Applying AI and machine learning in vertical growing
Source: VFD.com
This paper presents a systematic review of 208 peer-reviewed studies from 2015 to 2025. It explores how AI, ML, and IoT are applied across the VF ecosystem, focusing on key areas such as computer vision for disease detection, crop growth and yield prediction, smart climate control, and precision nutrient and irrigation management.
However, challenges remain: high energy use (especially from LED lighting, which makes up 40–60% of costs), expensive setup, scattered datasets, and limited real-world testing. Many high-accuracy claims (>95%) come from lab conditions and need better validation in actual farms.
Source: ResearchGate Frontpage photo: © Jy C | Dreamstime Publication date: Tue 8 Sep 2026 Related Articles → See More Technology adoption in the vertical farming industry Applying AI and machine learning in vertical growing Purdue hosts produce industry leadership program Indonesia: UMS lecturers assist KWT Kopen SAE in developing hydroponic vegetables The state of automation in Norwegian growing Pomona AI wants plant researchers to test more growing conditions at the same time US (NM): From school farm to school table Sweden: What do consumers think of hydroponic produce? Using vertical farming to decrease growing's carbon footprint Influence of three nutrient solutions on the yield and quality of leafy vegetables grown hydroponically Related Articles Technology adoption in the vertical farming industry Applying AI and machine learning in vertical growing Purdue hosts produce industry leadership program Indonesia: UMS lecturers assist KWT Kopen SAE in developing hydroponic vegetables The state of automation in Norwegian growing Related Articles Pomona AI wants plant researchers to test more growing conditions at the same time US (NM): From school farm to school table Sweden: What do consumers think of hydroponic produce?
Why this matters: For operators, this is a water-management story. The useful signal is that direct substrate measurements can help cut drain loss materially without giving up yield or fruit quality, which is exactly the kind of controllable efficiency gain a facility can build on.
Frequently Asked Questions
Why does substrate sensing matter in free-drain strawberry systems?
Because drain percentage tells a grower what already happened, while substrate moisture and EC data show root-zone conditions directly. That makes it easier to cut water loss without guessing.
What is the operator takeaway from this trial?
If the thresholds are understood well enough, growers can reduce drain water materially while protecting yield and fruit quality, which makes sensing an operational tool instead of a reporting tool.
