Optimisation of small-scale aquaponics systems using artificial intelligence and the IoT: Current status, challenges, and opportunities

Channa, Abdul Aziz; Munir, Kamran; Hansen, Mark; Tariq, Muhammad Fahim

Abstract

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.

• Journal Article Type: Article
• Acceptance Date: Feb 2, 2024
• Online Publication Date: Feb 8, 2024
• Publication Date: Mar 1, 2024
• Deposit Date: Feb 12, 2024
• Publicly Available Date: Feb 12, 2024
• Journal: Encyclopedia
• Print ISSN: 2673-8392
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 4
• Issue: 1
• Pages: 313-336
• DOI: https://doi.org/10.3390/encyclopedia4010023
• Keywords: aquaponics; AgriTech; sustainable farming; Internet of Things; artificial intelligence; big data
• Public URL: https://uwe-repository.worktribe.com/output/11692818