ReviewPlanta2026
AI-enabled hydroponics: a critical review of sensor-driven farming systems for sustainable global food security.
Review in Planta, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
MAIN
conclusionThis review concludes that integrating AI with smart sensing has the potential to substantially enhance hydroponic production through precise monitoring, adaptive control, and improved resource-use efficiency. Hydroponics, a key approach in controlled-environment agriculture (CEA), enables precise regulation of plant growth conditions, improving resource efficiency and year-round crop production. Recent advances in artificial intelligence (AI) and sensor technologies offer opportunities to enhance nutrient management, monitor plant health, and predict physiological responses in real time. This review critically evaluates AI-enabled hydroponic systems, focusing on sensor-driven monitoring of solution chemistry (EC, pH, DO), environmental parameters, and plant-health indicators through imaging. We highlight the application of time-series forecasting models, computer vision, and reinforcement learning for adaptive control, emphasizing their impact on plant growth, nutrient uptake, and stress mitigation. Key challenges, including sensor drift, model generalization across crops, and adoption barriers, are discussed. Finally, we outline strategies for integrating AI with plant-centered hydroponic design, including hybrid control frameworks, prototype concepts for explainable AI dashboards, and open datasets. The dashboard concept is presented as a future design direction to improve transparency and operator trust rather than as a validated user interface evaluated in this review. The accompanying conceptual implementation example illustrates how the reviewed AI methodologies could be integrated within an AIoT-enabled hydroponic system, while quantitative validation across multiple crops, cultivation cycles, and production environments remains necessary before generalizing their effectiveness.
Indexed as
Identifiers
42886254What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.