Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1 -- 1292 2024-02-17 02:47:27 |
2 layout Meta information modification 1292 2024-02-18 01:01:15 |

Video Upload Options

Do you have a full video?

Confirm

Are you sure to Delete?
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Channa, A.; Munir, K.; Hansen, M.; Tariq, M.F. Optimisation of Small-Scale Aquaponics Systems Using Artificial Intelligence and the IoT: Current Status, Challenges, and Opportunities. Encyclopedia. Available online: https://encyclopedia.pub/entry/55105 (accessed on 19 April 2024).
Channa A, Munir K, Hansen M, Tariq MF. Optimisation of Small-Scale Aquaponics Systems Using Artificial Intelligence and the IoT: Current Status, Challenges, and Opportunities. Encyclopedia. Available at: https://encyclopedia.pub/entry/55105. Accessed April 19, 2024.
Channa, Abdul, Kamran Munir, Mark Hansen, Muhammad Fahim Tariq. "Optimisation of Small-Scale Aquaponics Systems Using Artificial Intelligence and the IoT: Current Status, Challenges, and Opportunities" Encyclopedia, https://encyclopedia.pub/entry/55105 (accessed April 19, 2024).
Channa, A., Munir, K., Hansen, M., & Tariq, M.F. (2024, February 17). Optimisation of Small-Scale Aquaponics Systems Using Artificial Intelligence and the IoT: Current Status, Challenges, and Opportunities. In Encyclopedia. https://encyclopedia.pub/entry/55105
Channa, Abdul, et al. "Optimisation of Small-Scale Aquaponics Systems Using Artificial Intelligence and the IoT: Current Status, Challenges, and Opportunities." Encyclopedia. Web. 17 February, 2024.
Peer Reviewed
Optimisation of Small-Scale Aquaponics Systems Using Artificial Intelligence and the IoT: Current Status, Challenges, and Opportunities

Environment changes, water scarcity, soil depletion, and urbanisation are making it harder to produce food using traditional methods in various regions and countries. Aquaponics is emerging as a sustainable food production system that produces fish and plants in a closed-loop system. Aquaponics is not dependent on soil or external environmental factors. It uses fish waste to fertilise plants and can save up to 90–95% water. Aquaponics is an innovative system for growing food and is expected to be very promising, but it has its challenges. It is a complex ecosystem that requires multidisciplinary knowledge, proper monitoring of all crucial parameters, and high maintenance and initial investment costs to build the system. Artificial intelligence (AI) and the Internet of Things (IoT) are key technologies that can overcome these challenges. Numerous recent studies focus on the use of AI and the IoT to automate the process, improve efficiency and reliability, provide better management, and reduce operating costs. However, these studies often focus on limited aspects of the system, each considering different domains and parameters of the aquaponics system. This paper aims to consolidate the existing work, identify the state-of-the-art use of the IoT and AI, explore the key parameters affecting growth, analyse the sensing and communication technologies employed, highlight the research gaps in this field, and suggest future research directions. Based on the reviewed research, energy efficiency and economic viability were found to be a major bottleneck of current systems. Moreover, inconsistencies in sensor selection, lack of publicly available data, and the reproducibility of existing work were common issues among the studies.

aquaponics AgriTech sustainable farming Internet of Things artificial intelligence big data
Traditional farming methods are facing increasing threats from extreme weather events, resource scarcity, and urbanisation. These challenges are jeopardising food security, causing a shift towards more sustainable and resilient agricultural practices. Extreme weather events, like droughts, floods, and heatwaves, are causing widespread crop damage and yield losses. In 2018, heatwaves alone led to multiple crop failures and up to 50% yield reductions in central and northern Europe [1], highlighting the vulnerability of traditional farming systems to climate change. The escalating demand for food, coupled with urbanisation, is putting further strain on agricultural resources. Urban populations are projected to increase by about 50% by 2045 [2], and there is growing pressure to produce more food from a shrinking land base. This is further intensified by the depletion of water resources, deforestation, soil degradation, and greenhouse gas emissions associated with conventional farming practices [3]. There is a need to find new ways of food production that are more efficient, rely on fewer natural resources, and are resilient to climate change.
Aquaponics has emerged as one of the potential alternatives to overcome these challenges. It is a sustainable and innovative agricultural system that combines aquaculture (raising fish and other aquatic organisms) and hydroponics (growing plants without soil). In an aquaponics system, the nutrient-rich water from the fish tanks is used to fertilise the plants, and the plants help purify the water for the fish. This symbiotic relationship allows aquaponics systems to produce both fish and vegetables with significantly less water and land compared to traditional agriculture. Additionally, food can be grown indoors in a fully controlled environment, making it more resilient to climate change.
Despite its many benefits, aquaponics is a complex ecosystem with many critical parameters that must be closely monitored and maintained, such as dissolved oxygen (DO), ammonia, pH, temperature, and exposure to sunlight. Manually monitoring and maintaining all of these parameters is complicated, time-consuming, and requires multidisciplinary expert knowledge. However, the IoT and AI can help overcome these challenges by automating the monitoring and control process, analysing sensor data, and identifying patterns and trends that would be difficult or impossible for humans to detect. This could lead to the development of new and innovative ways to optimise aquaponics systems.
Recent studies have demonstrated the use of AI and machine learning to address various aspects of aquaponics systems. For example, Abbasi et al. [4] used machine learning algorithms to identify Foliage Chlorosis in lettuce, John and Mahalingam [5] tested the use of the You Only Look Once (YOLO) algorithm to detect excessive fish feed in a tank, and Karimanzira and Rauschenbach [6] used a convolutional neural network (CNN) to estimate plant growth parameters and a Long Short-Term Memory (LSTM) network to detect anomalies in the system. However, the majority of the AI-related literature on aquaponics focuses on visual observations using machine vision and image processing, whereas the use of data from IoT sensors remains largely unexplored.
Moreover, existing research on the use of the IoT for aquaponics often focuses on limited parameters. For instance, Wijayanto et al. [7] monitored pH, temperature, water level, and electrical conductivity but overlooked DO and other elements. Murakami and Yamamoto [8] detected DO but overlooked nitrate and solar radiation. There was no clear explanation about the parameter selection and use of sensors, suggesting that researchers are choosing sensors based on availability rather than on a thorough understanding of the needs of aquaponics systems. According to Yanes et al. [9], current aquaponics systems are still in their primitive stage, and not all the parameters of aquaponics have been thoroughly researched.
A comprehensive review is needed to consolidate the existing work on aquaponics, identify the crucial parameters to monitor, and survey the state-of-the-art AI and IoT technologies and sensing solutions available on the market.

Contributions

While there are several review papers on aquaponics, none of them provide an exhaustive evaluation of the current state-of-the-art use of AI and the IoT in this field. This paper aims to fill this gap by compiling and comparing the current literature. This review will cover the following key areas:
1.
The key parameters that need to be monitored in aquaponics systems.
2.
The sensors available for acquiring farm data.
3.
The AI and ML algorithms used to optimise aquaponic processes and management.
4.
The IoT systems and communication technologies used for remote monitoring and control.
5.
The research gaps and new opportunities in this field.

Scope and Boundaries

The scope and boundaries of this study can be summarised as follows:
1.
The scope of this study is mainly limited to small-scale experimental aquaponics systems within the academic domain. Commercial aquaponics systems, in contrast, exhibit significant variability in their technical specifications due to regional requirements, climate conditions, and resource availability. In addition, technical details about integrating AI and IoT technologies within commercial aquaponics systems appear to be limited in the public domain. Consequently, the comprehensive evaluation of commercial systems, particularly in relation to AI and IoT integration, proved challenging and, as a result, was excluded from the scope of this study.
2.
Although commercial systems are excluded from this review, their fundamentals and operational theories are the same. Therefore, the knowledge acquired from this review can be applied to commercial systems.
3.
This study is primarily focused on single-recirculation coupled aquaponics systems. While the identified IoT and AI technologies are applicable to both coupled and decoupled systems, optimising decoupled systems may require a separate study due to their distinct requirements. We have only included decoupled systems to provide a thorough overview of aquaponics systems.
4.
This review only covers the technical aspects of aquaponics related to the integration of AI and the IoT. It does not delve into mechanical, chemical, biological, ecological, or any other domain.

Paper Organisation

This paper begins with a comprehensive literature review. Section 2 describes the methodology and search criteria used to select papers for review. Section 3 provides a brief introduction to typical aquaponics systems, their types, and grow techniques. Section 4 reviews the use of the IoT and AI in the existing literature, discusses the key parameters that need to be monitored, and surveys the progress of AI solutions for aquaponics. Section 5 outlines the research gaps and opportunities in the field. Finally, Section 6 concludes this review by identifying key research areas for future work.

References

  1. Rampant Heatwaves Threaten Food Security of Entire Planet, Scientists Warn. Available online: https://www.theguardian.com/environment/2023/jul/21/rampant-heatwaves-threaten-food-security-of-entire-planet-scientists-warn (accessed on 5 February 2024).
  2. World Bank. Urban Development; The World Bank: Washington, DC, USA, 2023; Available online: https://www.worldbank.org/en/topic/urbandevelopment/overview (accessed on 5 February 2024).
  3. FAO. The Future of Food and Agriculture: Trends and Challenges; Food and Agriculture Organization of the United Nations: Quebec, QC, Canada, 2017; OCLC: ocn979567879.
  4. Abbasi, R.; Martinez, P.; Ahmad, R. Automated Visual Identification of Foliage Chlorosis in Lettuce Grown in Aquaponic Systems. Agriculture 2023, 13, 615.
  5. John, J.; Mahalingam, P.R. Automated Fish Feed Detection in IoT Based Aquaponics System. In Proceedings of the 2021 8th International Conference on Smart Computing and Communications (ICSCC), Kochi, Kerala, India, 1–3 July 2021; pp. 286–290.
  6. Karimanzira, D.; Rauschenbach, T. An intelligent management system for aquaponics. At-Automatisierungstechnik 2021, 69, 345–350.
  7. Wijayanto, A.; Wardhana, K.; Aziz, A. Implementation of Internet of Things (IoT) for Aquaponic System Automation. In Proceedings of the the 2021 International Conference on Computer, Control, Informatics and Its Applications (IC3INA ’21), Virtual/Online Conference, Indonesia, 5–6 October 2021; pp. 176–181.
  8. Murakami, R.; Yamamoto, H. Growth Estimation Sensor Network System for Aquaponics using Multiple Types of Depth Cameras. In Proceedings of the 2022 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), Jeju Island, Republic of Korea, 21–24 February 2022; pp. 033–038.
  9. Yanes, A.R.; Martinez, P.; Ahmad, R. Towards automated aquaponics: A review on monitoring, IoT, and smart systems. J. Clean. Prod. 2020, 263, 121571.
More
Information
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register : , , ,
View Times: 249
Entry Collection: Data Science
Online Date: 17 Feb 2024
1000/1000