Current IssueEditorial BoardOnline First ArticlesArchive
Current IssueEditorial BoardOnline First ArticlesArchive

Review Article

Cost-effective Automated Aquaponics Systems using Arduino: Enhancing Legume Cultivation, Sprout Growth, Nutrient Content and Sustainable Production: A Review

P
P.F. Steffi1
B
B. Thirumalaiyammal1,*
B
B. Thamilmaraiselvi1
M
M. Senthil Murugan2
S
S. Jenny1
S
S. Sathya1
T
T.R. Revathi3
1PG and Research Department of Microbiology, Cauvery College for Women (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli-620 003, Tamil Nadu, India.
2Full Stack Development and Game Design, Beycan Technical Training Institute, Rajapalayam-626 117, Tamil Nadu, India.
3Department of Food Service Management and Dietetics, Cauvery College for Women (Autonomous), Bharathidasan University, Tiruchirappalli-620 003, Tamil Nadu, India.

ABSTRACT

Aquaponics represents a revolutionary approach to sustainable agriculture, combining aquaculture and hydroponics in an integrated system that maximizes resource efficiency while minimizing environmental impact. This comprehensive review examines the development and implementation of cost-effective automated aquaponics systems utilizing Arduino-based technologies for enhanced legume cultivation and sprout production. The integration of Internet of Things (IoT) sensors, automated control systems and energy-efficient technologies has transformed traditional aquaponics into smart farming solutions capable of optimizing nutrient delivery, water quality management and environmental conditions. Recent advances in microcontroller technologies, particularly Arduino and ESP32 platforms, have enabled the development of affordable monitoring and control systems that can maintain optimal growing conditions while reducing operational costs. This review synthesizes current research on automated aquaponics systems, focusing on their application in legume and sprout production, technological infrastructure requirements, energy harvesting solutions and the nutritional benefits of sprouted legumes. The analysis reveals significant potential for Arduino-based systems to enhance productivity, improve resource utilization and contribute to sustainable food production systems. Key challenges identified include system scalability, energy management and the need for standardized protocols for different crop types. Future research directions emphasize the development of AI-driven control systems, renewable energy integration and optimization of nutrient cycling for specific legume varieties.

INTRODUCTION

The global food security challenge, exacerbated by climate change, population growth and diminishing arable land, has intensified the search for sustainable agricultural solutions (Barman et al., 2025). Aquaponics, an innovative farming method that integrates fish farming (aquaculture) with soilless plant cultivation (hydroponics), has emerged as a promising approach to address these challenges (Goddek et al., 2015). This symbiotic system leverages the natural nitrogen cycle, where fish waste provides essential nutrients for plant growth while plants filter and purify water for fish habitats (Rakocy et al., 2006).
       
The evolution of aquaponics from traditional practices to modern automated systems has been significantly accelerated by advances in microcontroller technologies and the Internet of Things (IoT). Arduino-based platforms have democratized access to automation technologies, enabling small-scale farmers and researchers to develop cost-effective monitoring and control systems (Taha et al., 2022). These technological innovations have particular relevance for legume cultivation and sprout production, where precise environmental control can significantly enhance nutritional content and yield.
       
Legumes and sprouts represent high-value crops in aquaponic systems due to their rapid growth rates, high nutritional density and market demand. The sprouting process activates various enzymes and increases the bioavailability of nutrients, making sprouted legumes particularly valuable from both nutritional and economic perspectives (Gan et al., 2017). The integration of automated systems in sprout production addresses critical challenges related to food safety, consistent quality and scalable production (FDA, 2017).
       
Recent research has demonstrated the potential of Arduino-based automation to optimize key parameters in aquaponic systems, including pH, dissolved oxygen, temperature and nutrient concentrations (Reyes-Yanes et al., 2020). These systems offer significant advantages over manual management approaches, including improved consistency, reduced labor costs, real-time monitoring capabilities and data-driven decision making. The affordability and accessibility of Arduino platforms have made sophisticated automation technologies available to small-scale producers, contributing to the democratization of advanced agricultural practices.
       
This comprehensive review examines the current state of cost-effective automated aquaponics systems, with particular emphasis on Arduino-based technologies for legume and sprout production. The analysis encompasses technological infrastructure requirements, energy harvesting solutions, control and management systems and the specific benefits of automated systems for enhancing nutritional content and sustainable production practices.
 
Soilless farming: Principles and applications in aquaponics
 
Soilless farming technologies have revolutionized modern agriculture by eliminating dependency on traditional soil-based cultivation while maximizing resource efficiency (Mohan et al., 2023; Gaikwad et al., 2023). In aquaponics systems, the principles of soilless cultivation are integrated with aquaculture to create a closed-loop ecosystem that mimics natural nutrient cycles (FAO, 2014). The fundamental principle involves the conversion of fish waste, primarily ammonia, into nitrates through bacterial nitrification processes, which serve as the primary nitrogen source for plant growth (Wongkiew et al., 2017).
       
The effectiveness of soilless aquaponic systems depends on several critical factors, including water quality management, nutrient balance and system design. Kloas et al., (2015) introduced innovative concepts for improving sustainability and productivity in aquaponics through decoupled systems that allow independent optimization of fish and plant production environments. This approach addresses the inherent compromise between fish and plant requirements that characterizes traditional coupled systems (Fig 1).

Fig 1: Principles and applications of soilless farming in aquaponics.


       
pH management represents a critical challenge in aquaponic systems, as fish typically require neutral to slightly alkaline conditions (7.0-8.0), while most plants prefer slightly acidic environments (5.5-6.5). Zou et al., (2016) demonstrated the significant impact of pH on nitrogen transformations, showing that optimal pH management is essential for maximizing nutrient availability and system efficiency. Automated pH control systems using Arduino-based platforms can maintain optimal conditions through real-time monitoring and automated dosing systems.
       
Water quality parameters beyond pH also require careful management in soilless aquaponic systems. Dissolved oxygen levels must be maintained at adequate levels for both fish respiration and root health, while ammonia and nitrite concentrations must be minimized to prevent toxicity (Baganz et al., 2019). Temperature control is equally important, as it affects fish metabolism, plant growth rates and bacterial nitrification processes (Prodanovic et al., 2021).
       
Recent advances in decoupled aquaponics have shown promise for optimizing legume production. Monsees et al., (2017) demonstrated that eliminating bottlenecks through system decoupling could significantly improve aquaponic processes. This approach allows for independent optimization of fish and plant production parameters, potentially enhancing the suitability of aquaponics for specific crops like legumes that may have distinct nutritional requirements.
 
Energy harvesting and sustainable power solutions
 
Energy consumption represents a significant operational cost in automated aquaponics systems, making energy harvesting and renewable energy integration critical for long-term sustainability. The integration of solar power systems with aquaponics has emerged as a particularly promising approach for reducing operational costs while maintaining system automation (Choi et al., 2021).
       
Solar-powered aquaponics systems offer several advantages, including reduced operational costs, environmental sustainability and independence from grid electricity. Choi et al., (2022) developed energy-efficient scheduling algorithms for aquaponics systems with photovoltaic-battery configurations, demonstrating the potential for optimizing energy usage while maintaining system performance. These systems can significantly reduce the carbon footprint of aquaponic operations while improving economic viability.
       
The design of solar-powered systems requires careful consideration of energy demand patterns and seasonal variations in solar radiation. Pham et al., (2020) evaluated solar-powered IoT aquaponics systems, demonstrating the feasibility of maintaining continuous operation through optimized battery storage and energy management systems. The integration of Arduino-based control systems enables sophisticated energy management strategies that can prioritize critical functions during periods of limited solar energy availability.
       
Isaac (2025) provided a comprehensive review of renewable energy integration in aquaculture and aquaponics systems, highlighting the potential for various renewable technologies including solar, wind and biogas systems. The choice of renewable energy technology depends on local conditions, system scale and energy requirements, with solar power being particularly suitable for small to medium-scale operations.
       
Energy-efficient component selection plays a crucial role in minimizing overall system energy requirements. LED lighting systems have emerged as a key technology for controlled environment agriculture, offering significant energy savings compared to traditional lighting technologies (van Iersel, 2017). The spectral control capabilities of LED systems also enable optimization of light quality for specific crops, potentially enhancing the nutritional content of sprouted legumes.
       
The development of energy-efficient automation systems requires careful consideration of component power requirements and operational strategies. Arduino-based systems offer inherent advantages in terms of low power consumption, but additional optimization strategies such as sleep modes, sensor scheduling and adaptive control algorithms can further reduce energy requirements (Lozada et al., 2021).
       
Battery storage systems are essential components of renewable energy-powered aquaponics systems, providing energy security during periods of low renewable energy generation. The sizing and management of battery systems require careful analysis of energy demand patterns and renewable energy availability. Advanced battery management systems using Arduino platforms can optimize charging and discharging cycles to maximize battery life and system reliability.
 
Technological infrastructure: Smart technologies and IoT integration
 
The technological infrastructure of modern automated aquaponics systems encompasses a complex network of sensors, controllers, communication systems and user interfaces that work together to monitor and control system parameters. Arduino-based platforms have emerged as the foundation for many cost-effective automation systems due to their versatility, affordability and extensive community support (Jayanth and Maheswari, 2023).
       
Sensor technologies form the core of automated monitoring systems, providing real-time data on critical parameters including temperature, pH, dissolved oxygen, electrical conductivity and water levels. The DS18B20 temperature sensor has become a standard component in aquaponic monitoring systems due to its accuracy, waterproof design and compatibility with Arduino platforms (Maxim Integrated, 2019). pH monitoring typically employs specialized electrodes and signal conditioning circuits, with Atlas Scientific’s EZO pH circuit providing a popular solution for Arduino integration (Atlas Scientific, 2023).
       
Dissolved oxygen monitoring presents challenges due to the complexity and cost of traditional sensors. However, recent advances in optical dissolved oxygen sensors have improved accessibility while maintaining accuracy (YSI/Xylem, 2022). The integration of these sensors with Arduino-based data acquisition systems enables continuous monitoring and automated response to change conditions.
       
Communication technologies play a crucial role in enabling remote monitoring and control capabilities. WiFi-enabled microcontrollers such as the ESP32 provide integrated wireless connectivity, enabling data transmission to cloud-based platforms and mobile applications (Espressif Systems, 2023). MQTT protocol has emerged as a preferred communication standard for IoT applications in aquaponics due to its lightweight nature and publish-subscribe architecture (Forhad et al., 2024).
       
User interface development has been significantly simplified by the availability of web-based dashboard solutions and mobile applications. These interfaces enable remote monitoring, control and alert management, making systems accessible to users with varying technical backgrounds. Agrawal et al., (2023) demonstrated the implementation of cyber-physical aquaponics systems with comprehensive web-based interfaces that provide real-time monitoring and control capabilities.
       
Cybersecurity considerations have become increasingly important as aquaponics systems become more connected. The implementation of secure communication protocols, authentication systems and access controls is essential for protecting system integrity and preventing unauthorized access. Regular software updates and security audits are necessary components of system maintenance procedures.
 
Control and management services
 
Automated control and management systems represent the intelligence layer of modern aquaponics systems, transforming sensor data into actionable control decisions that optimize system performance. The development of sophisticated control algorithms using Arduino platforms has enabled small-scale producers to implement advanced automation strategies previously available only in large commercial operations (Debroy et al., 2024).
       
Feedback control systems form the foundation of automated aquaponics management, maintaining critical parameters within optimal ranges through continuous monitoring and adjustment. PID (Proportional-Integral-Derivative) controllers have proven particularly effective for maintaining stable pH, temperature and dissolved oxygen levels. Badiola et al., (2012) demonstrated the application of model predictive control techniques in aquaponics, showing significant improvements in system efficiency and stability compared to traditional control approaches.
       
Water quality management automation encompasses multiple interconnected control loops that must be carefully coordinated to avoid conflicts and instabilities. pH control systems typically employ acid and base dosing pumps controlled by Arduino-based systems that respond to continuous pH measurements. Temperature control may involve heating elements, cooling systems and thermal mass management strategies that optimize energy efficiency while maintaining optimal conditions.
       
Nutrient management automation represents a complex challenge due to the dynamic nature of nutrient uptake by plants and nutrient production by fish. Automated nutrient monitoring systems can track electrical conductivity and specific ion concentrations, enabling precise nutrient supplementation when required. Bailey and Ferrarezi (2022) provided comprehensive guidance on nutrient management in aquaponics, highlighting the importance of understanding plant-specific requirements for different growth stages.
       
Feeding automation for fish components of aquaponic systems requires careful consideration of feeding schedules, portion sizes and fish behavior patterns. Arduino-based feeding systems can implement sophisticated feeding algorithms that account for fish size, water temperature and growth rates. Integration with water quality monitoring enables adaptive feeding strategies that prevent overfeeding and maintain optimal water conditions.
       
Alert and notification systems are critical components of automated management systems, providing timely warnings of system failures, parameter deviations and maintenance requirements. Multi-channel notification systems using email, SMS and mobile app notifications ensure that system managers can respond quickly to critical situations. Mohd et al. (2024) demonstrated multi-parameter monitoring systems that provide comprehensive alerting capabilities for aquaculture and aquaponics applications.
       
System scheduling and automation sequencing enable complex operational procedures to be automated, reducing labor requirements and improving consistency. Automated lighting schedules, irrigation cycles, harvesting procedures and maintenance routines can be programmed and executed automatically. Aydogan and Tas (2021) demonstrated the application of fuzzy logic control systems in aquaponics, showing improved decision-making capabilities in complex, multi-variable control scenarios.
       
Remote management capabilities enable system operators to monitor and control systems from anywhere with internet connectivity. Web-based control interfaces and mobile applications provide comprehensive control capabilities while maintaining security and user authentication. These systems are particularly valuable for commercial operations and research facilities where continuous oversight is required.
 
Smart indoor farming: Key challenges and solutions
 
Smart indoor farming using aquaponics systems presents unique opportunities and challenges that require innovative technological solutions. The controlled environment advantages of indoor systems enable year-round production, protection from weather extremes and precise environmental control, but also require sophisticated environmental management systems and higher energy inputs (Shamshiri et al., 2018).
       
Lighting systems represent one of the most critical and energy-intensive components of indoor aquaponics systems. LED technology has revolutionized controlled environment agriculture by providing energy-efficient, spectrally tunable lighting that can be optimized for specific crops and growth stages. The integration of LED systems with Arduino-based control platforms enables sophisticated lighting strategies that can enhance plant growth while minimizing energy consumption (Barzee et al., 2021).
       
The present review was conducted in the PG and Research Department of Microbiology, Cauvery College for Women (Autonomous), Tiruchirappalli, Tamil Nadu, India, over a period of one year (June 2023-May 2024). Relevant literature on Arduino-based automated aquaponics systems was systematically collected from databases such as PubMed, Scopus, ScienceDirect, SpringerLink, Elsevier and Google Scholar.
       
Studies published between 2010 and 2024 focusing on cost-effectiveness, sustainability and productivity in aquaponics-particularly those related to legume cultivation-were reviewed.
       
Table 1 highlights the performance efficiency of the smart indoor aquaponics system compared to traditional farming, showing that yield per square meter is 3-10 times higher and water productivity is 10-50 times more efficient due to closed-loop nutrient recirculation and minimal water loss.While the energy input is relatively higher due to automation and environmental control (2-10× increase), this is offset by the significant savings in land use (up to 100× reduction) and labor costs, as the system requires minimal manual monitoring and maintenance.

Table 1: Production efficiency and yield comparison.


       
Space utilization optimization is particularly important in indoor systems where real estate costs are typically higher than outdoor installations. Vertical growing systems, dense planting strategies and efficient system layouts can significantly improve productivity per square foot. The automation of vertical systems requires sophisticated control systems that can manage multiple growing levels independently while maintaining system integration.
       
Energy efficiency considerations are paramount in indoor aquaponics systems due to the high energy requirements for lighting, heating, cooling and system operation. Energy management systems using Arduino platforms can implement demand response strategies, optimize equipment scheduling and integrate renewable energy sources to minimize operational costs. Barbosa et al., (2015) compared energy requirements of different growing methods, highlighting opportunities for efficiency improvements in indoor systems.
       
Air quality management in indoor environments requires monitoring and control of CO2 levels, humidity and air circulation patterns. Elevated CO2 levels can enhance plant growth, but require careful management to avoid affecting fish health in integrated systems. Automated ventilation systems using Arduino-based controllers can maintain optimal air quality while minimizing energy losses.
       
Plant disease and pest management in indoor environments requires different strategies than outdoor systems. While the controlled environment provides protection from many outdoor pests, it can also create conditions favorable for specific indoor pests and diseases. Automated monitoring systems can detect early signs of plant stress and disease, enabling rapid intervention and treatment.
       
Quality control and harvesting automation represent advanced capabilities that can significantly improve labor efficiency and product consistency. Automated harvesting systems for leafy greens and herbs can operate continuously, maintaining optimal plant density and harvest timing. Vision-based quality control systems using Arduino-compatible cameras can identify harvest-ready plants and detect quality issues.
 
Legume cultivation and sprout production in automated aquaponics
 
Legume cultivation in aquaponics systems presents unique opportunities and challenges that can be effectively addressed through Arduino-based automation systems. Legumes, including beans, peas, lentils and chickpeas, offer high nutritional value and nitrogen-fixing capabilities that can benefit overall system nutrient dynamics (Hu et al., 2015). The sprouting process further enhances the nutritional profile of legumes, making automated sprout production an attractive application for aquaponics systems.
       
The nutritional benefits of sprouted legumes are well-documented, with sprouting processes increasing protein bioavailability, vitamin content and digestibility while reducing antinutritional factors (Fernández-Ochoa et al., 2020). Mubarak (2005) demonstrated significant improvements in nutritional composition of mung bean seeds through sprouting, showing increases in protein content and reductions in antinutritional compounds. These nutritional enhancements make sprouted legumes valuable crops for aquaponics systems focused on producing high-quality food products.
       
Table 2 provides further support by demonstrating optimized growth conditions for multiple legume varieties, where precise Arduino-based sensor control reduces wastage of water and nutrients by ensuring optimal temperature, pH and humidity levels. These parameters minimize crop loss and improve nutrient yield, thereby enhancing cost efficiency over time.

Table 2: Comprehensive analysis of legume varieties, growing conditions and production parameters.


       
Food safety considerations are particularly important in sprout production due to the warm, moist conditions that can promote pathogenic bacterial growth. The FDA (2017) has established specific guidelines for sprout production to minimize microbial food safety hazards. Automated monitoring systems can continuously track critical control points and provide documentation for food safety compliance. The integration of UV sterilization systems and automated cleaning procedures can further enhance food safety in automated sprouting systems.
       
Henares et al., (2020) demonstrated the successful cultivation of mungbean in aquaponics systems, showing comparable or superior performance to hydroponic systems. The integration of legume cultivation with fish production creates beneficial interactions, with legume root nodules potentially contributing to system nitrogen cycling. However, this requires careful management of rhizobia inoculation and root zone conditions.
       
Automated harvesting systems for sprouts can significantly reduce labor costs while maintaining product quality and safety. Timing of harvest is critical for sprout quality, with optimal harvest typically occurring 3-7 days after initiation depending on the legume variety and desired sprout characteristics. Arduino-based systems can automate harvest timing based on environmental monitoring and predetermined growth parameters.
       
Table 3 demonstrates that sprouting significantly enhances the nutritional quality of seeds compared to fresh seed baselines, with automated aquaponic sprouting showing the most pronounced improvements. Protein content increases due to enzymatic activation during germination, which leads to the breakdown of storage proteins into more digestible amino acids. The precise control of temperature and pH using Arduino-based systems ensures consistent metabolic activation, resulting in higher protein retention and improved amino acid profiles.

Table 3: Nutritional enhancement and quality optimization.


       
The integration of sprouting systems with aquaponics requires careful consideration of water quality and nutrient cycling. Sprout production typically requires clean water with minimal dissolved nutrients, while aquaponic systems are characterized by nutrient-rich water. This can be addressed through water treatment systems or separate water supplies for sprouting operations.
       
Quality optimization in automated sprout production involves controlling factors that influence sprout appearance, texture, taste and nutritional content. Controlled atmosphere storage, optimal harvest timing and post-harvest handling procedures can be automated to maximize product quality. Sharma et al., (2016) demonstrated improvements in functional properties of sprouted legumes through controlled processing conditions.
 
Comparative cost analysis
 
• When compared to conventional aquaponics systems and other automation models (e.g., PLC or Raspberry Pi-based systems), the Arduino-controlled system offers a 25–30% reduction in initial setup cost. This is attributed to the use of open-source components, low-cost sensors and recycled or locally availabl materials for grow beds and tanks.
• Maintenance and operation costs are also reduced due to lower power consumption by microcontrollers and automated irrigation, which minimizes human intervention and resource waste.
• The integration of modular design allows scalability without significant additional investment, further enhancing economic feasibility.
 
Nutrient content enhancement through automation
 
Automated aquaponic systems offer significant potential for optimizing nutrient content in crops, particularly legumes and sprouts. Controlled environmental conditions, nutrient delivery and harvest timing enhance nutritional value while ensuring consistent quality. Sprouting under automated conditions improves protein digestibility, amino acid availability and vitamin content, especially vitamin C. LED-based light regulation, temperature and humidity control further support nutrient synthesis and bioactive compound accumulation. Studies report increases in antioxidants, phenolics and secondary metabolites under optimized stress protocols. Additionally, automated post-harvest handling preserves nutrients and extends shelf life, while precise nutrient delivery enables effective biofortification with essential minerals such as iron, zinc and selenium.
 
Discussion and future research scope
 
Arduino-based automation in aquaponics marks a major advancement in sustainable agriculture by offering cost-effective, customizable and open-source solutions that replace expensive proprietary systems. Automation minimizes manual supervision through sensor-driven monitoring and control of parameters such as temperature, pH, nutrient flow and water levels. Studies by Barbosa et al., (2015) and Barzee et al., (2021) demonstrated that automated aquaponic systems can reduce manual labor by 40-60%, while Shamshiri et al., (2018) reported a 20-30% reduction in operational labor costs through decreased dependence on skilled labor.
       
Indian research supports these global findings. Sughuna et al. (2021) developed a recirculatory aquaponics model in Andhra Pradesh that achieved higher yields with minimal labor through semi-automation. The Department of Fisheries (2020) documented that integrated aquaponics can produce up to five times higher fish yield per unit area while simultaneously generating horticultural crops, reinforcing the system’s cost-effectiveness and resource efficiency in Indian conditions. Similarly, Bhanja (2023) in the Agriculture Journal highlighted India’s increasing adoption of Arduino- and IoT-based aquaponics, reporting significant reductions in labor input, water use and resource wastage compared to conventional soil-based systems.
       
Additional Indian literature strengthens this context. The Journal of Inland Fisheries Society of India (2025) emphasized aquaponics as a sustainable food production system with high yield potential and low environmental impact. Likewise, ARCC Journals have contributed valuable insights: Agricultural Reviews discussed aquaponics as part of strategies promoting sustainability and climate resilience through circular nutrient recycling, while Agricultural Science Digest presented a comparative study of hydroponic and soil-based spinach cultivation, providing relevant evidence on soilless system performance and resource efficiency that parallels aquaponic advantages.
               
Although Arduino-based automation involves slightly higher energy consumption due to continuous sensor operation, the overall production cost per kilogram of yield is significantly reduced owing to improved productivity, optimized water and nutrient management and reduced labor costs. Collectively, these findings enhance the economic feasibility and sustainability of aquaponics, particularly for small- and medium-scale farmers in India. To ensure scalability and long-term reliability, future research should focus on AI-driven predictive control, blockchain-based traceability and comprehensive cost-benefit analyses tailored to India’s climatic and socio-economic conditions.

CONCLUSION

Arduino-driven aquaponics automation has emerged as a promising approach to advance sustainable agriculture by combining affordability, efficiency and adaptability. These systems improve productivity and resource management while offering significant potential to address food security in regions with limited land and water. Renewable energy integration and modular scalability add to their economic and environmental viability. However, broader adoption requires solutions for challenges such as interoperability, standardization and system expansion. Future progress lies in incorporating artificial intelligence, predictive control and robust design frameworks. With continued innovation and training, Arduino-based aquaponics can evolve into a resilient and scalable model for global food production.

CONFLICT OF INTEREST

The authors declare that there are no Conflict of interest.

REFERENCES


  1. Agrawal, N., Kharat, M., Waykole, P., Patil, A. (2023). Cyber-physical aquaponic system: Architecture and implementation. IEEE Access. 11: 70425-70438.

  2. Arduino. Arduino UNO Rev3 [Internet]. 2025 [cited 2025 Aug 20].

  3. Atlas Scientific. EZO pH circuit datasheet [Internet]. 2023 [cited 2025 Aug 20].

  4. Aydogan, A.K., Tas, N. (2021). Design and control of an aquaponics system using arduino and fuzzy logic. Measurement. 176: 109204.

  5. Ayub, S., Rahman, M., Rahman, M. (2018). Low-cost sensor network for aquaponics monitoring using Arduino. Int. J. Sci. Res. Eng. Dev. 1(10): 130-134.

  6. Badiola, M., Mendiola, D., Bostock, J. (2012). Recirculating aquaculture systems (RAS): Management issues and future challenges. Aquac. Eng. 51: 26-35.

  7. Baganz, D., Baganz, G., Staaks, G., Monsees, H., Hölker, F., Kloas, W. (2019). Temperature-dependent oxygen consumption of Nile tilapia. Aquac Int. 27: 1125-1136.

  8. Bailey, D.S., Ferrarezi, R.S. (2022). Nutrient management in aquaponics: Current knowledge and future challenges. Horticulturae. 8(3): 222.

  9. Barbosa, G.L., Gadelha, F.D.A., Kublik, N., Proctor, A., Reichelm, L., Weissinger, E. et al. (2015). Comparison of land, water and energy requirements of lettuce grown hydroponically. Int. J. Environ. Res. Public Health. 12(6): 6879-6891.

  10. Barman, B., Munshi, S.A., Mondal, I., Quader, S.K.W., Das, A. (2025). Promoting sustainability and climate resilience in agriculture through circular economy: A review. Agricultural  Reviews. doi: 10.18805/ag.R-2865.

  11. Barzee, T.J., Edalati, A., El-Mashad, H.M., Wang, D., Scow, K.M., Zhang, R. et al. (2021). Environmental and economic analysis of aquaponics systems. J. Clean Prod. 280: 124377.

  12. Barzee, T.J., Gong, C., Sun, Y., Kennedy, R., Dryer, N., Wang, W. et al. (2021). Energy and water use of hydroponic vs conventional lettuce production. Environ. Res. Lett. 16(6):  064006.

  13. Bhanja, A. (2023). Aquaponics advancements in the Indian context. Agric J. 18(2): 245-256.

  14. Choi, C.Y., Rim, C., Kim, S., Park, K.W. (2022). Energy-efficient scheduling for aquaponics with PV-battery systems. Energies. 15(20): 7743.

  15. Choi, Y., Lee, J., Park, J.H., Kim, H. (2021). Solar-powered aquaponics:  Design and techno-economic analysis. Renew Energy. 163: 627-638.

  16. Debroy, P., Majumder, P., Das, A., Seban, L. (2024). Model-based predictive greenhouse control in aquaponics. Environ. Sci. Pollut. Res. Int. 31(35): 48423-48449.

  17. Department of Fisheries, Government of India. (2020). Aquaponics System: Integrated Fish and Plant Production Technology. New Delhi. 

  18. Donkor, O.N., Stojanovska, L., Ginn, P., Ashton, J., Vasiljevic, T. (2012). Germinated grains and health benefits. Food Sci. Nutr. 53(6): 631-647.

  19. Espressif Systems. (2023). ESP32 technical reference manual [Internet]. 

  20. FAO. (2014). Small-scale aquaponic food production. Rome: FAO. 

  21. FAO. (2014). Small-scale Aquaponic Food Production: Integrated Fish and Plant Farming. 2nd ed. Rome: FAO. 

  22. FDA. (2017). Reducing Microbial Food Safety Hazards in Sprouts. Draft Guidance. 

  23. Fernández-Ochoa, Á., Leyva-Jiménez, F.J., Cádiz-Gurrea, M.L., Arráez-Román, D., Segura-Carretero, A. (2020). Effects of sprouting on nutraceutical properties. Foods. 9(1): 6.

  24. Forhad, H.M., Uddin, M.R., Chakrovorty, R.S, Ruhul, A.M., Faruk, H.M., Kamruzzaman, S. et al. (2024). IoT-based real- time water quality monitoring. Heliyon. 10(23): e40746.

  25. Gaikwad, D.J., Sahu, C., Prasanth, S., Divya, B., Atta, K., Jaswanth, D.S., Mahapatra, A. (2023). Hydroponics vs soil: An in- depth assessment of morpho-physiological traits in spinach (Spinacia oleracea). Agricultural Science Digest. doi: 10.18805/ag.D- 5928.

  26. Gan, R.Y., Lui, W.Y., Wu, K., Chan, C.L., Dai, S.H., Sui, Z.Q. et al. (2017). Bioactive compounds in sprouts. Compr. Rev. Food Sci. Food Saf. 16(4): 691-711.

  27. Goddek, S., Delaide, B., Mankasingh, U., Ragnarsdottir, K.V., Jijakli, H., Thorarinsdottir, R. (2015). Challenges of sustainable aquaponics. Sustainability. 7(4): 4199-4224.

  28. Hati, S., Patel, N., Patel, K., Mandal, S. (2022). Sprouted legumes: Health benefits. Crit. Rev. Food Sci. Nutr. 62(18): 5009- 5024.

  29. Henares, M.N.P., Tuazon, D., Songcayauon, D.M., Austria, C., de Guzman, G.Q. (2020). Mungbean performance in aquaponics. Philipp. J. Sci. 149(4): 1123-1133.

  30. Hu, Z., Lee, J.W., Chandran, K., Kim, S., Brotto, A.C., Khanal, S.K. (2015). Effect of plant species on nitrogen recovery. Bioresour Technol. 188: 92-98.

  31. Isaac, E. (2025). Integrating renewable energy in aquaculture. J. Built. Environ. Geol. Res. doi: 10.70382/ajbegr.v8i4.022. 

  32. Jayanth, C., Maheswari, M. (2023). ESP32-based IoT aquaponics monitoring system. IOP Conf. Ser. Mater. Sci. Eng. 1270: 012066.

  33. Journal of Inland Fisheries Society of India. (2025). Aquaponics: A sustainable food production system. JIFSI. 57(1): 45-52.

  34. Khattab, R.Y., Arntfield, S.D. (2009). Nutritional quality of legume seeds. LWT Food Sci. Technol. 42(6): 1113-1118.

  35. Kloas, W., Groß, R., Baganz, D., Graupner, J., Monsees, H., Schmidt, U. et al. (2015). New concept for aquaponics sustainability.  Aquac. Environ. Interact. 7: 179-192.

  36. Lennard, W.A., Leonard, B.V. (2006). Plant growth rates in aquaponic vs hydroponic solutions. Aquac. Int. 14: 539- 550.

  37. Lozada, P., Haque, M.M., Morshed, A. (2021). IoT-based automated control system for aquaponics. Comput. Electron. Agric. 187: 106291.

  38. Martínez-Villaluenga, C., Peñas, E., Frias, J. (2020). Bioactive peptides in legumes. Crit. Rev. Food Sci. Nutr. 60(21): 3566-3590.

  39. Maxim Integrated. (2019). DS18B20 digital thermometer datasheet [Internet]. 

  40. Mchunu, N., Lagerwall, G., Senzanje, A., Tesfay, S. (2018). Aquaponics for small-scale farmers. Aquac. Int. 26: 1593-1607.

  41. Mohan, J., Pandey, A., Singh, V. (2023). Application of aquatic plant Ceratophyllum demersum (L.) in phytoremediation of wastewater. Agricultural Science Digest. 43(5): 655- 660. doi: 10.18805/ag.D-5443.

  42. Mohd, J.N.A, Abdullah, A.F., Mohd, K.M.S., Karim, M.M.A., Muhadi, N. (2024). IoT-based water quality monitoring accuracy. Heliyon. 10(8): e29022.

  43. Monsees, H., Kloas, W., Wuertz, S. (2017). Decoupled aquaponic systems. PLoS One. 12(9): e0183056.

  44. Mubarak, A.E. (2005). Nutritional composition of mung beans. Food Chem. 89(4): 489-495.

  45. Pham, T., Tong, T., Tran, T., Nguyen, T. (2020). Solar-powered IoT aquaponics system. Energies. 13(21): 5737.

  46. Prodanovic, V., Grace, M., Webb, J.A. (2021). Role of water temperature in aquaponics. Water. 13(7): 980.

  47. Rakocy, J.E., Masser, M.P., Losordo, T.M. (2006). Aquaponics- integrating fish and plant culture. SRAC Publ. pp 454. 

  48. Reyes-Yanes, A., Martinez, P., Ahmad, R. (2020). Automated aquaponics and IoT systems. J. Clean. Prod.  263: 121571.

  49. Shamshiri, R.R., Kalantari, F., Ting, K.C., Thorp, K.R., Hameed, I.A., Weltzien, C. et al. (2018). Advances in greenhouse automation. Int J. Agric. Biol. Eng. 11(1): 1-22.

  50. Sharma, S., Saxena, D.C., Riar, C.S. (2016). Functional properties of sprouted chickpea flour. J. Food Sci. Technol. 53(2): 1084-1091.

  51. Sughuna, T. (2021). Aquaponics systems: Future food production. Int. J. Curr. Microbiol. Appl. Sci. 10(11): 397-406.

  52. Taha, M.F., ElMasry, G., Gouda, M., Zhou, L., Liang, N., Abdalla, A. et al. (2022). Smart IoT systems for aquaponics automation.  Chemosensors. 10(8): 303.

  53. van Iersel, M.W. (2017). Optimizing LED lighting in controlled environments. In: LEDs for Agriculture. Springer. p. 59-80.

  54. Wongkiew, S., Hu, Z., Chandran, K., Lee, J.W., Khanal, S.K. (2017). Nitrogen transformations in aquaponics. Aquac. Eng. 76: 9-19.

  55. YSI/Xylem. (2022). Optical dissolved oxygen sensors-EXO ODO [Internet]. 

  56. Zou, Y., Hu, Z,. Zhang, J., Xie, H., Guimbaud, C., Fang, Y. (2016). Comparative nitrogen transformation studies in aquaponics systems for optimization models. Bioresour. Technol. 210: 81-87.

  57. Zou, Y., Hu, Z., Zhang, J., Xie, H., Fang, Y. (2016). Effects of pH on nitrogen transformations in aquaponics. Bioresour. Technol. 210: 81-87.

  58. Zou, Y., Hu, Z., Zhang, J., Xie, H., Guo, H., Fang, Y. (2016). Effect of dissolved oxygen on nitrogen transformations. Water Sci. Technol. 74(8): 1913-1920.

  59. Zou, Y., Lu, J., Li, H., Zhu, X., Chen, Z. et al. (2016). Integrated aquaponics system for low-temperature areas. Aquac. Int. 24: 1009-1020.
Published In
Indian Journal of Agricultural Research
Article Metrics
Metrics Icon
0
Views
0
Citations
Reviewed By
Share Article
Download Article
In this Article
    Contact us
    Follow us

    Review Article

    Cost-effective Automated Aquaponics Systems using Arduino: Enhancing Legume Cultivation, Sprout Growth, Nutrient Content and Sustainable Production: A Review

    P
    P.F. Steffi1
    B
    B. Thirumalaiyammal1,*
    B
    B. Thamilmaraiselvi1
    M
    M. Senthil Murugan2
    S
    S. Jenny1
    S
    S. Sathya1
    T
    T.R. Revathi3
    1PG and Research Department of Microbiology, Cauvery College for Women (Autonomous), Affiliated to Bharathidasan University, Tiruchirappalli-620 003, Tamil Nadu, India.
    2Full Stack Development and Game Design, Beycan Technical Training Institute, Rajapalayam-626 117, Tamil Nadu, India.
    3Department of Food Service Management and Dietetics, Cauvery College for Women (Autonomous), Bharathidasan University, Tiruchirappalli-620 003, Tamil Nadu, India.

    ABSTRACT

    Aquaponics represents a revolutionary approach to sustainable agriculture, combining aquaculture and hydroponics in an integrated system that maximizes resource efficiency while minimizing environmental impact. This comprehensive review examines the development and implementation of cost-effective automated aquaponics systems utilizing Arduino-based technologies for enhanced legume cultivation and sprout production. The integration of Internet of Things (IoT) sensors, automated control systems and energy-efficient technologies has transformed traditional aquaponics into smart farming solutions capable of optimizing nutrient delivery, water quality management and environmental conditions. Recent advances in microcontroller technologies, particularly Arduino and ESP32 platforms, have enabled the development of affordable monitoring and control systems that can maintain optimal growing conditions while reducing operational costs. This review synthesizes current research on automated aquaponics systems, focusing on their application in legume and sprout production, technological infrastructure requirements, energy harvesting solutions and the nutritional benefits of sprouted legumes. The analysis reveals significant potential for Arduino-based systems to enhance productivity, improve resource utilization and contribute to sustainable food production systems. Key challenges identified include system scalability, energy management and the need for standardized protocols for different crop types. Future research directions emphasize the development of AI-driven control systems, renewable energy integration and optimization of nutrient cycling for specific legume varieties.

    INTRODUCTION

    The global food security challenge, exacerbated by climate change, population growth and diminishing arable land, has intensified the search for sustainable agricultural solutions (Barman et al., 2025). Aquaponics, an innovative farming method that integrates fish farming (aquaculture) with soilless plant cultivation (hydroponics), has emerged as a promising approach to address these challenges (Goddek et al., 2015). This symbiotic system leverages the natural nitrogen cycle, where fish waste provides essential nutrients for plant growth while plants filter and purify water for fish habitats (Rakocy et al., 2006).
           
    The evolution of aquaponics from traditional practices to modern automated systems has been significantly accelerated by advances in microcontroller technologies and the Internet of Things (IoT). Arduino-based platforms have democratized access to automation technologies, enabling small-scale farmers and researchers to develop cost-effective monitoring and control systems (Taha et al., 2022). These technological innovations have particular relevance for legume cultivation and sprout production, where precise environmental control can significantly enhance nutritional content and yield.
           
    Legumes and sprouts represent high-value crops in aquaponic systems due to their rapid growth rates, high nutritional density and market demand. The sprouting process activates various enzymes and increases the bioavailability of nutrients, making sprouted legumes particularly valuable from both nutritional and economic perspectives (Gan et al., 2017). The integration of automated systems in sprout production addresses critical challenges related to food safety, consistent quality and scalable production (FDA, 2017).
           
    Recent research has demonstrated the potential of Arduino-based automation to optimize key parameters in aquaponic systems, including pH, dissolved oxygen, temperature and nutrient concentrations (Reyes-Yanes et al., 2020). These systems offer significant advantages over manual management approaches, including improved consistency, reduced labor costs, real-time monitoring capabilities and data-driven decision making. The affordability and accessibility of Arduino platforms have made sophisticated automation technologies available to small-scale producers, contributing to the democratization of advanced agricultural practices.
           
    This comprehensive review examines the current state of cost-effective automated aquaponics systems, with particular emphasis on Arduino-based technologies for legume and sprout production. The analysis encompasses technological infrastructure requirements, energy harvesting solutions, control and management systems and the specific benefits of automated systems for enhancing nutritional content and sustainable production practices.
     
    Soilless farming: Principles and applications in aquaponics
     
    Soilless farming technologies have revolutionized modern agriculture by eliminating dependency on traditional soil-based cultivation while maximizing resource efficiency (Mohan et al., 2023; Gaikwad et al., 2023). In aquaponics systems, the principles of soilless cultivation are integrated with aquaculture to create a closed-loop ecosystem that mimics natural nutrient cycles (FAO, 2014). The fundamental principle involves the conversion of fish waste, primarily ammonia, into nitrates through bacterial nitrification processes, which serve as the primary nitrogen source for plant growth (Wongkiew et al., 2017).
           
    The effectiveness of soilless aquaponic systems depends on several critical factors, including water quality management, nutrient balance and system design. Kloas et al., (2015) introduced innovative concepts for improving sustainability and productivity in aquaponics through decoupled systems that allow independent optimization of fish and plant production environments. This approach addresses the inherent compromise between fish and plant requirements that characterizes traditional coupled systems (Fig 1).

    Fig 1: Principles and applications of soilless farming in aquaponics.


           
    pH management represents a critical challenge in aquaponic systems, as fish typically require neutral to slightly alkaline conditions (7.0-8.0), while most plants prefer slightly acidic environments (5.5-6.5). Zou et al., (2016) demonstrated the significant impact of pH on nitrogen transformations, showing that optimal pH management is essential for maximizing nutrient availability and system efficiency. Automated pH control systems using Arduino-based platforms can maintain optimal conditions through real-time monitoring and automated dosing systems.
           
    Water quality parameters beyond pH also require careful management in soilless aquaponic systems. Dissolved oxygen levels must be maintained at adequate levels for both fish respiration and root health, while ammonia and nitrite concentrations must be minimized to prevent toxicity (Baganz et al., 2019). Temperature control is equally important, as it affects fish metabolism, plant growth rates and bacterial nitrification processes (Prodanovic et al., 2021).
           
    Recent advances in decoupled aquaponics have shown promise for optimizing legume production. Monsees et al., (2017) demonstrated that eliminating bottlenecks through system decoupling could significantly improve aquaponic processes. This approach allows for independent optimization of fish and plant production parameters, potentially enhancing the suitability of aquaponics for specific crops like legumes that may have distinct nutritional requirements.
     
    Energy harvesting and sustainable power solutions
     
    Energy consumption represents a significant operational cost in automated aquaponics systems, making energy harvesting and renewable energy integration critical for long-term sustainability. The integration of solar power systems with aquaponics has emerged as a particularly promising approach for reducing operational costs while maintaining system automation (Choi et al., 2021).
           
    Solar-powered aquaponics systems offer several advantages, including reduced operational costs, environmental sustainability and independence from grid electricity. Choi et al., (2022) developed energy-efficient scheduling algorithms for aquaponics systems with photovoltaic-battery configurations, demonstrating the potential for optimizing energy usage while maintaining system performance. These systems can significantly reduce the carbon footprint of aquaponic operations while improving economic viability.
           
    The design of solar-powered systems requires careful consideration of energy demand patterns and seasonal variations in solar radiation. Pham et al., (2020) evaluated solar-powered IoT aquaponics systems, demonstrating the feasibility of maintaining continuous operation through optimized battery storage and energy management systems. The integration of Arduino-based control systems enables sophisticated energy management strategies that can prioritize critical functions during periods of limited solar energy availability.
           
    Isaac (2025) provided a comprehensive review of renewable energy integration in aquaculture and aquaponics systems, highlighting the potential for various renewable technologies including solar, wind and biogas systems. The choice of renewable energy technology depends on local conditions, system scale and energy requirements, with solar power being particularly suitable for small to medium-scale operations.
           
    Energy-efficient component selection plays a crucial role in minimizing overall system energy requirements. LED lighting systems have emerged as a key technology for controlled environment agriculture, offering significant energy savings compared to traditional lighting technologies (van Iersel, 2017). The spectral control capabilities of LED systems also enable optimization of light quality for specific crops, potentially enhancing the nutritional content of sprouted legumes.
           
    The development of energy-efficient automation systems requires careful consideration of component power requirements and operational strategies. Arduino-based systems offer inherent advantages in terms of low power consumption, but additional optimization strategies such as sleep modes, sensor scheduling and adaptive control algorithms can further reduce energy requirements (Lozada et al., 2021).
           
    Battery storage systems are essential components of renewable energy-powered aquaponics systems, providing energy security during periods of low renewable energy generation. The sizing and management of battery systems require careful analysis of energy demand patterns and renewable energy availability. Advanced battery management systems using Arduino platforms can optimize charging and discharging cycles to maximize battery life and system reliability.
     
    Technological infrastructure: Smart technologies and IoT integration
     
    The technological infrastructure of modern automated aquaponics systems encompasses a complex network of sensors, controllers, communication systems and user interfaces that work together to monitor and control system parameters. Arduino-based platforms have emerged as the foundation for many cost-effective automation systems due to their versatility, affordability and extensive community support (Jayanth and Maheswari, 2023).
           
    Sensor technologies form the core of automated monitoring systems, providing real-time data on critical parameters including temperature, pH, dissolved oxygen, electrical conductivity and water levels. The DS18B20 temperature sensor has become a standard component in aquaponic monitoring systems due to its accuracy, waterproof design and compatibility with Arduino platforms (Maxim Integrated, 2019). pH monitoring typically employs specialized electrodes and signal conditioning circuits, with Atlas Scientific’s EZO pH circuit providing a popular solution for Arduino integration (Atlas Scientific, 2023).
           
    Dissolved oxygen monitoring presents challenges due to the complexity and cost of traditional sensors. However, recent advances in optical dissolved oxygen sensors have improved accessibility while maintaining accuracy (YSI/Xylem, 2022). The integration of these sensors with Arduino-based data acquisition systems enables continuous monitoring and automated response to change conditions.
           
    Communication technologies play a crucial role in enabling remote monitoring and control capabilities. WiFi-enabled microcontrollers such as the ESP32 provide integrated wireless connectivity, enabling data transmission to cloud-based platforms and mobile applications (Espressif Systems, 2023). MQTT protocol has emerged as a preferred communication standard for IoT applications in aquaponics due to its lightweight nature and publish-subscribe architecture (Forhad et al., 2024).
           
    User interface development has been significantly simplified by the availability of web-based dashboard solutions and mobile applications. These interfaces enable remote monitoring, control and alert management, making systems accessible to users with varying technical backgrounds. Agrawal et al., (2023) demonstrated the implementation of cyber-physical aquaponics systems with comprehensive web-based interfaces that provide real-time monitoring and control capabilities.
           
    Cybersecurity considerations have become increasingly important as aquaponics systems become more connected. The implementation of secure communication protocols, authentication systems and access controls is essential for protecting system integrity and preventing unauthorized access. Regular software updates and security audits are necessary components of system maintenance procedures.
     
    Control and management services
     
    Automated control and management systems represent the intelligence layer of modern aquaponics systems, transforming sensor data into actionable control decisions that optimize system performance. The development of sophisticated control algorithms using Arduino platforms has enabled small-scale producers to implement advanced automation strategies previously available only in large commercial operations (Debroy et al., 2024).
           
    Feedback control systems form the foundation of automated aquaponics management, maintaining critical parameters within optimal ranges through continuous monitoring and adjustment. PID (Proportional-Integral-Derivative) controllers have proven particularly effective for maintaining stable pH, temperature and dissolved oxygen levels. Badiola et al., (2012) demonstrated the application of model predictive control techniques in aquaponics, showing significant improvements in system efficiency and stability compared to traditional control approaches.
           
    Water quality management automation encompasses multiple interconnected control loops that must be carefully coordinated to avoid conflicts and instabilities. pH control systems typically employ acid and base dosing pumps controlled by Arduino-based systems that respond to continuous pH measurements. Temperature control may involve heating elements, cooling systems and thermal mass management strategies that optimize energy efficiency while maintaining optimal conditions.
           
    Nutrient management automation represents a complex challenge due to the dynamic nature of nutrient uptake by plants and nutrient production by fish. Automated nutrient monitoring systems can track electrical conductivity and specific ion concentrations, enabling precise nutrient supplementation when required. Bailey and Ferrarezi (2022) provided comprehensive guidance on nutrient management in aquaponics, highlighting the importance of understanding plant-specific requirements for different growth stages.
           
    Feeding automation for fish components of aquaponic systems requires careful consideration of feeding schedules, portion sizes and fish behavior patterns. Arduino-based feeding systems can implement sophisticated feeding algorithms that account for fish size, water temperature and growth rates. Integration with water quality monitoring enables adaptive feeding strategies that prevent overfeeding and maintain optimal water conditions.
           
    Alert and notification systems are critical components of automated management systems, providing timely warnings of system failures, parameter deviations and maintenance requirements. Multi-channel notification systems using email, SMS and mobile app notifications ensure that system managers can respond quickly to critical situations. Mohd et al. (2024) demonstrated multi-parameter monitoring systems that provide comprehensive alerting capabilities for aquaculture and aquaponics applications.
           
    System scheduling and automation sequencing enable complex operational procedures to be automated, reducing labor requirements and improving consistency. Automated lighting schedules, irrigation cycles, harvesting procedures and maintenance routines can be programmed and executed automatically. Aydogan and Tas (2021) demonstrated the application of fuzzy logic control systems in aquaponics, showing improved decision-making capabilities in complex, multi-variable control scenarios.
           
    Remote management capabilities enable system operators to monitor and control systems from anywhere with internet connectivity. Web-based control interfaces and mobile applications provide comprehensive control capabilities while maintaining security and user authentication. These systems are particularly valuable for commercial operations and research facilities where continuous oversight is required.
     
    Smart indoor farming: Key challenges and solutions
     
    Smart indoor farming using aquaponics systems presents unique opportunities and challenges that require innovative technological solutions. The controlled environment advantages of indoor systems enable year-round production, protection from weather extremes and precise environmental control, but also require sophisticated environmental management systems and higher energy inputs (Shamshiri et al., 2018).
           
    Lighting systems represent one of the most critical and energy-intensive components of indoor aquaponics systems. LED technology has revolutionized controlled environment agriculture by providing energy-efficient, spectrally tunable lighting that can be optimized for specific crops and growth stages. The integration of LED systems with Arduino-based control platforms enables sophisticated lighting strategies that can enhance plant growth while minimizing energy consumption (Barzee et al., 2021).
           
    The present review was conducted in the PG and Research Department of Microbiology, Cauvery College for Women (Autonomous), Tiruchirappalli, Tamil Nadu, India, over a period of one year (June 2023-May 2024). Relevant literature on Arduino-based automated aquaponics systems was systematically collected from databases such as PubMed, Scopus, ScienceDirect, SpringerLink, Elsevier and Google Scholar.
           
    Studies published between 2010 and 2024 focusing on cost-effectiveness, sustainability and productivity in aquaponics-particularly those related to legume cultivation-were reviewed.
           
    Table 1 highlights the performance efficiency of the smart indoor aquaponics system compared to traditional farming, showing that yield per square meter is 3-10 times higher and water productivity is 10-50 times more efficient due to closed-loop nutrient recirculation and minimal water loss.While the energy input is relatively higher due to automation and environmental control (2-10× increase), this is offset by the significant savings in land use (up to 100× reduction) and labor costs, as the system requires minimal manual monitoring and maintenance.

    Table 1: Production efficiency and yield comparison.


           
    Space utilization optimization is particularly important in indoor systems where real estate costs are typically higher than outdoor installations. Vertical growing systems, dense planting strategies and efficient system layouts can significantly improve productivity per square foot. The automation of vertical systems requires sophisticated control systems that can manage multiple growing levels independently while maintaining system integration.
           
    Energy efficiency considerations are paramount in indoor aquaponics systems due to the high energy requirements for lighting, heating, cooling and system operation. Energy management systems using Arduino platforms can implement demand response strategies, optimize equipment scheduling and integrate renewable energy sources to minimize operational costs. Barbosa et al., (2015) compared energy requirements of different growing methods, highlighting opportunities for efficiency improvements in indoor systems.
           
    Air quality management in indoor environments requires monitoring and control of CO2 levels, humidity and air circulation patterns. Elevated CO2 levels can enhance plant growth, but require careful management to avoid affecting fish health in integrated systems. Automated ventilation systems using Arduino-based controllers can maintain optimal air quality while minimizing energy losses.
           
    Plant disease and pest management in indoor environments requires different strategies than outdoor systems. While the controlled environment provides protection from many outdoor pests, it can also create conditions favorable for specific indoor pests and diseases. Automated monitoring systems can detect early signs of plant stress and disease, enabling rapid intervention and treatment.
           
    Quality control and harvesting automation represent advanced capabilities that can significantly improve labor efficiency and product consistency. Automated harvesting systems for leafy greens and herbs can operate continuously, maintaining optimal plant density and harvest timing. Vision-based quality control systems using Arduino-compatible cameras can identify harvest-ready plants and detect quality issues.
     
    Legume cultivation and sprout production in automated aquaponics
     
    Legume cultivation in aquaponics systems presents unique opportunities and challenges that can be effectively addressed through Arduino-based automation systems. Legumes, including beans, peas, lentils and chickpeas, offer high nutritional value and nitrogen-fixing capabilities that can benefit overall system nutrient dynamics (Hu et al., 2015). The sprouting process further enhances the nutritional profile of legumes, making automated sprout production an attractive application for aquaponics systems.
           
    The nutritional benefits of sprouted legumes are well-documented, with sprouting processes increasing protein bioavailability, vitamin content and digestibility while reducing antinutritional factors (Fernández-Ochoa et al., 2020). Mubarak (2005) demonstrated significant improvements in nutritional composition of mung bean seeds through sprouting, showing increases in protein content and reductions in antinutritional compounds. These nutritional enhancements make sprouted legumes valuable crops for aquaponics systems focused on producing high-quality food products.
           
    Table 2 provides further support by demonstrating optimized growth conditions for multiple legume varieties, where precise Arduino-based sensor control reduces wastage of water and nutrients by ensuring optimal temperature, pH and humidity levels. These parameters minimize crop loss and improve nutrient yield, thereby enhancing cost efficiency over time.

    Table 2: Comprehensive analysis of legume varieties, growing conditions and production parameters.


           
    Food safety considerations are particularly important in sprout production due to the warm, moist conditions that can promote pathogenic bacterial growth. The FDA (2017) has established specific guidelines for sprout production to minimize microbial food safety hazards. Automated monitoring systems can continuously track critical control points and provide documentation for food safety compliance. The integration of UV sterilization systems and automated cleaning procedures can further enhance food safety in automated sprouting systems.
           
    Henares et al., (2020) demonstrated the successful cultivation of mungbean in aquaponics systems, showing comparable or superior performance to hydroponic systems. The integration of legume cultivation with fish production creates beneficial interactions, with legume root nodules potentially contributing to system nitrogen cycling. However, this requires careful management of rhizobia inoculation and root zone conditions.
           
    Automated harvesting systems for sprouts can significantly reduce labor costs while maintaining product quality and safety. Timing of harvest is critical for sprout quality, with optimal harvest typically occurring 3-7 days after initiation depending on the legume variety and desired sprout characteristics. Arduino-based systems can automate harvest timing based on environmental monitoring and predetermined growth parameters.
           
    Table 3 demonstrates that sprouting significantly enhances the nutritional quality of seeds compared to fresh seed baselines, with automated aquaponic sprouting showing the most pronounced improvements. Protein content increases due to enzymatic activation during germination, which leads to the breakdown of storage proteins into more digestible amino acids. The precise control of temperature and pH using Arduino-based systems ensures consistent metabolic activation, resulting in higher protein retention and improved amino acid profiles.

    Table 3: Nutritional enhancement and quality optimization.


           
    The integration of sprouting systems with aquaponics requires careful consideration of water quality and nutrient cycling. Sprout production typically requires clean water with minimal dissolved nutrients, while aquaponic systems are characterized by nutrient-rich water. This can be addressed through water treatment systems or separate water supplies for sprouting operations.
           
    Quality optimization in automated sprout production involves controlling factors that influence sprout appearance, texture, taste and nutritional content. Controlled atmosphere storage, optimal harvest timing and post-harvest handling procedures can be automated to maximize product quality. Sharma et al., (2016) demonstrated improvements in functional properties of sprouted legumes through controlled processing conditions.
     
    Comparative cost analysis
     
    • When compared to conventional aquaponics systems and other automation models (e.g., PLC or Raspberry Pi-based systems), the Arduino-controlled system offers a 25–30% reduction in initial setup cost. This is attributed to the use of open-source components, low-cost sensors and recycled or locally availabl materials for grow beds and tanks.
    • Maintenance and operation costs are also reduced due to lower power consumption by microcontrollers and automated irrigation, which minimizes human intervention and resource waste.
    • The integration of modular design allows scalability without significant additional investment, further enhancing economic feasibility.
     
    Nutrient content enhancement through automation
     
    Automated aquaponic systems offer significant potential for optimizing nutrient content in crops, particularly legumes and sprouts. Controlled environmental conditions, nutrient delivery and harvest timing enhance nutritional value while ensuring consistent quality. Sprouting under automated conditions improves protein digestibility, amino acid availability and vitamin content, especially vitamin C. LED-based light regulation, temperature and humidity control further support nutrient synthesis and bioactive compound accumulation. Studies report increases in antioxidants, phenolics and secondary metabolites under optimized stress protocols. Additionally, automated post-harvest handling preserves nutrients and extends shelf life, while precise nutrient delivery enables effective biofortification with essential minerals such as iron, zinc and selenium.
     
    Discussion and future research scope
     
    Arduino-based automation in aquaponics marks a major advancement in sustainable agriculture by offering cost-effective, customizable and open-source solutions that replace expensive proprietary systems. Automation minimizes manual supervision through sensor-driven monitoring and control of parameters such as temperature, pH, nutrient flow and water levels. Studies by Barbosa et al., (2015) and Barzee et al., (2021) demonstrated that automated aquaponic systems can reduce manual labor by 40-60%, while Shamshiri et al., (2018) reported a 20-30% reduction in operational labor costs through decreased dependence on skilled labor.
           
    Indian research supports these global findings. Sughuna et al. (2021) developed a recirculatory aquaponics model in Andhra Pradesh that achieved higher yields with minimal labor through semi-automation. The Department of Fisheries (2020) documented that integrated aquaponics can produce up to five times higher fish yield per unit area while simultaneously generating horticultural crops, reinforcing the system’s cost-effectiveness and resource efficiency in Indian conditions. Similarly, Bhanja (2023) in the Agriculture Journal highlighted India’s increasing adoption of Arduino- and IoT-based aquaponics, reporting significant reductions in labor input, water use and resource wastage compared to conventional soil-based systems.
           
    Additional Indian literature strengthens this context. The Journal of Inland Fisheries Society of India (2025) emphasized aquaponics as a sustainable food production system with high yield potential and low environmental impact. Likewise, ARCC Journals have contributed valuable insights: Agricultural Reviews discussed aquaponics as part of strategies promoting sustainability and climate resilience through circular nutrient recycling, while Agricultural Science Digest presented a comparative study of hydroponic and soil-based spinach cultivation, providing relevant evidence on soilless system performance and resource efficiency that parallels aquaponic advantages.
                   
    Although Arduino-based automation involves slightly higher energy consumption due to continuous sensor operation, the overall production cost per kilogram of yield is significantly reduced owing to improved productivity, optimized water and nutrient management and reduced labor costs. Collectively, these findings enhance the economic feasibility and sustainability of aquaponics, particularly for small- and medium-scale farmers in India. To ensure scalability and long-term reliability, future research should focus on AI-driven predictive control, blockchain-based traceability and comprehensive cost-benefit analyses tailored to India’s climatic and socio-economic conditions.

    CONCLUSION

    Arduino-driven aquaponics automation has emerged as a promising approach to advance sustainable agriculture by combining affordability, efficiency and adaptability. These systems improve productivity and resource management while offering significant potential to address food security in regions with limited land and water. Renewable energy integration and modular scalability add to their economic and environmental viability. However, broader adoption requires solutions for challenges such as interoperability, standardization and system expansion. Future progress lies in incorporating artificial intelligence, predictive control and robust design frameworks. With continued innovation and training, Arduino-based aquaponics can evolve into a resilient and scalable model for global food production.

    CONFLICT OF INTEREST

    The authors declare that there are no Conflict of interest.

    REFERENCES


    1. Agrawal, N., Kharat, M., Waykole, P., Patil, A. (2023). Cyber-physical aquaponic system: Architecture and implementation. IEEE Access. 11: 70425-70438.

    2. Arduino. Arduino UNO Rev3 [Internet]. 2025 [cited 2025 Aug 20].

    3. Atlas Scientific. EZO pH circuit datasheet [Internet]. 2023 [cited 2025 Aug 20].

    4. Aydogan, A.K., Tas, N. (2021). Design and control of an aquaponics system using arduino and fuzzy logic. Measurement. 176: 109204.

    5. Ayub, S., Rahman, M., Rahman, M. (2018). Low-cost sensor network for aquaponics monitoring using Arduino. Int. J. Sci. Res. Eng. Dev. 1(10): 130-134.

    6. Badiola, M., Mendiola, D., Bostock, J. (2012). Recirculating aquaculture systems (RAS): Management issues and future challenges. Aquac. Eng. 51: 26-35.

    7. Baganz, D., Baganz, G., Staaks, G., Monsees, H., Hölker, F., Kloas, W. (2019). Temperature-dependent oxygen consumption of Nile tilapia. Aquac Int. 27: 1125-1136.

    8. Bailey, D.S., Ferrarezi, R.S. (2022). Nutrient management in aquaponics: Current knowledge and future challenges. Horticulturae. 8(3): 222.

    9. Barbosa, G.L., Gadelha, F.D.A., Kublik, N., Proctor, A., Reichelm, L., Weissinger, E. et al. (2015). Comparison of land, water and energy requirements of lettuce grown hydroponically. Int. J. Environ. Res. Public Health. 12(6): 6879-6891.

    10. Barman, B., Munshi, S.A., Mondal, I., Quader, S.K.W., Das, A. (2025). Promoting sustainability and climate resilience in agriculture through circular economy: A review. Agricultural  Reviews. doi: 10.18805/ag.R-2865.

    11. Barzee, T.J., Edalati, A., El-Mashad, H.M., Wang, D., Scow, K.M., Zhang, R. et al. (2021). Environmental and economic analysis of aquaponics systems. J. Clean Prod. 280: 124377.

    12. Barzee, T.J., Gong, C., Sun, Y., Kennedy, R., Dryer, N., Wang, W. et al. (2021). Energy and water use of hydroponic vs conventional lettuce production. Environ. Res. Lett. 16(6):  064006.

    13. Bhanja, A. (2023). Aquaponics advancements in the Indian context. Agric J. 18(2): 245-256.

    14. Choi, C.Y., Rim, C., Kim, S., Park, K.W. (2022). Energy-efficient scheduling for aquaponics with PV-battery systems. Energies. 15(20): 7743.

    15. Choi, Y., Lee, J., Park, J.H., Kim, H. (2021). Solar-powered aquaponics:  Design and techno-economic analysis. Renew Energy. 163: 627-638.

    16. Debroy, P., Majumder, P., Das, A., Seban, L. (2024). Model-based predictive greenhouse control in aquaponics. Environ. Sci. Pollut. Res. Int. 31(35): 48423-48449.

    17. Department of Fisheries, Government of India. (2020). Aquaponics System: Integrated Fish and Plant Production Technology. New Delhi. 

    18. Donkor, O.N., Stojanovska, L., Ginn, P., Ashton, J., Vasiljevic, T. (2012). Germinated grains and health benefits. Food Sci. Nutr. 53(6): 631-647.

    19. Espressif Systems. (2023). ESP32 technical reference manual [Internet]. 

    20. FAO. (2014). Small-scale aquaponic food production. Rome: FAO. 

    21. FAO. (2014). Small-scale Aquaponic Food Production: Integrated Fish and Plant Farming. 2nd ed. Rome: FAO. 

    22. FDA. (2017). Reducing Microbial Food Safety Hazards in Sprouts. Draft Guidance. 

    23. Fernández-Ochoa, Á., Leyva-Jiménez, F.J., Cádiz-Gurrea, M.L., Arráez-Román, D., Segura-Carretero, A. (2020). Effects of sprouting on nutraceutical properties. Foods. 9(1): 6.

    24. Forhad, H.M., Uddin, M.R., Chakrovorty, R.S, Ruhul, A.M., Faruk, H.M., Kamruzzaman, S. et al. (2024). IoT-based real- time water quality monitoring. Heliyon. 10(23): e40746.

    25. Gaikwad, D.J., Sahu, C., Prasanth, S., Divya, B., Atta, K., Jaswanth, D.S., Mahapatra, A. (2023). Hydroponics vs soil: An in- depth assessment of morpho-physiological traits in spinach (Spinacia oleracea). Agricultural Science Digest. doi: 10.18805/ag.D- 5928.

    26. Gan, R.Y., Lui, W.Y., Wu, K., Chan, C.L., Dai, S.H., Sui, Z.Q. et al. (2017). Bioactive compounds in sprouts. Compr. Rev. Food Sci. Food Saf. 16(4): 691-711.

    27. Goddek, S., Delaide, B., Mankasingh, U., Ragnarsdottir, K.V., Jijakli, H., Thorarinsdottir, R. (2015). Challenges of sustainable aquaponics. Sustainability. 7(4): 4199-4224.

    28. Hati, S., Patel, N., Patel, K., Mandal, S. (2022). Sprouted legumes: Health benefits. Crit. Rev. Food Sci. Nutr. 62(18): 5009- 5024.

    29. Henares, M.N.P., Tuazon, D., Songcayauon, D.M., Austria, C., de Guzman, G.Q. (2020). Mungbean performance in aquaponics. Philipp. J. Sci. 149(4): 1123-1133.

    30. Hu, Z., Lee, J.W., Chandran, K., Kim, S., Brotto, A.C., Khanal, S.K. (2015). Effect of plant species on nitrogen recovery. Bioresour Technol. 188: 92-98.

    31. Isaac, E. (2025). Integrating renewable energy in aquaculture. J. Built. Environ. Geol. Res. doi: 10.70382/ajbegr.v8i4.022. 

    32. Jayanth, C., Maheswari, M. (2023). ESP32-based IoT aquaponics monitoring system. IOP Conf. Ser. Mater. Sci. Eng. 1270: 012066.

    33. Journal of Inland Fisheries Society of India. (2025). Aquaponics: A sustainable food production system. JIFSI. 57(1): 45-52.

    34. Khattab, R.Y., Arntfield, S.D. (2009). Nutritional quality of legume seeds. LWT Food Sci. Technol. 42(6): 1113-1118.

    35. Kloas, W., Groß, R., Baganz, D., Graupner, J., Monsees, H., Schmidt, U. et al. (2015). New concept for aquaponics sustainability.  Aquac. Environ. Interact. 7: 179-192.

    36. Lennard, W.A., Leonard, B.V. (2006). Plant growth rates in aquaponic vs hydroponic solutions. Aquac. Int. 14: 539- 550.

    37. Lozada, P., Haque, M.M., Morshed, A. (2021). IoT-based automated control system for aquaponics. Comput. Electron. Agric. 187: 106291.

    38. Martínez-Villaluenga, C., Peñas, E., Frias, J. (2020). Bioactive peptides in legumes. Crit. Rev. Food Sci. Nutr. 60(21): 3566-3590.

    39. Maxim Integrated. (2019). DS18B20 digital thermometer datasheet [Internet]. 

    40. Mchunu, N., Lagerwall, G., Senzanje, A., Tesfay, S. (2018). Aquaponics for small-scale farmers. Aquac. Int. 26: 1593-1607.

    41. Mohan, J., Pandey, A., Singh, V. (2023). Application of aquatic plant Ceratophyllum demersum (L.) in phytoremediation of wastewater. Agricultural Science Digest. 43(5): 655- 660. doi: 10.18805/ag.D-5443.

    42. Mohd, J.N.A, Abdullah, A.F., Mohd, K.M.S., Karim, M.M.A., Muhadi, N. (2024). IoT-based water quality monitoring accuracy. Heliyon. 10(8): e29022.

    43. Monsees, H., Kloas, W., Wuertz, S. (2017). Decoupled aquaponic systems. PLoS One. 12(9): e0183056.

    44. Mubarak, A.E. (2005). Nutritional composition of mung beans. Food Chem. 89(4): 489-495.

    45. Pham, T., Tong, T., Tran, T., Nguyen, T. (2020). Solar-powered IoT aquaponics system. Energies. 13(21): 5737.

    46. Prodanovic, V., Grace, M., Webb, J.A. (2021). Role of water temperature in aquaponics. Water. 13(7): 980.

    47. Rakocy, J.E., Masser, M.P., Losordo, T.M. (2006). Aquaponics- integrating fish and plant culture. SRAC Publ. pp 454. 

    48. Reyes-Yanes, A., Martinez, P., Ahmad, R. (2020). Automated aquaponics and IoT systems. J. Clean. Prod.  263: 121571.

    49. Shamshiri, R.R., Kalantari, F., Ting, K.C., Thorp, K.R., Hameed, I.A., Weltzien, C. et al. (2018). Advances in greenhouse automation. Int J. Agric. Biol. Eng. 11(1): 1-22.

    50. Sharma, S., Saxena, D.C., Riar, C.S. (2016). Functional properties of sprouted chickpea flour. J. Food Sci. Technol. 53(2): 1084-1091.

    51. Sughuna, T. (2021). Aquaponics systems: Future food production. Int. J. Curr. Microbiol. Appl. Sci. 10(11): 397-406.

    52. Taha, M.F., ElMasry, G., Gouda, M., Zhou, L., Liang, N., Abdalla, A. et al. (2022). Smart IoT systems for aquaponics automation.  Chemosensors. 10(8): 303.

    53. van Iersel, M.W. (2017). Optimizing LED lighting in controlled environments. In: LEDs for Agriculture. Springer. p. 59-80.

    54. Wongkiew, S., Hu, Z., Chandran, K., Lee, J.W., Khanal, S.K. (2017). Nitrogen transformations in aquaponics. Aquac. Eng. 76: 9-19.

    55. YSI/Xylem. (2022). Optical dissolved oxygen sensors-EXO ODO [Internet]. 

    56. Zou, Y., Hu, Z,. Zhang, J., Xie, H., Guimbaud, C., Fang, Y. (2016). Comparative nitrogen transformation studies in aquaponics systems for optimization models. Bioresour. Technol. 210: 81-87.

    57. Zou, Y., Hu, Z., Zhang, J., Xie, H., Fang, Y. (2016). Effects of pH on nitrogen transformations in aquaponics. Bioresour. Technol. 210: 81-87.

    58. Zou, Y., Hu, Z., Zhang, J., Xie, H., Guo, H., Fang, Y. (2016). Effect of dissolved oxygen on nitrogen transformations. Water Sci. Technol. 74(8): 1913-1920.

    59. Zou, Y., Lu, J., Li, H., Zhu, X., Chen, Z. et al. (2016). Integrated aquaponics system for low-temperature areas. Aquac. Int. 24: 1009-1020.
    In this Article
      Contact us
      Follow us
      Published In
      Indian Journal of Agricultural Research

      Editorial Board

      View all (0)