Summary
This critical review evaluates the integration of artificial intelligence with smart sensing technologies in hydroponic systems, focusing on sensor-driven monitoring of solution chemistry, environmental conditions, and plant health indicators. The authors assess the potential of AI methodologies—including time-series forecasting, computer vision, and reinforcement learning—to enhance nutrient management, predict physiological responses, and enable adaptive control. The review identifies substantial opportunities for improved resource efficiency and crop productivity, but highlights key implementation barriers including sensor drift, model generalisation across crops, and adoption challenges, proposing future directions including explainable AI dashboards and hybrid control frameworks.
Regional applicability
This review is internationally applicable and directly relevant to United Kingdom policy and practice in controlled-environment agriculture. The UK has significant interests in year-round protected cropping and sustainable intensification; AI-enabled hydroponics offers potential to increase domestic horticulture productivity whilst reducing water and nutrient inputs, supporting food security and environmental objectives, though the review notes that field validation across UK growing conditions and multiple crop types remains necessary before operational adoption.
Key measures
Sensor monitoring of electrical conductivity (EC), pH, dissolved oxygen (DO), environmental parameters, plant-health imaging, crop yield, nutrient uptake, plant growth, stress mitigation outcomes, and system resource-use efficiency
Outcomes reported
The review evaluates how AI-enabled sensor systems can enhance hydroponic production through real-time monitoring of solution chemistry, environmental parameters, and plant health, whilst identifying key implementation challenges and design strategies. It assesses the potential of time-series forecasting, computer vision, and reinforcement learning for adaptive control and stress mitigation in controlled-environment agriculture.
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