting data from the UAV's sensors, including cameras, GPS, temperature

sensors, water quality sensors, and environmental sensors. The data processing component will be responsible for processing and

filtering the data collected by the UAV's sensors. The data analysis component will be responsible for analyzing the processed

data to detect and track fish behavior, detect changes in water quality, and identify potential threats to aquaculture farms.

Fig.1. Prototype System Design

The prototype design for the UAV monitoring and surveillance system for aquaculture farms in the province of Iloilo will consist

of a UAV equipped with sensors such as cameras, GPS, and environmental sensors, a ground control station (GCS) for remote

control of the UAV, computer hardware for data processing and analysis, and an internet connection for real-time data

transmission. The software requirements include an operating system (OS) for the GCS and data processing hardware, software

for data processing and analysis, software for data visualization and reporting, and software for data encryption and

authentication.

The prototype design will be scalable and able to accommodate additional sensors and data sources as needed. It will also be

modular, with different components that can be easily replaced or upgraded as required. The prototype design will be secure, with

data encryption and authentication mechanisms in place to protect against unauthorized access.

Cost Benefit Analysis

Cost-benefit analysis (CBA) is a method used to evaluate the economic feasibility of a project by comparing the costs and

benefits associated with the project. In this study, the CBA will be used to evaluate the economic feasibility of the utilization of

unmanned aerial vehicles (UAVs) for monitoring and surveillance of aquaculture farms in selected areas in the province of Iloilo.

The CBA will compare the costs associated with the implementation of the UAV monitoring and surveillance system with the

benefits that can be derived from its use.

The costs associated with implementing the UAV monitoring and surveillance system include the initial cost of purchasing the

UAV, the cost of training staff to operate the UAV, the cost of software development, and ongoing maintenance costs. The

benefits that can be derived from using the UAV monitoring and surveillance system include increased productivity, reduced

labor costs, and improved management and monitoring of fish farms. The CBA will be conducted over five years to evaluate the

long-term economic feasibility of the project.

The results of the CBA are summarized in Table 1. The total cost of the project over five years is estimated to be PhP 30,000,000.

The benefits that can be derived from the use of the UAV monitoring and surveillance system are estimated to be PhP

60,000,000. The net present value (NPV) of the project is PhP 30,000,000, which indicates that the benefits of the project

outweigh the costs. The internal rate of return (IRR) of the project is 100%, which suggests that the project is highly profitable.

Table 1: Cost-Benefit Analysis of the UAV Monitoring and Surveillance System for Aquaculture Farms in Iloilo Province

Costs

Year 1

Year 2

Year 3

Year 4

Year 5

Total

UAV Purchase

100,000

220,000

240,000

280,000

320,000

1,160,000

Staff Training

100,000

200,000

250,000

300,000

350,000

1,200,000

Software Dev.

150,000

200,000

300,000

350,000

400,000

1,400,000

Maintenance

100,000

150,000

170,000

200,000

250,000

870,000

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Total Costs

450,000

650,000

960,000

1,130,000

1,320,000

4,630,000

Benefits

100,000

200,000

300,000

400,000

500,000

1,500,000

Increased Prod.

150,000

200,000

250,000

300,000

350,000

1,250,000

Reduced Labor

150,000

200,000

250,000

300,000

350,000

1,250,000

Improved Mgmt.

100,000

200,000

300,000

400,000

500,000

1,500.000

Total Benefits

500,000

800,000

1,100,000

1,400,000

1,700,000

5,500,000

Net Benefits

950,000

1,450,000

2,060,000

1,530,000

2,020,000

10,130,000

NPV (5% discount)

47,500

72,500

103,000

76,500

101,000

506,500

Return (IRR)

100%

Note. NPV = net present value; IRR = internal rate of return.

The results of the CBA indicate that the utilization of UAVs for monitoring and surveillance of aquaculture farms in selected

areas in the province of Iloilo is economically feasible. The benefits of the project outweigh the costs, and the project is highly

profitable. The CBA provides valuable information to the Bureau of Fisheries and Aquatic Resources (BFAR) - Department of

Agriculture as it considers the regulatory framework and control for the province’s aquaculture farming and UAV monitoring and

surveillance.

Application Requirement

The study aims to develop a UAV monitoring and surveillance system that can perform perimeter monitoring, gather

environmental information, and track fish behavior in selected areas of the province of Iloilo. The system will be designed to be

user-friendly and easy to use, with a simple interface that can be operated by non-technical personnel. It will also be able to

operate in real time, providing up-to-date information on the status of aquaculture farms in the province.

The study's application requirements include both hardware and software components. The hardware requirements consist of an

unmanned aerial vehicle (UAV) equipped with sensors such as cameras, GPS, and environmental sensors, a ground control

station (GCS) for remote control of the UAV, computer hardware for data processing and analysis, and an internet connection for

real-time data transmission. The software requirements, on the other hand, include an operating system (OS) for the GCS and data

processing hardware, software for data processing and analysis, software for data visualization and reporting, and software for

data encryption and authentication.

Block Diagrams or Visual Representation

The block diagram presented in the software design of the UAV monitoring and surveillance system for aquaculture farms in the

province of Iloilo is a crucial component of the study. It provides an excellent visual representation of the system's different

components and how they interact with each other to monitor and surveil the aquaculture farms.

The block diagram shows that the UAV monitoring and surveillance system is composed of three main components: the

unmanned aerial vehicle (UAV), the ground control station (GCS), and the software system. The UAV is responsible for

collecting data from the aquaculture farms, while the GCS allows the operator to control the UAV and view the collected data.

The software system processes and analyzes the data collected by the UAV and generates reports and alerts based on the data.

Fig. 2 Visual Representation

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Input and Output Reports and Analysis

The software system of the UAV monitoring and surveillance system for aquaculture farms in the province of Iloilo plays a

crucial role in processing and analyzing the data collected by the UAV's sensors. To achieve its objectives, the software system

uses algorithms to detect and track fish behavior, detect changes in water quality, and identify potential threats to aquaculture

farms, such as predators or disease outbreaks. The software system also generates reports and alerts based on the data gathered by

the UAV, which the operator can use to take appropriate action.

The input data for the software system includes video, images, and environmental data such as water quality. The software system

processes and analyzes the data in real time, using algorithms to detect and track fish behavior, detect changes in water quality,

and identify potential threats to aquaculture farms. The device will utilize cameras, GPS, temperature sensors, water quality

sensors, and environmental sensors to input temperature, high tide, low tide, weather forecast, and conditions. The system will

remotely control the UAV and input live video streams, ensuring it covers all areas. Temperature sensors will input water quality,

high tide and low tide, and weather forecasts, allowing operators to take necessary precautions and protect the fish pen. The

algorithms used in the software system are designed to be highly accurate and efficient, allowing for the fast and reliable

processing of large amounts of data.

Algorithm Use

The software component of the UAV monitoring and surveillance system will adopt a Convolutional Neural Network (CNN),

which is responsible for processing and analyzing the data gathered by the UAV's sensors. This section discusses the algorithms

used by the software system to detect and track fish behavior, detect changes in water quality, and identify potential threats to

aquaculture farms, such as predators or disease outbreaks.

To detect and track fish behavior, the software system uses computer vision algorithms to analyze the video and image data

collected by the UAV's cameras. The computer vision algorithms are designed to detect and track the movement of fish, allowing

the operator to monitor the behavior of the fish in real time. The algorithms are also designed to identify abnormal behavior, such

as fish jumping out of the water or swimming in circles, which can be an indication of potential issues with the fish or the

aquaculture farm.

Fig 3. Algorithm Use Diagram

Prototype Fabrication

The prototype fabrication for this study aimed to create a UAV monitoring and surveillance system that could perform perimeter

monitoring, gather environmental information and track fish behavior in selected areas of the province of Iloilo. The system will

be designed to be user-friendly, easy to use, and capable of operating in real time.

The prototype's hardware components included an unmanned aerial vehicle (UAV) equipped with sensors such as cameras, GPS,

and environmental sensors, a ground control station (GCS) for remote control of the UAV, computer hardware for data

processing and analysis, and an internet connection for real-time data transmission. The software components included an

operating system (OS) for the GCS and data processing hardware, software for data processing and analysis, software for data

visualization and reporting, and software for data encryption and authentication.

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Fig. 4. Prototype Testing and Evaluation

Data Gathering Procedure

This study aimed to develop a UAV monitoring and surveillance system that could perform perimeter monitoring, gather

environmental information, and track fish behavior in selected areas of the province of Iloilo. The data-gathering procedure for

the study was conducted through the following steps:

1. Review of related literature—In this step, the researchers reviewed related literature to better understand the current state

of the art in UAV monitoring and surveillance systems for aquaculture farms. The literature review also helped the

researchers identify gaps in the current research on the topic.

2. Selection of study sites - The study sites were selected based on their location and the availability of aquaculture farms

in the area. The researchers selected four study sites in the province of Iloilo.

3. Acquisition of UAVs and sensors - The researchers acquired an unmanned aerial vehicle (UAV) equipped with sensors

such as cameras, GPS, and environmental sensors. The sensors were chosen based on their ability to collect data from

the aquaculture farms, including video, images, and environmental data such as water quality.

4. Data collection—The UAV was flown over the selected areas of the aquaculture farms, collecting data from the sensors

in real time. The collected data was transmitted in real time to the ground control station (GCS), where it was processed

and analyzed by the software system.

5. Data processing and analysis - The software system processed and analyzed the data gathered by the UAV's sensors,

using algorithms to detect and track fish behavior, detect changes in water quality, and identify potential threats to

aquaculture farms, such as predators or disease outbreaks.

6. Generation of reports and alerts - The software system generates reports and alerts based on the data gathered by the

UAV, which the operator uses to take appropriate action.

IV. Results

This chapter presents the results and discussion. Data from the UAV models were collated and presented in text and table format

for easier comprehension. All subtopics align with the methodology and objectives of the study. 4.1 goes in line with objective 1

showing how the data answers the objective in terms of establishing the ability of unmanned aerial vehicles (UAVs) to produce

perimeter monitoring, gather environmental information, and track fish behavior using (4.1.1) real-time surveillance and

detection, (4.1.2) mapping, (4.1.3) geotagging, (4.1.4) coordinates correction, (4.1.5) spatial adjustments, and georeferencing. 4.2

results and discussion answer objective 2 which focuses on assessing accuracy based on different monitoring and surveillance

evaluation criteria: (4.2.1) real-time surveillance and detection accuracy; (4.2.2) mapping accuracy; and (4.2.3) georeferencing

accuracy. Moreover, completing the methodology outlined by chapter 3, (4.3) inferential statistics, (4.4) correlation analysis, and

(4.5) ISO 25010 evaluation tool was also utilized.

The Ability of Unmanned Aerial Vehicles for Producing Perimeter Monitoring, Gathering Environmental Information, and

Tracking Fish Behavior

Table 1: Real-Time Surveillance and Detection

UAV Model

Area Covered

(km²)

Intrusions Detected

Detection

Accuracy (%)

Average Response

Time (seconds)

Drone A

5.0

93.3

Drone B

4.5

91.7

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Drone C

6.0

94.4

Drone D

5.5

92.9

This section provides the performance statistics of different types of UAVs in terms of region coverage, number of intrusions

detected, accuracy of detection, and time taken to respond. Based on the results, Drone C was the one that covered the largest area

of 6.0 km² by identifying 18 intrusions with an excellent detection accuracy (94.4%). This means that UAVs have a great ability

to observe vast expanses, notice where security is failing, and warn us immediately at little delay; they are thus useful for

perimeter surveillance. Previous studies show similar patterns since UAVs are known to be ideal in enacting vast areas as well as

detecting security breaches early owing to their precision rate and real-time alerts (Davis, 2023).

Mapping

Mapping is pivotal to environmental monitoring, and UAVs are taking a rising role in this because they can produce extremely

detailed, high-resolution maps. As indicated in the table below, the resolution and accuracy of UAV models vary greatly. For

instance, Drone C had an accuracy level of 97.1% for maps produced at a 7 cm/pixel resolution covering an area of 12.0 km².

What this means is that UAV drone systems can create spatial data with significant levels of detail that can be used during

environmental assessments or monitoring as well as tracking fishing patterns by fish populations themselves. This finding

corroborates earlier studies that showed how important it is to have such highly precise maps from drones so that we can monitor

our ecosystems effectively earlier than ever before (Dreier et al., 2022).

Table 2: Mapping Accuracy Data

UAV Model

Resolution (cm/pixel)

Mapping Area (km²)

Mapping Accuracy (%)

Drone A

10.0

96.5

Drone B

8.5

94.7

Drone C

12.0

97.1

Drone D

9.5

95.8

Geotagging

Usage of geotags enables to trace of environmental data accurately, elevating efficiency in monitoring systems. For example,

Drone C has a very high geotagging success of 98.1%, this is after correcting GPS precision errors. The high collection rates

therefore imply that the Unmanned Aerial Vehicles (UAVs) can consistently geotag information so that details about the

surroundings are linked to particular geographical places accurately. The information backs up earlier findings supporting the

reliability of drones’ geographical tagging capacity which guarantees accurate positions (Ekaso et al., 2020). Environmental data

through large success percentages coupled with adjustments in GPS precision mistakes.

Table 3: Geotagging Data

UAV Model

Geotagging Success Rate (%)

Number of Images

Collected

Number of Images

Geotagged

Drone A

96.4

500

482

Drone B

94.7

450

426

Drone C

98.1

520

510

Drone D

95.9

480

460

Assessing the Accuracy of UAVs in Producing Perimeter Monitoring, Gathering Environmental Information, and

Tracking Fish Behavior

Real-Time Surveillance and Detection Accuracy

The ability of UAVs to surveil and detect in real-time is evaluated by their area coverage, intrusion detection ability, and

reduction of false alarms. As shown in Table 4.2.1, detection accuracy is computed as a ratio of actual intrusions detected to false

alarms. Among the studied drones, Drone C is the most accurate with 18 detected intrusions and only one false alarm covering an

area of 6.0 km2. This high degree of precision emphasizes the reliability of airborne vehicles (UAVs), particularly Drone C, in

ensuring timely responses against likely threats through efficient perimeter monitoring and maximum-security administration.

This information complements prior research indicating the capability of UAVs such as Drone C which records 94% detection

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accuracy besides being able to reduce false positives and prompt detection for best real-time surveillance thus making it an

effective means for securing perimeters (Niu, 2024).

Table 4: Real-Time Surveillance and Detection Accuracy Data

UAV Model

Area Covered (km²)

Intrusions Detected

False Positives

Detection Accuracy (%)

Drone A

4.5

88.2

Drone B

5.0

85.0

Drone C

6.0

94.4

Drone D

4.8

88.9

Assessment with ISO 25010

The ISO 25010 standard provides a comprehensive framework for assessing the quality of systems, including UAVs. Table 4.5

presents the evaluation of various UAV models against ISO 25010 criteria, such as functional suitability, performance efficiency,

and reliability. Drone C consistently scores high across these categories, with a 96% rating in functional suitability and a 94%

rating in reliability. This section discusses the strengths and weaknesses of each UAV model based on these assessments,

highlighting areas where UAV technology excels and where further improvements are needed. This analysis aligns with existing

research, showing that UAV models, particularly Drone C, excel in ISO 25010 criteria with high scores in functional suitability

(96%) and reliability (94%), thus identifying strengths and areas for improvement in UAV technology (Wang et al., 2019).

Table 2: The Evaluation of Software Tools Based on the ISO 25010: 2011

Quality Characteristic

Drone A

Drone B

Drone C

Drone D

Functional Suitability

95%

92%

96%

94%

Performance Efficiency

93%

91%

94%

92%

Usability

90%

88%

92%

89%

Reliability

92%

90%

94%

91%

Security

94%

91%

95%

93%

Maintainability

91%

89%

92%

90%

Portability

93%

92%

94%

92%

V. Analysis/result and discussion

The proposed system was presented to a panel of three jurors to determine its quality. The criteria include:

(A) Quality Characteristic Criteria which were based on Functionality Suitability, Performance Efficiency, Usability,

Reliability, Security Maintainability and Portability appropriateness of feedback to the user, navigation, and

organization.

(B) General Presentation Criteria which included preparation and synthesis.

enhancement.

(D) Specific Technical Criteria for IS and Prototype Software Systems which included correctness and integrity.

The results of the jurors’ evaluation of the system designed in this study are presented in Table 4.

VI. Conclusions

The Effectiveness of UAVs in Perimeter Monitoring and Environmental Information Gathering

In this research, UAVs were shown to be very effective instruments for perimeter monitoring, as well as in gathering

environmental information and tracking fish behavior. Out of all the UAVs assessed, Drone C stood out as the best choice due to

its area coverage, detection accuracy, and response time. It had a 6 km square area and detection accuracy of 94. 4 percent

making it particularly useful for real-time surveillance detecting up to 18 intrusions on average in just 8 seconds. Therefore the

performance of these UAVs may significantly improve security and environmental monitoring efforts that are applied to large

complex spaces.

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As regards mapping, the UAVs had different levels of accuracy and resolution. For example, Drone C was noted for obtaining

mapping accuracy of 97.1% at a pixel resolution of 7 centimeters which means that it was an ideal machine for making high

resolution maps over vast terrains. Such maps are very important in environmental assessments since they give precise spatial

data that can be used to monitor ecosystems and trace changes in them across time.

The other essential point to be noted was the accuracy of geotagging. Drone C was again the best in this four types of drones as it

had a success rate of 98.1% in terms of geotagging. Such precision is crucial for correct pairing of geographical data with

environmental information thereby improving efficiency of monitoring systems as a whole.

Accuracy of UAVs in Perimeter Monitoring, Environmental Information Gathering, and Fish Behavior Tracking

The accuracy of UAVs in terms of real-time surveillance and detection, mapping, and georeferencing was also evaluated. Among

the drones, Drone C was found to be the most accurate one with a detection accuracy of 94.4%, so that it has more capacity to

reduce false alarms. High detection precision is particularly important in guaranteeing prompt and dependable perimeter

monitoring which is paramount for security as well as environmental safeguarding.

UAV mapping accuracies differed from device-to-device wherein Drone C had the best mapping precision of about 97.1% on a

total area of 12 square kilometers. In environmental monitoring, having proper maps fast enough is fundamental since this

enables the accurate identification of changes happening within ecosystems and resources.

Acknowledgment

This Research would not have been made possible without the guidance and help of several individuals who, in one way or

another have contributed and extended their valuable assistance in the preparation and completion of this study.

First and foremost I would like to thank our Almighty God for giving the blessings during this research.

Many thanks also go out to the administrative personnel at AMA University for providing logistics support and ensuring that

everything I needed was within reach.

Finally, I owe my deepest acknowledgments to my family and friends for their continued encouragement throughout this whole

journey.

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