Multilayer optimization represents one of the points of maximum complexity-and maximum potential-in indoor vertical farming. When stacking multiple growing layers, any design error multiplies; conversely, any well-calculated improvement scales exponentially.
In this article we address the logic, calculations, and simulations required to design and optimize high-performance multilayer systems.
Optimizing a multilayer system means more than just adding layers. It means ensuring that each layer receives:
the same level of useful light (PPFD)
the same quality of air and CO₂
the same thermal stability
comparable root conditions
In the absence of optimization, upper layers become more productive and lower layers progressively less efficient, with average yields plummeting.
In a professional multilayer system, parameters are not managed "by feel." They must be modeled and simulated.
PPFD target per layer
uniformity (% standard deviation)
light spillover between layers
LED efficiency decay with temperature
heat buildup per layer
vertical gradient (top vs. bottom)
active/passive dissipation capacity
air velocity (m/s)
hourly changes per layer
dead zones and stagnation
CO₂ distribution.
number of plants/m² per layer
leaf area (LAI)
light competition
Simplified formula:
PPFD_layer = (LED Output × Optical Efficiency) / Useful area
But in multilayer it has to be corrected for:
reflections
absorption of the upper layer
actual LED-canopy distance
Typical error: use the same setpoint for all layers → guaranteed failure.
Each layer adds:
Q = LED electrical power × (1 - photosynthetic efficiency)
In stacks of 4-6 layers, the thermal load is not linear, but cumulative upward.
Without CFD simulation, the top layer becomes systematically off target.
Real operating range:
< 0.2 m/s → insufficient gas exchange
0.6 m/s → mechanical stress and dehydration
Each layer must be in the optimal window, not the system average.
CFD for multilayer airflow
transient thermal simulations
real PPFD distribution
maximum load scenarios (summer, full density)
"theoretical" growth without real feedbacks
static models without variability
simulations not validated by field data
Simulation is to reduce errors, not to replace reality.
Replicating the same setup on all layers
Ignoring thermal stratification
Underestimating the effect of moisture between layers
Not recalibrating light and airflow as biomass changes
Designing "in plan" without thinking "in volume"
An efficient multilayer system always follows this cycle:
Initial engineering design
Prior simulation
Actual measurement by layer
Dynamic correction
Standardization only after validation
Skipping any of these steps means building an unstable system.
Manual multilayer optimization does not scale.
It needs:
independent control per layer
distributed sensors
separate actuators
dynamic compensation logic
Only then does multilayer become a competitive advantage and not an operational problem.
Tomato+ develops multilayer indoor growing systems based on intelligent control, advanced simulation, and real growth data.
Each layer is managed as an independent system, but optimized as part of a whole.
Automation, artificial intelligence and engineering design are at the heart of our vision.
Growing better is not about adding complexity, but governing it.
This is the Tomato+ approach.
Multilayer optimization is an engineering discipline, not a layout exercise.
Those who approach it with calculations, simulations and real data achieve higher yields, stable costs and consistent quality.
Those who improvise it accumulate inefficiencies at every level.
Thank you for reading this article. Keep following us to discover new content on hydroponics, vertical farming and smart agriculture.
Tomato+ Team