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Abstract Smart aquaponics systems are gaining popularity as they contribute immensely to sustainable food production. These systems enhance traditional farming with advanced technologies like the Internet of Things (IoT), solar energy, and Artificial Intelligence (AI) for increased proficiency and productivity. However, assessing the performance and effectiveness of these systems is challenging. A systematic literature review (SLR) was conducted to examine the applications, technologies, and evaluation methods used in smart aquaponics. The study sourced peer-reviewed publications from IEEE Xplore, Scopus, SpringerLink and Science Direct. After applying inclusion and exclusion criteria, a total of 105 primary studies were selected for the SLR. The findings show that aquaponics predictions (27%) have been under-explored compared to applications that involved monitoring or monitoring and controlling aquaponics (73%). IoT technologies have been used to create prototype aquaponic systems and collect data, while machine learning/deep learning (predictive analytics) are used for prediction, abnormality detection, and intelligent decision-making. So far, predictive analytics solutions for aquaponics yield prediction, return-on-investment (ROI) estimates, resource optimisation, product marketing, security of aquaponics systems, and sustainability assessment have received very little attention. Also, few studies (37.7%) incorporated any form of evaluation of the proposed solutions, while expert feedback and usability evaluation, which involved stakeholders and end-users of aquaponics solutions, have been rarely used for their assessment. In addition, existing smart aquaponics studies have limitations in terms of their short-term focus (monitoring and controlling of aquaponics not undertaken over a long time to assess performance and sustainability), being conducted mostly in controlled settings (which limits applicability to diverse conditions), and being focused on specific geographical contexts(which limits their generalizability). These limitations provide opportunities for future research. Generally, this study provides new insights and expands discussion on the topic of smart aquaponics.
Anila et al. (Mon,) studied this question.