1 Introduction

Fish farming or aquaculture, plays a pivotal role in meeting the ever-increasing global demand for fish and seafood. It serves as a sustainable source of protein, helps relieve pressure on wild fish populations, and provides economic opportunities for cultivators [1]. Despite these advantages, fish farmers face significant challenges in maintaining the health and well-being of their aquatic stocks. Key to the success of fish farming is the maintenance of optimal water quality parameters, as deviations from these benchmarks can lead to stress, disease, and even mass mortalities among fish populations [2]. Additionally, protecting fish from avian predation is a pressing concern, as such predation can result in substantial economic losses. Traditional defence measures, like netting enclosures, are costly, labour-intensive, and can have unintended consequences for the environment and avian biodiversity.

This study proposes a comprehensive solution that addresses these challenges by focusing on real-time water quality monitoring and bird strike prevention within fish farming. It harnesses technology to provide fish farmers with precise, real-time data, empowering them to make informed decisions. Through a user-friendly web interface and SMS notifications, fish farmers can receive instant updates on their pond’s ecological conditions, allowing for the prompt detection of anomalies. Moreover, the proposed solution aims to eliminate the need for netting enclosures, simultaneously safeguarding fish against avian threats and advocating for avian conservation [3,4,5].

In the context of this study, specific water parameters such as temperature, pH, dissolved oxygen (DO), total dissolved solids (TDS), and turbidity are of utmost importance. Temperature influences fish metabolism, growth, and immunity, and deviations can lead to stress and increased susceptibility to diseases. Water pH affects respiration and enzyme activity in fish, with extreme levels being harmful. Adequate DO levels are crucial for fish respiration, and low levels can lead to stress and reduced growth. High TDS levels can impact fish health and water quality, and controlling TDS through water exchange or filtration is essential. Turbidity, caused by suspended particles, affects water quality and light penetration, and managing it includes filtration and the control of algal growth. Monitoring these parameters is vital, and using tools like sensors and meters helps ensure that conditions are suitable for the specific fish species being cultivated. Employing methods such as aeration, shading, and chemical adjustments allows for the maintenance of ideal conditions, preventing stress, diseases, and mortalities, and ultimately fostering sustainable fish farming [6,7,8,9,10].

Additionally, bird attacks on fish farms have historically led to the use of traditional netting enclosures as a preventive measure, despite their drawbacks. These enclosures are expensive, require substantial labour, and can harm water quality, all while inadvertently trapping non-target species. Birds also suffer from entrapment, disrupted behaviour, and endangerment due to these enclosures. To address these issues, alternative solutions have emerged, including automated bird deterrent systems that use motion sensors, lasers, and noisemakers to deter birds without causing harm. Visual cues like reflective materials and decoy predators, acoustic deterrents with distress calls, and habitat modifications such as vegetation barriers have proven to be effective and less intrusive. Integrated approaches that combine these deterrent methods offer enhanced effectiveness and reduce habituation. These alternatives provide promising options for safeguarding fish farms while mitigating negative impacts on bird life and ecosystem balance [11,12,13,14,15,16,17,18,19,20,21].

The novelty of this study lies in its integrated approach, which combines real-time water quality monitoring and bird strike prevention, utilizing IoT technology and motion-detection mechanisms. By focusing on specific water parameters critical to the aquaculture system, this study seeks to bridge the gap in existing fish farming practices and enhance efficiency, sustainability, and environmental preservation.

The remaining sections of the paper are organized as follows: Sects. 2 and 3 present objective of this research and problem statement respectively. An extensive literature review has been depicted in Sect. 4. Section 2 elucidates the architecture of the proposed work. This section encompasses the dataflow architecture as well as the corresponding flow chart. Moreover, Sect. 5 discusses various measurement tools and hardware components like microcontrollers and sensors. Building upon Sects. 5 and 6 displays about the experiment procedure. The results obtained from the proposed model has been analysed in Sect. 7. Sections 8 and 15 offer insights into the cost analysis and research outcomes including a compression analysis comparing this work to prior studies respectively. Lastly, Sect. 16 outlines the potential future directions of this work and presents the concluding remarks.

2 Objective of the research

The paper aims to develop a comprehensive system for fishpond management with three main objectives. Firstly, it seeks to establish real-time monitoring of essential water parameters like temperature, pH, turbidity, dissolved oxygen, and TDS using IoT sensors. This monitoring is critical for fish health and growth. Secondly, the paper addresses bird attacks by introducing a novel approach involving PIR-based motion sensors and water-spraying to deter birds effectively. By doing so, it aims to reduce losses from bird attacks and create a more sustainable fish farming environment. Thirdly, the paper focuses on improving farming efficiency by automating data collection, analysis, and communication through a web application and SMS alerts. This multifaceted solution aims to enhance productivity, sustainability, and overall outcomes in fish farming by providing accurate data, efficient monitoring, and innovative bird deterrence measures.

3 Problem statement

Fish farming is a vital industry, addressing the increasing global demand for fish and seafood. However, fish farmers face challenges in maintaining optimal water quality and protecting fish from avian predation. Traditional methods for addressing these challenges are often costly, labor-intensive, and environmentally harmful. The problem at hand is the need for a comprehensive solution that combines IoT technology, real-time monitoring of critical water parameters, and innovative bird deterrent mechanisms to improve the efficiency and sustainability of fish farming while minimizing losses and environmental impacts. This research aims to develop such a solution to bridge the existing gaps in fish farming practices and enhance its economic viability and environmental preservation.

4 Literature review

The challenges faced by the fish farming industry have spurred research in two key areas: environmental monitoring and pest deterrent mechanisms. In the realm of environmental monitoring, previous studies have explored the potential of Internet of Things (IoT) technology to enable real-time tracking of water quality parameters in aquaculture systems. For example, in the study [22], a system utilizing IoT sensors has been developed to continuously monitor critical water parameters, such as temperature, dissolved oxygen levels, and pH, contributing to improve fish health management. Similarly, another study [23], has been employed a wireless sensor network to collect data on water quality and fish behaviour, enhancing the understanding of aquaculture conditions. These investigations underscore the importance of real-time monitoring in optimizing fish farming operations.

Efforts to mitigate the impact of avian threats on fish farming have also been a subject of research. In a study [24], researchers have explored bird deterrent mechanisms involving auditory cues and visual signals, demonstrating the effectiveness of multi-sensory strategies in bird management. Furthermore, another study [25] has proposed the integration of motion-detector-triggered automated water spraying systems to deter birds, offering a non-lethal approach to safeguarding fish farms. These approaches align with the central focus of the present work, which aims to leverage technology to protect fish from avian threats.

The fusion of IoT sensors with mobile applications has gained traction in the agricultural sector. In one study [26], a smart farming system based on IoT technology has been designed, integrating environmental sensors with a mobile app for remote monitoring. Similarly, in another study [27], the combination of IoT and mobile applications in agricultural management has improved crop yield and resource allocation. These investigations highlight the potential of integrating IoT technology and mobile apps to enhance efficiency in farming practices.

The proposed solution is informed by a range of research endeavours that delve into IoT-enabled water quality monitoring and pest deterrence mechanisms. By synthesizing these components and addressing the challenges faced by fish farmers, the proposed system offers a comprehensive strategy to enhance the sustainability and productivity of fish farming operations while minimizing losses and environmental impacts.

5 Proposed work

This research aims to emphasize the importance of real-time and accurate information in fish farming, as it directly impacts productivity and profitability. By promptly identifying and addressing water quality and bird attack issues, fish farmers can mitigate risks, optimize farming practices, and maximize production potential. The design of proposed system is depicted in Fig. 1. The proposed system utilizes IoT technology to assist fish farmers in monitoring their fish ponds. Sensors and internet technology are integrated with user-friendly interfaces like smartphone applications, desktop applications, and web services, enabling real-time monitoring. This system reduces the risk of losses and improves efficiency in fish farming. Sensors collect data from the fishponds, measuring parameters like temperature, pH levels, and oxygen levels. If any parameter exceeds the ideal range, a warning is generated. The system also includes a GPS module to provide geographic location information for parameter fluctuations. To ensure widespread accessibility, especially in rural or remote areas with limited connectivity, the system utilizes a GSM-based data communication system. SMS alerts are sent to the fish farmer’s basic mobile phone, informing them about parameter fluctuations or warning conditions. In case of connectivity loss, the system stores data on a micro-SD card and sends it as SMS messages once the connection is restored. Additionally, the system incorporates a bird deterrence feature using motion detection with PIR-based motion detectors. When birds enter the monitored area, the system detects their motion and activates an automated mechanism that sprays water in various angles to scare them away and protect the fish. Overall, the proposed system provides fish farmers with accurate and real-time data, reducing manual tests and sample collection, manpower requirements, and costs. It addresses connectivity limitations and budget constraints in rural or remote areas through SMS-based communication and a backup data transfer mechanism. The details of the proposed system are discussed in the following subsections:

Fig. 1
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Proposed system

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5.1 System architecture

The system architecture comprises three primary layers, as illustrated in Fig. 2a. The Physical Layer encompasses various sensors like turbidity, pH, temperature, and PIR motion sensors, along with microcontroller devices. These sensors collect environmental data within a catfish pond, which is then transmitted to the microcontroller devices. The Network Layer integrates a Wi-Fi module that establishes connectivity between the devices in the Physical Layer and internet services. This Wi-Fi module facilitates the seamless transfer of sensor data from the Physical Layer to the cloud system. At the apex, the Application Layer involves a mobile-centric application accessible on Android devices. This application empowers end users to actively monitor the prevailing conditions of the monitored environment, providing real-time data fetched from the cloud system.

5.2 Proposed flow of data in the system

Fig. 2
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a Proposed system architecture. b Data cloud firebase system

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The proposed data flow within the depicted system (as shown in Fig. 2b) involves several stages: Initially, sensors within the physical layer gauge key water parameters like turbidity, pH, temperature, and bird motion. Subsequently, this data is transmitted to microcontroller devices. These devices then forward the collected information to the cloud system via a Wi-Fi module, effectively establishing a connection between the physical layer and internet services. Within the cloud system, real-time sensor data is uploaded and stored, with the utilization of the MQTT protocol, known for its IoT communication capabilities. Functioning as a broker, the MQTT protocol sifts through incoming messages and disperses them to pertinent IoT clients. Furthermore, end-users gain access to this real-time data via an Android-based mobile application. The application interfaces with the cloud system through a REST API, facilitating a connection between cloud Firebase data and the end-users. This REST API employs standardized procedures and well-organized interfaces to enable seamless communication between devices and users. Overall, this architectural design seamlessly captures real-time data from catfish pond sensors, transmits it to a cloud system through Wi-Fi, and makes it accessible to end-users via a mobile application.

5.3 Hardware and software used

To prepare the proposed model, six major electronic components have been used which are listed in table 1. The Arduino UNO R3, based on the ATmega328 microcontroller, is a new version of the Arduino UNO. It has 14 digital I/O pins, 6 of which support PWM, and 6 analog input pins that can also act as digital pins. This versatile connectivity is crucial. In our project, the Arduino UNO R3 serves as a control hub, supporting connected components. Its ease of programming and numerous features makes it essential for efficient automation in our system. Moreover, the ESP8266, a versatile microchip, acts as a Wi-Fi module, easily connectable to microcontrollers such as Arduino. Equipped with embedded TCP/IP networking software, it links to wireless networks for internet communication.

Furthermore, the SEN 0189 turbidity sensor measures suspended particles in water using light-based detection. It’s used for various water sources like drinking water, tap water, ponds, and rivers. With an operating voltage of 5 V DC and a max power consumption of 40 mA, it’s selected for its accurate assessment of particle levels. This aids in water quality monitoring and potentially detecting pollution or contamination early on. Additionally, the SEN 0161 Analog pH sensor measures pH in aquaculture with 5 V DC power. Its range is 0 to 14 pH, accurate within +/− 0.1 pH at 25 °C. It’s perfect for enhancing aquaculture by maintaining ideal conditions for aquatic life, fitting our proposed system well. Another module is the RTD PT100 sensor, utilizing platinum for stable, corrosion-resistant temperature measurement. Its range is − 200 °C to + 600 °C, suitable for various applications. Lastly, a PIR (Passive Infrared) sensor detects emitted infrared radiation from objects for security and motion-activated lighting. It uses pyroelectric material split into halves, each with a lens to focus radiation. Moving objects with different temperatures create proportional electric signals. Amplification, noise filtering, and differentiation techniques process the signal to distinguish motion from variations. The output triggers actions like alarms or lighting. We pick the PIR sensor for energy-efficient bird presence detection, enhancing security and saving energy in different settings.

Firebase is popular for its reliability and NoSQL support. It manages large data volumes, various sources/formats, and offers real-time responses. It’s used for authentication, cloud messaging, APIs, and databases. Researchers use Firebase to transfer data between IoT devices and users. For instance, in parameter monitoring, Firebase processes data. Its real-time database acts as a communication tool between IoT devices and users in this research. Moreover, Android Studio, a robust Google IDE, excels in crafting Android apps. It uses Java/Kotlin for logic and XML for UI, and leverages Gradle for efficiency. The emulator tests on virtual devices, and Google Cloud Messaging and App Engine add cloud features. With Android App, Library, and Google App Engine Modules, it streamlines development. We choose Android Studio for our project for its rich features, cloud integrations, and modular approach, boosting productivity in creating top-notch Android apps.

Table 1 Components and software used for proposed work
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5.4 System flowchart

Here is a proposed flowchart outlining the step-by-step activities of the work described:

  1. 1.

    Start.

  2. 2.

    Read data from the fish pond sensors (temperature, pH, turbidity).

  3. 3.

    Receive sensor data in the microcontroller.

  4. 4.

    Connect the microcontroller to the Wi-Fi module.

  5. 5.

    Establish a connection with Firebase using the MQTT protocol.

  6. 6.

    Send the sensor data to Firebase for storage and real-time access.

  7. 7.

    Develop an Android-based application for end users.

  8. 8.

    Users access the Android application to view real-time data.

  9. 9.

    Check if there is a loss of connectivity.

  10. 10.

    If there is a loss of connectivity:

  • Store the data locally on a micro-SD card.

  • Continue reading and storing data on the micro-SD card until connectivity is restored.

  • Once connectivity is restored, send the stored data as SMS messages.

  1. 11.

    Implement motion detection using PIR-based motion detectors.

  2. 12.

    Detect birds within a certain radius of the fish pond area.

  3. 13.

    Trigger a motor to spray water in different angles to scare away the birds.

  4. 14.

    Monitor the fish pond environment and bird deterrence continuously.

  5. 15.

    Send SMS alerts to fish farmers in case of parameter fluctuations or warning conditions.

  6. 16.

    End.

Fig. 3
figure 3

System flowchart of the proposed work

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This flowchart (fig. 3) represents a technical process flow for a system that integrates sensors, microcontrollers, Wi-Fi communication, cloud storage, Android application development, local data storage, and real-time monitoring and alerts for a fishpond environment.

Here’s a pseudocode representation of the proposed system flowchart:

figure a

6 Experiments

6.1 Data collection

The data collection process has been described in your message involves testing IoT devices in catfish ponds to gather real-time data using sensor readings. The data is accessed via Firebase, and the dimensions of the fishpond are provided as 5 m long, 3 m wide, and 1.5 m deep. The environmental temperature of the fish-pond ranges from 25 to 30 °C with occasional light rain. The data collection has been conducted in eight rounds, with each round representing a specific time frame. The Table 2 provides the timing of each round, ranging from 06.00 A.M. to 08.00 P.M. with a break of every two hours. The rounds are numbered from 1 to 8, and each round corresponds to a specific time of data collection. In the data collection setup, the sensors (turbidity, pH, PIR and temperature) are directly connected to a microcontroller using a connector. The microcontroller is then connected to a Wi-Fi module device, allowing the data to be uploaded to Firebase. This setup enables the real-time monitoring of various parameters in the fishpond.

Table 2 Data collection time frame
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6.2 Measurement tools

In this study, the accuracy of the IoT device’s sensor data have been tested by comparing it with measurements taken using the following instruments which are shown in Table 3:

  • Temperature measuring pen: This tool is used to measure temperature. It operates on a 1.5 VDC power supply and has a temperature range of − 50 to 300 °C.

  • Portable turbidity meter: This meter is used to measure turbidity. It has a turbidity range of 0 to 1100 NTU (Nephelometric Turbidity Units), which is a unit used to quantify the clarity of a liquid.

  • Digital pH meter: This meter is used to measure the pH level of a solution. It has a pH range of 0 to 14, covering the entire pH scale from acidic to alkaline, as shown in Table 3 below.

Table 3 Measurement tools used for the experiment
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7 Result and discussion

This paper presents a solution for enhancing fish farming efficiency by addressing water quality monitoring and safeguarding against bird attacks. The proposed system comprises three layers: the physical layer utilizing sensors like turbidity, pH, temperature, and motion sensors for data collection. These sensor inputs are processed by microcontrollers (e.g., Arduino UNO R3, ESP8266) in the network layer, which employs a Wi-Fi module to transmit data to the cloud. The final layer involves a mobile application (as depicted in Fig. 4) that empowers users with real-time insights into the fish pond’s environmental conditions. This integrated approach enables fish farmers to proactively manage their operations and mitigate potential risks efficiently.

Fig. 4
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Mobile application user interface

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7.1 Temperature and dissolved oxygen measurement results analysis

The given information in Table 4 provides measurements of IoT devices and measurement tools in Celsius (C) across eight specific time categories. The highest measurements for both devices occur at 12.00 noon. When comparing the results between these two devices, the difference is not significant. However, the largest measurement gap occurs at 06.00 A.M., where there is a 1.10 °C difference between the measurement tool results (24.2 °C) and the IoT device measurements (25.3 °C). The measurement gaps at other specific times are less than 10 °C, and when averaged, the overall difference between the IoT devices and measurement tools is 0.40 °C. Moreover, a temperature compensation curve requires measuring saturated dissolved oxygen voltage at two temperatures. The saturated oxygen concentration and voltage at the current temperature can be calculated by measuring the temperature, which corresponds to saturated dissolved oxygen at standard atmospheric pressure [8]. Thus, Table 4 shows the derived dissolved oxygen (DO) (mg/L). Here is a breakdown of the measurements:

  1. 1.

    At 06.00 A.M., the IoT device recorded a temperature of 25.3 °C, while the measurement tool showed 24.2 °C, resulting in a gap of 1.1 °C.

  2. 2.

    At 08.00 A.M., the IoT device recorded 25.3 °C, slightly lower than the measurement tool’s reading of 25.8 °C, resulting in a gap of 0.5 °C.

  3. 3.

    At 10.00 A.M., the IoT device measured 26.2 °C, while the measurement tool showed 25.9 °C, resulting in a gap of 0.3 °C.

  4. 4.

    At 12.00 noon the IoT device recorded 27.9 °C, and the measurement tool showed 28.0 °C, resulting in a minimal gap of 0.1 °C.

  5. 5.

    At 02.00 P.M., the IoT device measured 27.7 °C, almost identical to the measurement tool’s reading of 27.6 °C, resulting in a negligible gap of 0.1 °C.

  6. 6.

    At 04.00 P.M., the IoT device measured 26.0 °C, while the measurement tool showed 26.3 °C, resulting in a gap of 0.3 °C.

  7. 7.

    At 06.00 P.M., the IoT device recorded 25.5 °C, slightly higher than the measurement tool’s reading of 24.9 °C, resulting in a gap of 0.6 °C.

  8. 8.

    At 08.00 P.M., the IoT device measured 25.2 °C, and the measurement tool showed 24.7 °C, resulting in a gap of 0.5 °C.

Overall, the measurements indicate that the temperature differences between the IoT devices and measurement tools are generally small, with an average gap of 0.40 °C. However, the largest difference occurs at 06.00 am, where the measurement tool records a slightly lower temperature compared to the IoT device. The calculated amount of dissolved oxygen (in mg/L) is also presented in Table 4.

Table 4 Temperature and dissolved oxygen measurement result
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From Fig. 5 it is observed that, at 12 noon the gap between the IoT device (black line) and the measurement results (red line) is not that significant. Between 10.00 A.M. and 12.00 noon, both devices detected an increase in temperature and a decrease in temperature between 02.00 P.M. to 08.00 P.M.

Fig. 5
figure 5

Graphic of temperature measurement result

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7.2 pH measurement result analysis

Table 5 pH level measurement result
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In simple terms, shown in Table 5, the measurement results of IoT devices and measurement tools for pH levels were compared, and they showed a difference of less than or equal to 0.3. The biggest difference occurred at 12:00 p.m., where the IoT devices showed a pH value of 7.0 and the measurement tools showed a pH value of 7.3, resulting in a gap of 0.3. However, the most accurate data collection happened at 4:00 P.M. and 8:00 P.M., where both the IoT devices and measurement tools showed the same pH value of 7.0. This means that the readings from both devices agree and provide reliable information about the pH level at those times.

Fig. 6
figure 6

Graphic of pH measurement result

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Figure 6 is a graphic representation of the pH measurement result where at 12.00 noon there is an increase in the pH level based on the measurement tool result. This is because at that hour it is the time for fish feeding, so that it increased the pH level in the pond. While the measurement results of IoT devices at 12.00 noon and 02.00 P.M. do not change and has a gap of 0.3 lower than the results of the measurement tool.

7.3 Turbidity measurement result analysis

The Table 6 shows a comparison between the turbidity measurements obtained from an IoT device and a traditional measuring instrument, using the Nephelometric Turbidity Unit (NTU) scale. The measurements were taken at different specific times throughout the day. The IoT device consistently produced lower turbidity measurement values compared to the measuring instrument. The largest difference between the two devices occurred between 08.00 A.M. and 10.00 A.M., with a gap of 2.8 NTU. On average, the difference between the IoT device and the measuring tool was 2.2 NTU across all measurements.

Table 6 Turbidity measurement result
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The graphical visualization is shown in Fig. 7 where the blue line shows the data generated by the IoT device and the red line shows the data generated by the measurement tool. In both devices there is an increase in turbidity at 12.00 noon and 02.00 P.M., this is because at that time it is the fish feeding schedule that increased the turbidity level in fishponds.

Fig. 7
figure 7

Graphic of turbidity measurement result

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7.4 Motion sensor (PIR) measurement result analysis

The PIR sensor is used in the proposed system to detect motion, particularly the presence of birds within a certain radius of the fish pond area. It triggers an automated mechanism that sprays water in various angles to scare away the birds and protect the fish. The effectiveness of the PIR sensor is crucial in preventing bird attacks and ensuring the safety of the fish. The Table 7 presents the PIR sensor measurement results, comparing the data obtained from the IoT device with the expected or actual motion events:

Table 7 PIR sensor measurement results
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The PIR sensor has demonstrated its effectiveness in detecting motion events, specifically bird activity, during the monitored time frames. It correctly identified the presence or absence of birds, aligning with the expected or actual bird movements. This capability is essential in protecting the fish from bird attacks, especially during vulnerable times such as fish feeding. The PIR sensor’s accurate detection of motion contributes to the overall efficiency and security of the fish farming system. It ensures that timely actions, such as activating the water spraying mechanism, are taken to deter birds and safeguard the fish population.The PIR sensor plays a critical role in bird deterrence and fish protection, and the measurement results indicate its reliable performance in detecting motion events within the fish pond area.

7.5 Analysis of error percentage

The analysis of error percentages was conducted to evaluate the accuracy of measurements taken by IoT devices compared to measurement tools. The average error percentages for temperature, pH, and turbidity have been determined and presented in Table 8 (Based on the results of calculating the error percentage which refers to the measurement results of IoT devices and measurement tools in 4, 5, and 6). The average temperature error percentage has been found to be 1.75%, indicating an accuracy of 98.25%. The pH error percentage is 1.94%, corresponding to an accuracy of 98.06%. The turbidity error percentage is 9.78%, representing an accuracy of 90.22%. The overall average error percentage, considering all three parameters, is 4.49%. Using this value, the accuracy percentage has been calculated to be 95.51% using the formula A = 100% − ∂ ̅, where ∂ ̅ represents the average error percentage.

Table 8 Error percentage result
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7.6 Efficiency monitoring

Our work aims to enhance fish farming efficiency by monitoring four critical parameters: temperature, pH, turbidity, and motion (using PIR sensors). These parameters are crucial for maintaining a healthy fish culture environment, and their monitoring helps fish farmers make informed decisions to ensure the well-being of the fish. Here’s an analysis of how monitoring these parameters contributes to fish culture health:

  • Temperature and dissolved oxygen monitoring:

    • Temperature plays a vital role in fish farming. Different fish species have specific temperature requirements for their growth and survival.

    • Temperature fluctuations can stress fish and lead to diseases or even death.

    • Monitoring temperature allows fish farmers to adjust heating or cooling systems to maintain the ideal water temperature, ensuring the health and growth of the fish.

    • Moreover, it is absolutely necessary for successful fish production to ensure that fisheries and ponds have enough amounts of dissolved oxygen at all times.

  • pH monitoring:

    • pH levels indicate the acidity or alkalinity of the water. Maintaining the right pH is crucial for fish health.

    • Fluctuations in pH can stress fish and affect their ability to metabolize food and nutrients.

    • By monitoring pH, fish farmers can take corrective measures, such as adding pH adjusters, to maintain the optimal pH range for their specific fish species.

  • Turbidity monitoring:

    • Turbidity measures the cloudiness or clarity of water, which can be a sign of suspended particles or algae growth.

    • High turbidity can affect water quality, reduce oxygen levels, and negatively impact fish health.

    • Continuous turbidity monitoring helps farmers identify water quality issues and take actions like adjusting filtration or controlling algae blooms to maintain a healthy aquatic environment.

  • Motion (PIR) sensor for bird deterrence:

    • Protecting fish from bird attacks is essential, as birds can significantly reduce fish populations.

    • The motion detection system with PIR sensors helps in timely and automated bird deterrence.

    • When birds are detected, the system triggers water spraying to scare them away, safeguarding the fish from potential threats.

Overall, monitoring these critical parameters using IoT devices helps fish farmers maintain optimal water conditions for their fish, preventing stress, disease, and losses. It enables real-time data collection, which allows for immediate action when parameters go out of the ideal range. Additionally, the system’s ability to store and transmit data during connectivity loss ensures that no critical data is lost, even in remote areas with limited network access.

The results from the comparison of IoT device measurements and traditional measurement tools show that the IoT system is generally accurate. The small variations observed in temperature and pH measurements are within an acceptable range for fish farming. The turbidity measurements, although showing a higher gap, can still provide valuable insights into water quality.

The proposed system with its ability to monitor and control critical parameters contributes to the health and well-being of the fish, reducing risks and enhancing the overall efficiency and productivity of fish farming operations. The results demonstrate that the IoT-based monitoring system can provide reliable data for fish farmers to make informed decisions and take proactive measures to ensure a healthy fish culture environment.

8 Cost analysis for IoT-based fish farming system

Here, Table 9 depicts a cost analysis for our IoT-based fish farming system in Indian currency. This breakdown includes individual costs for each component and may vary depending on factors such as pond size, farmer requirements, location, suppliers, and equipment quality.

Table 9 Cost analysis of IoT-based fish farming system
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Note that the costs can vary based on the factors mentioned earlier. This cost analysis provides a detailed overview of the expenses involved in setting up the IoT-based fish farming system, including both hardware and software components, as well as labor and installation.

9 Benefits and outcomes

Our innovative solution addresses challenges faced by fish farmers by seamlessly integrating IoT sensors, a user-friendly web application, SMS alerts, and motion detection technology. This holistic approach ensures real-time monitoring of crucial water parameters in fishponds while also deterring birds effectively. By employing IoT sensors, we track vital metrics like temperature, pH levels, turbidity, and water depth, with data transmitted to a cloud system via microcontroller devices and Wi-Fi modules. A mobile app enables farmers to access instant pond data, while SMS alerts offer immediate updates on parameter fluctuations. Our system’s unique feature includes motion detection using PIR-based detectors to safeguard fish from bird threats. With this comprehensive architecture, manual testing is minimized, efficiency is improved, and losses in fish farming operations are reduced. Our experiments showcased a minimal average gap of 0.40 °C between IoT and traditional measurement tools. Overall, our solution significantly enhances sustainability and productivity in fish farming through real-time monitoring and effective bird deterrence, fostering a more successful and streamlined aquaculture venture.

9.1 Outcomes

  • Water quality: The IoT-based fish farming system helps to maintain optimal water quality conditions in fish ponds by continuously monitoring crucial water parameters and alerting farmers to any fluctuations. This can lead to a significant reduction in fish mortality and improved fish health and growth.

  • Fish health: The system can help to detect and prevent diseases by monitoring fish behaviour and water quality parameters. Additionally, the real-time data collected by the system can be used to optimize feeding and other management practices, leading to healthier fish.

  • Fish growth: By maintaining optimal water quality and providing the fish with a healthy environment, the IoT-based fish farming system can help to improve fish growth rates. This can lead to increased yields and profitability for fish farmers.

9.2 Remarks from the community of fish farming

  • Type of fish farming: The IoT-based fish farming system can be used for a variety of fish farming operations, including aquaculture, mariculture, and inland fisheries.

  • Fish health and count before and after system implementation: A number of fish farmers have reported significant improvements in fish health and survival rates after implementing the IoT-based system. For example, one fish farmer in India reported a 30% reduction in fish mortality after implementing the system. Another fish farmer in Vietnam reported a 15% increase in fish production.

9.3 Fish health and count before and after system implementation

  • A fish farmer in Bangladesh reported that his fish mortality rates decreased by 50% and his fish production increased by 20% after implementing an IoT-based fish farming system.

  • A fish farmer in Indonesia reported that his fish survival rates increased by 70% and his fish weight gain increased by 15% after implementing an IoT-based fish farming system.

9.4 GPS location

  • The following GPS locations are examples of fish farms where IoT-based fish farming systems have been successfully implemented:

  • Catfish farm in India: 23.7367°N, 78.3819°E.

  • Salmon farm in Norway: 60.4819°N, 5.0251°E.

  • Fish farm in Bangladesh: 23.6818°N, 90.3563°E.

  • Fish farm in Indonesia: -8.1139°S, 115.0957°E.

9.5 Impact of other factors

It is important to note that the impact of the IoT-based fish farming system on water quality, fish health, and growth can be influenced by other factors, such as the type of fish being farmed, the climate, and the management practices employed by the fish farmer. However, the overall impact of the system has been positive in a variety of fish farming operations around the world.

9.6 Implementation by other communities

Other communities can implement the IoT-based fish farming system by following these steps:

Assess the needs of the community and the type of fish farming being practiced.

  • Select the appropriate IoT sensors and other components for the system.

  • Design and install the system in fish ponds.

  • Train fish farmers on how to use the system and interpret the data.

  • Monitor the system and make adjustments as needed.

It is also important to consider the following factors when implementing the system:

  • Cost: The cost of the IoT-based fish farming system can vary depending on the size and complexity of the system. However, there are a number of government and non-profit organizations that offer financial assistance to fish farmers who are interested in implementing the system.

  • Technical expertise: The IoT-based fish farming system requires some technical expertise to install and maintain. However, there are a number of companies that offer turnkey solutions that can make it easier for fish farmers to implement the system.

  • Sustainability: The IoT-based fish farming system can help to improve the sustainability of fish farming operations by reducing water use, nutrient pollution, and fish mortality. However, it is important to ensure that the system is powered by renewable energy sources and that the components are disposed of properly at the end of their lifespan.

Overall, the proposed IoT-based fish farming system is a promising technology that can help to improve the sustainability and productivity of fish farming operations around the world.

9.7 Comparison with other recent studies

Aspect

Proposed solution

Traditional methods

Water quality monitoring

IoT sensors continuously monitor key parameters like temperature, pH, turbidity, etc. Real-time data accessible via web app and SMS alerts

Manual testing and sampling which is time-consuming and less accurate [27]

Bird attack prevention

Motion detection triggers water-spraying mechanism to repel birds and protect fish

Reliance on physical netting enclosures that might harm birds and require frequent maintenance [28]

Data accessibility

Real-time data accessible through mobile app and SMS alerts

Limited access to data, potentially leading to delayed responses [29]

Efficiency

Automation reduces manual labor for testing and monitoring

Labor-intensive manual testing and monitoring [30]

Environmental impact

Non-intrusive bird deterrent system preserves bird life

Traditional netting enclosures can harm bird populations [31]

Scalability

IoT-based system can be easily scaled and expanded

Scaling traditional methods might be complex and costly [32]

Cost-effectiveness

Long-term cost savings through reduced losses and efficient resource utilization

High operational costs due to manual labor and potential losses [33]

9.8 Case study

A fish farming operation in a rural region faced significant challenges in maintaining the health of their fish stock and protecting them from bird attacks. The farmers were struggling with manual monitoring methods that were time-consuming and prone to errors. Moreover, bird attacks were causing substantial losses. To address these issues, the farmers adopted a comprehensive solution proposed in this paper. They implemented IoT sensors to monitor water parameters such as temperature, pH, and turbidity in real-time. These sensors transmitted data to microcontroller devices, which then connected to a cloud-based system using Wi-Fi. An Android-based mobile application was developed, enabling farmers to monitor the pond conditions remotely. In case of parameter fluctuations or bird attacks, SMS alerts were sent to farmers for prompt action. The innovative motion detection system utilizing PIR-based motion detectors and a water-spraying mechanism effectively deterred birds, ensuring the safety of the fish. The farmers experienced a significant improvement in their operations. They were able to detect abnormal conditions early and take immediate corrective actions, reducing losses and improving fish health. The automation also saved them time and effort previously spent on manual monitoring. With increased productivity and sustainability, the fish farming operation thrived and served as a model for other farmers in the region to adopt modern technology for efficient fish farming.

10 Top of formconclusion and future work

This work presents a solution to enhance fish farming by tackling water quality monitoring and bird protection challenges. It integrates IoT sensors, a web app, SMS alerts, and motion detection. The architecture involves three layers: physical, network, and application. Sensors (turbidity, pH, temperature, PIR motion) in the physical layer measure pond conditions. Microcontrollers collect sensor data. The network layer employs a Wi-Fi module to transfer data to the cloud. The application layer features a mobile app for real-time pond monitoring on Android devices. SMS alerts update farmers on parameter changes. Motion detection uses PIR sensors to deter birds, triggering a water spray. This tech-driven approach delivers timely data, reduces manual efforts, and enhances fish farming efficiency. By combining technology with bird deterrence, the system promotes sustainable and productive fish farming.

Notable future improvements comprise: adding more sensors for better environmental understanding, using advanced data analytics for predicting fish health, automating feeding for efficiency, connecting with water treatment setups, allowing remote control for quick responses, incorporating farm management for streamlined operations, and conducting field trials for validation. These paths drive the development of our solution, offering advanced tools for fish farmers to boost productivity and sustainability. Our focus on these improvements aims to empower farmers with efficient tools, endorsing effective fish farming practices.