Smart aquaponics: trends, challenges, and future directions
TL;DR
Future progress requires stronger integration of prediction, control, calibration, biological benchmarking, and operator usability, and this systematic literature review addresses this gap through 10 research questions.
Abstract
Smart aquaponics couples recirculating aquaculture with hydroponic plant cultivation, and a substantial body of recent research applies IoT sensing, machine learning, and edge computing to this domain. The literature, however, lacks a synthesis that maps how predictive models, system architectures, and biological context combine in deployed systems, leaving researchers and practitioners without a clear technical roadmap. This systematic literature review (SLR) addresses this gap through 10 research questions following the Kitchenham and Charters protocol and PRISMA-style screening. The search initially identified 3,123 records from six bibliographic databases and snowballing; after duplicate removal, screening, and quality assessment, 49 primary studies were retained for analysis. First, the literature has a prediction-to-control gap: 24% of the studies report forecasters or classifiers without specifying how the resulting prediction is consumed by an actuator, leaving inference layers technically ahead of control layers. Second, system architectures are transitioning from centralised cloud designs toward federated and edge configurations to address privacy, latency, bandwidth, and sensor-drift issues. Third, hybrid models incorporating physical, or domain knowledge show promising performance under non-stationary aquaponics conditions. Fourth, long-term and multi-site field deployments remain very limited, making it difficult to interpret reported machine-learning accuracy as evidence of commercial readiness. Overall, future progress requires stronger integration of prediction, control, calibration, biological benchmarking, and operator usability.
