Smart Monitoring and Control Systems in Bioreactor-Based Aquaculture Water Treatment

Alebachew Molla

doi:doi:10.11648/j.ijssn.20251302.11

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| Peer-Reviewed

Smart Monitoring and Control Systems in Bioreactor-Based Aquaculture Water Treatment

Alebachew Molla^*

Published in International Journal of Sensors and Sensor Networks (Volume 13, Issue 2)

Received: 7 September 2025 Accepted: 18 September 2025 Published: 10 October 2025

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Abstract

This abstract presents a concise overview of smart monitoring and control systems for aquaculture water treatment. It highlights the critical safety and productivity challenges faced by aquaculture due to fluctuations in essential water quality parameters such as temperature, pH, dissolved oxygen, and ammonia. Traditional water quality monitoring methods are often labor-intensive and intermittent, risking suboptimal conditions and economic losses. The advent of Internet of Things based smart systems, integrating diverse sensors, cloud computing, and automated actuators, enables real-time, continuous water quality monitoring and dynamic control. These systems facilitate remote data access, efficient management, and rapid response to environmental changes, enhancing fish health and optimizing bioreactor performance. Furthermore, incorporation of artificial intelligence and machine learning offers predictive analytics that improve decision-making and enable proactive interventions. Practical deployments demonstrate significant benefits such as reduced labor costs, improved resource utilization, and enhanced sustainability. Challenges in sensor robustness, data security, and cost remain, but ongoing advances in low-cost, energy-efficient sensors and integrated biosensing technologies promise wider adoption. Overall, smart monitoring and control technologies represent a transformative step toward fully automated, data-driven aquaculture systems, promoting a sustainable blue economy while meeting the growing global demand for aquatic food resources. This review encompasses current technologies, applications, challenges, case studies, and future directions in this dynamic field, offering valuable insights for researchers, practitioners, and policy makers aiming to advance sustainable aquaculture water management.

Published in	International Journal of Sensors and Sensor Networks (Volume 13, Issue 2)
DOI	10.11648/j.ijssn.20251302.11
Page(s)	22-32
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2025. Published by Science Publishing Group

Keywords

Bioreactor-based Aquaculture, Water Quality Monitoring, Automated Control Systems, Electrochemical Sensors, Optical Sensors, Biosensors, Real-time Data Acquisition

1. Introduction

Monitoring and control are critical components in the successful operation of aquaculture bioreactors, as they ensure the maintenance of optimal water quality conditions essential for healthy aquatic organism growth and efficient bioreactor performance

[1, 2]

. Effective monitoring allows for real-time tracking of key parameters such as dissolved oxygen, pH, temperature, and nutrient concentrations, while control systems adjust these factors dynamically to sustain favorable environments and prevent toxic buildup

[1]

. The complexity of aquaculture bioreactors requires precise regulation to balance microbial activity, biofilm development, and waste treatment processes, directly impacting water quality and system sustainability

[3]

Maintaining optimal water quality remains a significant challenge due to the dynamic nature of biological and chemical processes in bioreactors. Fluctuations in environmental parameters, microbial community shifts, and accumulation of harmful metabolites can disrupt system stability, compromising aquatic organism health and productivity

[4]

. Traditional manual monitoring methods and intermittent sampling are labor-intensive and often insufficient to capture rapid changes

[1]

. Furthermore, integrating multiple control variables simultaneously complicates maintaining system equilibrium, emphasizing the need for advanced solutions that provide continuous, accurate data and responsive management

[5]

The emergence of smart technologies has revolutionized aquaculture water management by enabling integrated, automated monitoring and control systems

[6]

. Innovations such as Internet of Things (IoT)-enabled sensors, artificial intelligence models, and wireless communication platforms facilitate real-time, remote data acquisition and intelligent decision-making

[7]

. These smart systems enhance operational efficiency, reduce human error, and enable predictive management strategies that optimize bioreactor conditions while minimizing resource consumption and environmental impact

[8]

. The integration of these technologies marks a transformative step toward sustainable, scalable aquaculture practices with improved water treatment outcomes.

2. Key Parameters Monitored in Aquaculture Bioreactors

In aquaculture bioreactors, several key water quality parameters are continuously monitored to ensure a healthy and productive environment for aquatic organisms

[2]

. Dissolved oxygen (DO) is critical for fish respiration and microbial activity; low levels can lead to hypoxia and stress

[9]

. pH affects biological processes and chemical equilibria; deviations from optimal ranges can cause toxicity and reduce metabolic efficiency

[2]

. Temperature influences fish metabolism, growth rates, and microbial kinetics, making it vital to maintain within species-specific limits

[10]

. Ammonia, a toxic nitrogenous waste from fish metabolism, must be kept low to prevent physiological stress and mortality. Nitrate, a product of nitrification, is less toxic but should be monitored to assess biofilter performance and avoid long-term accumulation

[11]

. Turbidity impacts light penetration and can indicate suspended solids or microbial blooms affecting health and system function

[12]

. Salinity controls osmoregulation in aquatic species and influences microbial community structure in bioreactors

[13]

2.1. Dissolved Oxygen

Dissolved Oxygen (DO) is critical for the respiration of fish, shrimp, and other aquatic organisms in aquaculture. Fish require DO levels between 5-6 ppm (parts per million) to grow and thrive optimally. When DO drops below about 3 ppm, most aquatic organisms experience stress, and at extremely low levels, survival becomes impossible. DO is often the most important chemical parameter for water quality because it directly affects the health, growth, and survival of cultured species. The oxygen demand varies depending on species, size, activity (feeding, reproduction), and water temperature

[14]

. For example, larger fish consume more oxygen in total, but smaller fish consume more oxygen relative to their body weight. Aquaculture systems typically use aeration and oxygenation techniques along with DO sensors to maintain these optimal oxygen levels and avoid stressful or lethal conditions.

2.2. Temperature

Temperature plays a crucial role in aquaculture by directly influencing the metabolic rates of fish and other aquatic organisms, as well as affecting the solubility of oxygen in water. Optimal temperature control ensures species-specific growth rates and helps prevent thermal stress, which can impair immune function and increase disease susceptibility

[10]

. Fish and shrimp have specific temperature ranges in which they grow best and survive optimally. Deviations outside these ranges can lead to slower growth, reduced feed efficiency, and increased mortality. For example, many temperate fish species thrive in moderate temperature ranges (e.g., 12-25°C), while tropical species prefer warmer waters. Sudden temperature fluctuations can also induce stress responses that negatively impact health

[15]

2.3. pH

pH is a critical parameter in aquaculture because it directly influences the health of aquatic organisms and many biochemical processes in the water. Most freshwater aquatic organisms, including fish and shrimp, thrive in a pH range of about 6.5 to 8.5. This range supports optimal physiological functions and overall health. If pH drops below 5 or rises above 9 for extended periods, it can become harmful or lethal to many species. Fluctuations in pH also impact the toxicity of compounds like ammonia, which becomes more toxic at higher pH levels. Therefore, regular pH monitoring in aquaculture systems helps stabilize the environment and prevent conditions that can induce stress or mortality in cultured organisms

[16]

2.4. Ammonia and Nitrogen Compound

Ammonia (NH₃), nitrite (NO₂^-), and nitrate (NO₃^-) are important nitrogenous compounds that must be carefully monitored in aquaculture systems to prevent toxicity and maintain water quality. Ammonia is primarily produced as a waste product from the metabolism of aquatic organisms and the decomposition of uneaten feed and organic matter. It exists in two forms in water ionized ammonium (NH₄⁺) and the more toxic un-ionized ammonia (NH₃)

[17]

. Un-ionized ammonia is highly toxic even at low concentrations, causing damage to the gills and organs of fish and shrimp and potentially leading to death. In a biologically active system, beneficial bacteria in the biofilter convert ammonia into nitrite, which is also toxic and can impair the oxygen-carrying capacity of blood by converting hemoglobin to methemoglobin, causing a condition called brown blood disease

[18]

. Nitrite toxicity can weaken immune responses and increase susceptibility to secondary infections. Nitrite is further converted by another group of bacteria into nitrate, which is significantly less toxic but can still have negative effects on growth and survival if it accumulates to high levels over time. Excessive nitrate can also lead to environmental pollution if discharged untreated. Managing these nitrogen compounds through efficient biofiltration, regular water quality monitoring, and timely water exchange is critical for the health and productivity of aquaculture bioreactors. Preventing the harmful build-up of ammonia and nitrite is especially essential for maintaining a safe environment for cultured species

[19]

. This cycle of nitrogen transformation highlights the importance of monitoring and control to sustain aquaculture operations.

2.5. Turbidity

Turbidity in aquaculture refers to the cloudiness or haziness of water caused by suspended particles such as clay, organic matter, plankton, and uneaten feed. It is an important parameter because it affects water clarity, light penetration, and the overall health of aquatic animals. Moderate turbidity can sometimes be beneficial by providing shade, reducing excessive algal growth, and offering fish some protection from predators

[20]

. However, high turbidity levels especially from inorganic particles like clay can be harmful. Excessive turbidity can clog fish gills, leading to respiratory distress, reduce feeding efficiency by impairing visibility for sight-feeding species, stress aquatic organisms, and promote the spread of pathogens. Furthermore, high turbidity limits light penetration, which reduces photosynthesis by phytoplankton and can decrease dissolved oxygen levels, destabilizing water conditions. Organic turbidity from uneaten feed and waste also fuels bacterial growth, resulting in oxygen depletion and accumulation of harmful substances such as ammonia and nitrite. Therefore, controlling turbidity through proper feeding management, preventing erosion, and regular water quality monitoring is crucial to maintaining a healthy aquaculture environment

[21]

2.6. Salinity

Salinity is a crucial parameter in aquaculture systems, especially those culturing marine or brackish water species. It directly affects the osmoregulation process, which is how aquatic organisms maintain the balance of salts and water in their bodies. Proper salinity levels ensure overall health, growth, and survival of cultured species

[22]

. Marine species typically require salinity levels close to seawater, around 30-35 parts per thousand (ppt), while brackish water species thrive in intermediate salinities generally ranging from 10 to 30 ppt. Species differ in their salinity tolerance; for example, the black tiger shrimp (Penaeus monodon) prefers salinity between 15 and 30 ppt for optimal growth, but can survive in lower salinities temporarily. Fluctuations or inappropriate salinity levels can cause physiological stress, reduce feeding efficiency, impair growth, and increase vulnerability to diseases. For these reasons, monitoring and maintaining appropriate salinity is essential in aquaculture to support the osmoregulatory functions and ensure the productivity and welfare of the aquatic organisms being cultured

[23]

2.7. Microbial Load

Monitoring microbial load in aquaculture systems is crucial for maintaining system stability and controlling disease outbreaks. Both beneficial and pathogenic microbial populations affect water quality and the health of cultured organisms. Beneficial microbes play essential roles in nutrient recycling, organic matter decomposition, and biofiltration, while pathogenic microbes can cause infections and significant losses

[24]

. Effective monitoring involves identifying microbial diversity, abundance, and metabolic activity using a combination of methods such as culture-based techniques, molecular tools like 16S rRNA gene metabarcoding, and metabolic profiling. In recirculating aquaculture systems (RAS), operators typically monitor microbial load at critical control points like water intake, fish tanks, and treatment stages to ensure timely detection and management of microbial risks

[24]

. Rapid quantitative techniques, such as the rqmicro. COUNT system or fluorometric detection methods, enable near real-time assessment of viable bacterial populations, supporting proactive interventions. Overall, integrated microbial monitoring is vital for sustainable aquaculture management, helping to safeguard animal welfare and optimize production outcomes

[25]

Each parameter plays a significant role in maintaining water quality and fish health. For example, proper dissolved oxygen levels optimize aerobic bacteria efficiency for waste breakdown and support fish vitality

[14, 26]

. pH stability ensures enzymatic activities and ammonia toxicity mitigation. Temperature control prevents thermal stress and disease susceptibility. Monitoring ammonia and nitrate helps maintain nitrogen balance and prevents toxic buildup

[27]

. Turbidity and salinity measurements guide water clarity management and physiological adaptation for target species

[2]

. Together, these parameters provide a comprehensive picture of water quality, enabling timely interventions through smart monitoring and control systems to sustain optimal bioreactor conditions and ensure aquaculture success.

3. Smart Monitoring Technologies

Smart monitoring technologies in aquaculture bioreactors rely on diverse sensor types to measure critical water quality parameters accurately and in real time

[6]

. Electrochemical sensors detect changes in chemical concentrations, such as dissolved oxygen, pH, ammonia, and nitrate, through electrical signals generated by chemical reactions

[26]

. Optical sensors utilize light-based methods like fluorescence and absorbance to assess parameters including turbidity, microbial populations, and oxygen levels with high sensitivity

[27]

. Biosensors, which combine biological recognition elements with physical transducers, can detect specific biomolecules, toxins, or pathogens, supporting food safety and environmental compliance

[28, 29]

Wireless sensor networks and IoT-enabled systems form the backbone of smart aquaculture monitoring

[30, 31]

. These systems integrate multiple sensors into wireless modules that transmit data continuously to cloud platforms, enabling remote access, real-time visualization, and automated alerts for deviations

[32]

. Such setups reduce the need for manual sampling and enhance decision-making speed and accuracy. Multi-parameter integrated sensor platforms combine several sensing modalities in one device, simplifying installation and maintenance while providing comprehensive water quality assessment.

Download: Download full-size image

Figure 1. Submergible Cage Aquaculture System with Underwater Feeding Device and Remote Smartphone Control.

The real-time data acquisition and transmission capabilities of these technologies empower fish farmers and aquaculture managers to monitor environmental conditions dynamically and respond proactively. This continuous monitoring ensures optimized bioreactor performance, improves aquatic organism health, and minimizes operational costs

[33]

. As sensor miniaturization and connectivity improve, these smart monitoring technologies become increasingly accessible, laying the foundation for precision aquaculture practices driven by data and intelligent automation

[34, 35]

4. Data Management and Processing

Smart aquaculture monitoring systems increasingly rely on cloud computing platforms such as Thing Speak and Blynk to enhance data management and remote accessibility

[36]

. These platforms enable real-time data acquisition, storage, and visualization, allowing stakeholders to monitor water quality parameters continuously through user-friendly interfaces accessible via smartphones or computers

[37]

. Thing Speak offers robust cloud-based data storage and analytical tools, while Blynk (internet of things) provides interactive dashboards for remote control and immediate notification alerts, facilitating efficient system management

[38, 39]

Data visualization tools integrated into these platforms transform raw sensor data into clear, actionable insights using graphs, charts, and alerts that point to deviations in water quality

[40]

. These intuitive user interfaces support timely interventions and improve decision-making processes, especially when managing complex bioreactor environments with dynamic conditions

[41]

The integration of big data analytics and artificial intelligence (AI) further advances aquaculture water management by enabling pattern recognition and predictive capabilities from large datasets collected over time

[42]

. Machine learning algorithms analyze historical and real-time data to detect trends, anomalies, and correlations that human operators might overlook

[43]

. These AI-driven insights support optimized control strategies, help foresee potential system failures, and guide adaptive responses, thereby enhancing the sustainability and productivity of aquaculture bioreactors

[44]

. Together, cloud computing, data visualization, and AI integration form a powerful triad enabling sophisticated, data-driven management of bioreactor water quality, leading to smarter, more autonomous aquaculture practices

[45, 46]

5. Smart Control Systems

Smart control systems in aquaculture bioreactors employ automated actuators to manage key processes such as aeration, pH adjustment, feeding, and water exchange based on real-time sensor data

[6]

. These actuators dynamically regulate oxygen supply to maintain dissolved oxygen within optimal ranges, dose buffering agents to stabilize pH, dispense feed at scheduled intervals or based on fish behavior, and control water exchange to remove accumulated wastes. The automation significantly reduces manual labor and enhances operational precision

[47]

. Smart control systems in aquaculture bioreactors typically consist of the following key components: Smart Sensors, Automated Actuators, Communication Channels and Data Analytics and Machine Learning.

5.1. Smart Sensors

Smart sensors in aquaculture bioreactors are advanced devices designed to monitor a wide range of physical, chemical, physiological, and biochemical parameters crucial to maintaining optimal water quality and organism health. These include dissolved oxygen, temperature, pH, turbidity, ammonia, nitrate, nitrite, salinity, and microbial load

[48]

. Such sensors often incorporate on-board processing capabilities to analyse data locally, self-diagnostics to ensure accuracy and reliability, and communication features like wireless connectivity to transmit real-time data to control systems or centralized monitoring platforms. Technologies underpinning these sensors include electrochemical, optical, piezoelectric, and biosensors that detect specific reactions or environmental changes

[49]

. Integrated biosensing platforms combine microelectronics, nanotechnology, and bio-recognition elements to provide sensitive, continuous, and non-invasive monitoring. When embedded within IoT frameworks, these sensors enable precision aquaculture by facilitating real-time alerts, remote management, and data-driven decision-making to optimize feeding, aeration, and water treatment strategies, thus enhancing system resilience and profitability

[30]

5.2. Automated Actuators

Automated actuators in aquaculture bioreactors are essential devices that automatically adjust key system conditions based on real-time sensor data to maintain optimal water quality and environmental parameters. These actuators include valves that regulate water flow and exchanges, aerators that control oxygen levels by adjusting air or oxygen input, automated feeders that dispense feed precisely to match fish or shrimp demand, pumps for circulating and filtering water, and pH regulators that add chemicals to maintain suitable pH ranges

[3]

. Together, these actuators work within a closed-loop control system to continuously optimize aeration, feeding schedules, water exchange rates, pH balance, and temperature control, ensuring healthy aquatic organisms and efficient bioreactor performance. Automated control systems often incorporate programmable logic controllers (PLCs) or supervisory control and data acquisition (SCADA) systems to coordinate actuators based on sensor feedback, reducing manual intervention and minimizing risks from sudden environmental fluctuations

[50]

5.3. Communication Channels

Communication channels in aquaculture bioreactors utilize wired or wireless networks, including fieldbus protocols and IoT connectivity, to enable bidirectional, real-time data transfer between sensors, automated actuators, controllers (such as PLCs), and central management systems

[51]

. These networks facilitate seamless integration and coordination of system components, allowing sensor data on water quality parameters (e.g., dissolved oxygen, pH, temperature, ammonia) to be rapidly transmitted to controllers, which then send commands to actuators managing aeration, feeding, water exchange, and pH adjustment

[2]

. The communication infrastructure empowers operators with remote monitoring and control capabilities through centralized software platforms. This enhances operational efficiency by enabling timely responses to environmental changes, preventing system failures, and supporting predictive maintenance. Emerging technologies like 5G, LoRaWAN, and cloud computing further expand connectivity options, improving data reliability, scalability, and integration with advanced analytics for optimized aquaculture management

[52]

5.4. Data Analytics and Machine Learning

Advanced aquaculture bioreactor systems increasingly integrate data analytics and machine learning (ML) techniques to enhance operational efficiency and sustainability. Machine learning algorithms analyze large datasets from sensors to predict water quality trends, optimize feeding regimes, and forecast growth rates of cultured species dynamically

[53]

. For example, artificial neural networks (ANNs) and support vector machines (SVMs) have been used to model environmental parameters and automate real-time decision-making. These predictive models help improve feed conversion ratios, reduce waste, and minimize disease risks by detecting early signs of stress or contamination. ML also supports biomass estimation using computer vision and image processing, enabling non-invasive assessment of fish size and health. Additionally, AI-powered growth forecasting aids operators in scheduling harvests and adjusting management practices according to species-specific needs. The adoption of these technologies promises to increase productivity, reduce operational costs, and advance precision aquaculture practices

[54]

Download: Download full-size image

Figure 2. Smart Control system in aquaculture.

Feedback control loops form the core of these systems, where continuous monitoring of water quality parameters informs immediate adjustments through programmable logic controllers or microcomputers

[55]

. For example, dissolved oxygen sensors relay oxygen levels to a controller that adjusts aerator activity accordingly, ensuring oxygen concentration remains within required limits. Such closed-loop control improves system stability, energy efficiency, and aquatic organism health

[56]

AIoT (Artificial Intelligence of Things) systems in aquaculture pond management combine IoT-enabled sensors, cloud data processing, and AI-driven decision-making algorithms to optimize water quality control

[38, 57]

. These systems analyze data patterns to predict potential issues and autonomously adjust aeration or feeding rates

[39, 58]

. One patented system implements wireless programmable controllers linked to dissolved oxygen sensors and aerators, reducing human intervention while maintaining water quality above critical thresholds. These AIoT solutions represent a shift towards intelligent, autonomous aquaculture operations with improved productivity and sustainability

[59]

6. Case Studies and Applications

IoT-powered water quality monitoring systems have shown significant benefits in freshwater aquaculture, exemplified by applications in Mahseer and catfish farming

[6, 8, 60]

. In one study, an IoT system integrated with sensors for temperature, pH, dissolved oxygen, and water level used NodeMCU microcontrollers to provide real-time data and automated control

[61]

. This system enabled timely interventions such as water pumping to adjust parameters, significantly improving fish survival and health compared to conventional methods

[62]

. Remote monitoring via cloud platforms allowed farmers to access critical data anytime, reducing the risk of disease and optimizing growth conditions

[63]

Intelligent fish pond systems often incorporate multi-sensor arrays combined with wireless communication and cloud-based data management

[64, 65]

. These systems monitor water parameters continuously and facilitate remote control of actuators like aerators and pumps

[7]

. One example includes a comprehensive solution integrating sensors for temperature, pH, dissolved oxygen, conductivity, turbidity, and ammonia

[66]

. Data visualization dashboards enable users to monitor pond conditions in real-time and automate responses to unfavorable readings, thus enhancing operational efficiency and reducing labor costs

[66]

Advanced smart growth and health monitoring systems combine water quality sensing with imaging technologies for holistic aquaculture management

[6, 24]

. These platforms utilize cameras to monitor fish behavior and health, while sensors track water chemistry parameters

[67]

. The fusion of visual data and water quality metrics powered by AI algorithms allows for early disease detection, feeding optimization, and stress reduction

[68-70]

. This integrated approach strengthens the precision and effectiveness of aquaculture practices, driving improved productivity and sustainability.

7. Benefits and Challenges

Smart monitoring and control systems in aquaculture significantly improve water quality management and fish health by enabling continuous, real-time tracking and automated adjustment of critical parameters

[71]

. This proactive approach reduces instances of stress and disease in aquatic organisms, promoting better growth rates and higher survival

[72]

. Moreover, automation and remote monitoring cut down labor demands and operational costs by minimizing manual sampling, interventions, and system failures

[73]

However, challenges remain in sensor accuracy, cost, and environmental robustness. Sensors must deliver reliable data over long periods despite biofouling, temperature fluctuations, and chemical interferences, which can degrade performance

[6, 8]

. High-quality sensing devices and their integration into smart systems can be expensive, limiting wider adoption, especially in small-scale farms. Additionally, data security and privacy concerns arise as IoT platforms involve the transmission and storage of potentially sensitive farm data

[74]

. Ensuring secure communication channels, protecting user data, and safeguarding against cyber threats are critical to maintaining trust and functionality in smart aquaculture systems

[75]

. Overall, while smart technologies offer transformative benefits for aquaculture water treatment and management, addressing technical, economic, and security challenges is key to realizing their full potential and fostering sustainable industry growth

[8]

8. Future Directions

Future advances in aquaculture bioreactor monitoring and control are likely to focus on the development of low-cost, energy-efficient sensors that provide reliable real-time data with minimal maintenance

[6, 19, 59]

. These sensors will be more accessible to a broader range of aquaculture operations, including small-scale farms, reducing economic barriers to smart management

[6, 33, 76]

A promising direction is the integration of biosensors and omics-based monitoring, enabling comprehensive assessment of both environmental parameters and biological factors such as microbial communities and pathogen presence

[18-20, 77]

. Biosensors can detect physiological signals from sentinel animals or specific biomolecules, providing rapid insight into animal health and ecosystem status

[49]

. Omics approaches (e.g., genomics, transcriptomics) combined with biosensor data will enhance precision monitoring and early-warning capabilities.

Enhanced predictive analytics using artificial intelligence and machine learning will further empower proactive management by analyzing complex datasets to forecast water quality fluctuations, disease outbreaks, and optimal feeding schedules

[39, 62, 78, 79]

. These tools will support decision-making and autonomous system adjustments to optimize aquaculture performance.

The expansion of smart aquaculture towards full automation is anticipated, incorporating closed-loop control systems, robotic interventions, and integration of circular economy principles

[80]

. This includes sustainable resource use, waste recycling, and energy efficiency to create environmentally friendly, economically viable aquaculture systems that align with global sustainability goals

[81, 82]

. Together, these future trends will drive a transformative, data-driven aquaculture industry that maximizes productivity while minimizing environmental impacts through the fusion of advanced sensing, biological monitoring, AI, and sustainable practices.

9. Conclusion

Smart monitoring and control technologies have revolutionized aquaculture bioreactor water treatment by enabling continuous, real-time tracking and automated management of critical water quality parameters. These technologies integrate advanced sensor systems, IoT platforms, cloud computing, artificial intelligence, and automated actuators to optimize environmental conditions, improve fish health, and enhance bioreactor efficiency. The ability to remotely monitor and control aquaculture systems not only reduces labor and operational costs but also allows proactive interventions that minimize risks related to water quality fluctuations and disease.

By advancing precision aquaculture practices, these smart systems contribute significantly to the sustainability and productivity of aquaculture operations. They support resource-efficient management aligned with environmental conservation and circular economy principles. As sensor affordability improves and AI-driven predictive analytics mature, the role of smart monitoring and control systems will continue to expand, driving full automation and intelligent decision-making in aquaculture.

The future of smart monitoring and control systems in bioreactor-based aquaculture water treatment will be shaped by advances in sensor technology, AI, IoT, and data analytics, enabling precise real-time management of water quality and microbial processes. Industry trends lean toward sustainability, automation, and remote system control, supported by big data and cloud platforms. Challenges include high costs, technical complexity, data security, and sensor reliability. Solutions involve cost-effective modular designs, standardization, AI-driven decision support, and collaboration among stakeholders. These developments promise to enhance aquaculture productivity, animal health, and environmental sustainability through innovative water treatment technologies.

Abbreviations

AI	Artificial Intelligence
AIoT	Artificial Intelligence of Things
DO	Dissolved oxygen
IoT	Internet of Things

Author Contributions

Alebachew Molla is the sole author. The author read and approved the final manuscript.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

No new data were created or analyzed in this review.

Funding

This review received no external funding.

Conflicts of Interest

The author declares no conflicts of interest.

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Molla, A. (2025). Smart Monitoring and Control Systems in Bioreactor-Based Aquaculture Water Treatment. International Journal of Sensors and Sensor Networks, 13(2), 22-32. https://doi.org/10.11648/j.ijssn.20251302.11

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Molla, A. Smart Monitoring and Control Systems in Bioreactor-Based Aquaculture Water Treatment. Int. J. Sens. Sens. Netw. 2025, 13(2), 22-32. doi: 10.11648/j.ijssn.20251302.11

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Molla A. Smart Monitoring and Control Systems in Bioreactor-Based Aquaculture Water Treatment. Int J Sens Sens Netw. 2025;13(2):22-32. doi: 10.11648/j.ijssn.20251302.11

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@article{10.11648/j.ijssn.20251302.11,
  author = {Alebachew Molla},
  title = {Smart Monitoring and Control Systems in Bioreactor-Based Aquaculture Water Treatment
},
  journal = {International Journal of Sensors and Sensor Networks},
  volume = {13},
  number = {2},
  pages = {22-32},
  doi = {10.11648/j.ijssn.20251302.11},
  url = {https://doi.org/10.11648/j.ijssn.20251302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssn.20251302.11},
  abstract = {This abstract presents a concise overview of smart monitoring and control systems for aquaculture water treatment. It highlights the critical safety and productivity challenges faced by aquaculture due to fluctuations in essential water quality parameters such as temperature, pH, dissolved oxygen, and ammonia. Traditional water quality monitoring methods are often labor-intensive and intermittent, risking suboptimal conditions and economic losses. The advent of Internet of Things based smart systems, integrating diverse sensors, cloud computing, and automated actuators, enables real-time, continuous water quality monitoring and dynamic control. These systems facilitate remote data access, efficient management, and rapid response to environmental changes, enhancing fish health and optimizing bioreactor performance. Furthermore, incorporation of artificial intelligence and machine learning offers predictive analytics that improve decision-making and enable proactive interventions. Practical deployments demonstrate significant benefits such as reduced labor costs, improved resource utilization, and enhanced sustainability. Challenges in sensor robustness, data security, and cost remain, but ongoing advances in low-cost, energy-efficient sensors and integrated biosensing technologies promise wider adoption. Overall, smart monitoring and control technologies represent a transformative step toward fully automated, data-driven aquaculture systems, promoting a sustainable blue economy while meeting the growing global demand for aquatic food resources. This review encompasses current technologies, applications, challenges, case studies, and future directions in this dynamic field, offering valuable insights for researchers, practitioners, and policy makers aiming to advance sustainable aquaculture water management.
},
 year = {2025}
}

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TY  - JOUR
T1  - Smart Monitoring and Control Systems in Bioreactor-Based Aquaculture Water Treatment

AU  - Alebachew Molla
Y1  - 2025/10/10
PY  - 2025
N1  - https://doi.org/10.11648/j.ijssn.20251302.11
DO  - 10.11648/j.ijssn.20251302.11
T2  - International Journal of Sensors and Sensor Networks
JF  - International Journal of Sensors and Sensor Networks
JO  - International Journal of Sensors and Sensor Networks
SP  - 22
EP  - 32
PB  - Science Publishing Group
SN  - 2329-1788
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AB  - This abstract presents a concise overview of smart monitoring and control systems for aquaculture water treatment. It highlights the critical safety and productivity challenges faced by aquaculture due to fluctuations in essential water quality parameters such as temperature, pH, dissolved oxygen, and ammonia. Traditional water quality monitoring methods are often labor-intensive and intermittent, risking suboptimal conditions and economic losses. The advent of Internet of Things based smart systems, integrating diverse sensors, cloud computing, and automated actuators, enables real-time, continuous water quality monitoring and dynamic control. These systems facilitate remote data access, efficient management, and rapid response to environmental changes, enhancing fish health and optimizing bioreactor performance. Furthermore, incorporation of artificial intelligence and machine learning offers predictive analytics that improve decision-making and enable proactive interventions. Practical deployments demonstrate significant benefits such as reduced labor costs, improved resource utilization, and enhanced sustainability. Challenges in sensor robustness, data security, and cost remain, but ongoing advances in low-cost, energy-efficient sensors and integrated biosensing technologies promise wider adoption. Overall, smart monitoring and control technologies represent a transformative step toward fully automated, data-driven aquaculture systems, promoting a sustainable blue economy while meeting the growing global demand for aquatic food resources. This review encompasses current technologies, applications, challenges, case studies, and future directions in this dynamic field, offering valuable insights for researchers, practitioners, and policy makers aiming to advance sustainable aquaculture water management.

VL  - 13
IS  - 2
ER  -

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Alebachew Molla

Department of Biotechnology, Wolaita Sodo University, Wolaita, Ethiopia

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Molla, A. (2025). Smart Monitoring and Control Systems in Bioreactor-Based Aquaculture Water Treatment. International Journal of Sensors and Sensor Networks, 13(2), 22-32. https://doi.org/10.11648/j.ijssn.20251302.11

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Molla, A. Smart Monitoring and Control Systems in Bioreactor-Based Aquaculture Water Treatment. Int. J. Sens. Sens. Netw. 2025, 13(2), 22-32. doi: 10.11648/j.ijssn.20251302.11

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Molla A. Smart Monitoring and Control Systems in Bioreactor-Based Aquaculture Water Treatment. Int J Sens Sens Netw. 2025;13(2):22-32. doi: 10.11648/j.ijssn.20251302.11

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@article{10.11648/j.ijssn.20251302.11,
  author = {Alebachew Molla},
  title = {Smart Monitoring and Control Systems in Bioreactor-Based Aquaculture Water Treatment
},
  journal = {International Journal of Sensors and Sensor Networks},
  volume = {13},
  number = {2},
  pages = {22-32},
  doi = {10.11648/j.ijssn.20251302.11},
  url = {https://doi.org/10.11648/j.ijssn.20251302.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijssn.20251302.11},
  abstract = {This abstract presents a concise overview of smart monitoring and control systems for aquaculture water treatment. It highlights the critical safety and productivity challenges faced by aquaculture due to fluctuations in essential water quality parameters such as temperature, pH, dissolved oxygen, and ammonia. Traditional water quality monitoring methods are often labor-intensive and intermittent, risking suboptimal conditions and economic losses. The advent of Internet of Things based smart systems, integrating diverse sensors, cloud computing, and automated actuators, enables real-time, continuous water quality monitoring and dynamic control. These systems facilitate remote data access, efficient management, and rapid response to environmental changes, enhancing fish health and optimizing bioreactor performance. Furthermore, incorporation of artificial intelligence and machine learning offers predictive analytics that improve decision-making and enable proactive interventions. Practical deployments demonstrate significant benefits such as reduced labor costs, improved resource utilization, and enhanced sustainability. Challenges in sensor robustness, data security, and cost remain, but ongoing advances in low-cost, energy-efficient sensors and integrated biosensing technologies promise wider adoption. Overall, smart monitoring and control technologies represent a transformative step toward fully automated, data-driven aquaculture systems, promoting a sustainable blue economy while meeting the growing global demand for aquatic food resources. This review encompasses current technologies, applications, challenges, case studies, and future directions in this dynamic field, offering valuable insights for researchers, practitioners, and policy makers aiming to advance sustainable aquaculture water management.
},
 year = {2025}
}

Copy | Download

TY  - JOUR
T1  - Smart Monitoring and Control Systems in Bioreactor-Based Aquaculture Water Treatment

AU  - Alebachew Molla
Y1  - 2025/10/10
PY  - 2025
N1  - https://doi.org/10.11648/j.ijssn.20251302.11
DO  - 10.11648/j.ijssn.20251302.11
T2  - International Journal of Sensors and Sensor Networks
JF  - International Journal of Sensors and Sensor Networks
JO  - International Journal of Sensors and Sensor Networks
SP  - 22
EP  - 32
PB  - Science Publishing Group
SN  - 2329-1788
UR  - https://doi.org/10.11648/j.ijssn.20251302.11
AB  - This abstract presents a concise overview of smart monitoring and control systems for aquaculture water treatment. It highlights the critical safety and productivity challenges faced by aquaculture due to fluctuations in essential water quality parameters such as temperature, pH, dissolved oxygen, and ammonia. Traditional water quality monitoring methods are often labor-intensive and intermittent, risking suboptimal conditions and economic losses. The advent of Internet of Things based smart systems, integrating diverse sensors, cloud computing, and automated actuators, enables real-time, continuous water quality monitoring and dynamic control. These systems facilitate remote data access, efficient management, and rapid response to environmental changes, enhancing fish health and optimizing bioreactor performance. Furthermore, incorporation of artificial intelligence and machine learning offers predictive analytics that improve decision-making and enable proactive interventions. Practical deployments demonstrate significant benefits such as reduced labor costs, improved resource utilization, and enhanced sustainability. Challenges in sensor robustness, data security, and cost remain, but ongoing advances in low-cost, energy-efficient sensors and integrated biosensing technologies promise wider adoption. Overall, smart monitoring and control technologies represent a transformative step toward fully automated, data-driven aquaculture systems, promoting a sustainable blue economy while meeting the growing global demand for aquatic food resources. This review encompasses current technologies, applications, challenges, case studies, and future directions in this dynamic field, offering valuable insights for researchers, practitioners, and policy makers aiming to advance sustainable aquaculture water management.

VL  - 13
IS  - 2
ER  -

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