A Systematic Review of AIoT in Agricultural Environmental Monitoring: A Comparative Analysis of Machine Learning Approaches - Kharisma, Derry Artha; Sunanto, Sunanto (ILKOMNIKA, 2026)
Agricultural water management increasingly deploys AIoT systems that integrate IoT sensing with machine learning, yet deployable autonomous irrigation control remains largely unrealized despite widely reported accuracy above 90%. This systematic literature review of 25 peer-reviewed studies (2020–2026), conducted across five databases following PRISMA 2020 guidelines, diagnoses why predictive performance fails to translate into operational autonomy. The analysis identifies six interdependent structural gaps: open-loop prediction architectures, informationally narrow sensing, correlated co-sensor packaging, static non-adaptive models, accuracy–deployability decoupling, and metric inconsistency. These gaps form a dependency chain across data, inference, and actuation layers in which closed-loop integration depends on resolving data adequacy. A parallel finding reveals systematic methodological divergence between national and international research contexts, driven by infrastructure and deployment constraints rather than research quality, with reinforcement learning, hybrid multi-modal architectures, and continual learning largely absent in national studies. This study contributes a reframing of AIoT system maturity by demonstrating that within-study accuracy is misaligned with operational validity. It further establishes environmental generalizability as a more appropriate evaluation criterion, shows that the six structural gaps form a sequential dependency structure that prevents single-gap solutions from producing deployable improvement, and provides directional evidence that reported accuracy and validation scope are inversely related across the corpus, suggesting that current performance claims systematically overstate operational readiness.