Predicting in advance how much yield you will get, how long a cycle will last, and when you will harvest is one of the most complex-and strategic-elements of vertical farming.
Indoor growing allows a level of control impossible in traditional agriculture, but it does not eliminate the need to accurately predict crop performance.
In this article we look at:
What yield and harvest times depend on
How to build a reliable forecast
The minimum data to monitor
The variables that distort forecasts
How automation and AI (such as that in Tomato+ greenhouses) completely change the process
Practical approach to estimating yield and cycles in an indoor facility
Yields in vertical farming do not depend on the "green thumb," but on a precise set of parameters:
Photoperiod (hours of light per day)
Light intensity (PPFD) and LED spectrum
Available nutrients and their balance
Air and water temperature
Relative humidity and VPD
Planting density
Genetics and variety
These parameters define the metabolic rate and, consequently, the expected growth time.
A realistic forecast does not start from intuition, but from a model consisting of three elements:
Each previous cycle provides:
growth curve,
fresh weight per plant,
waste,
germination and ripening times.
The more data you have, the more reliable the prediction becomes.
Stable prediction is only possible if environment and lighting are repeatable and controlled.
Otherwise, historical data become unusable.
One can start from a simple linear model:
"Given the same PPFD and temperature, variety X reaches biomass Y in Z days."
Or use advanced models such as those built into the Tomato+ system, which cross-reference:
thousands of data points per cycle,
plant images,
actual growing conditions, to return an optimized and updated cycle in real time.
To achieve forecasts with <10% error, at least these data per cycle are needed:
Final weight per variety
Actual growth time (germination → harvest)
Average PPFD by level
Nutrient consumption and EC/pH deviations
Water temperature (critical in hydroponics)
Daily VPD
Rejection rate and anomalies
In Tomato+ these data are automatically collected from sensors and telemetry, and stored in the AWS cloud to feed AI.
Even in a controlled plant there are factors that can compromise accuracy:
uneven PPFD between levels
root stress from incorrect water temperature
sudden changes in EC/pH
errors in seeding or density
non-uniform microclimate (insufficient airflow)
suboptimal light spectrum for that growth stage
Predictions only work if the environment is stable.
Tomato+ technologies make it possible to overcome the "human" logic of forecasting with a much more robust model:
Each greenhouse sends data on:
light received,
humidity,
temperature,
plant status via AI imaging.
AI automatically modifies:
LED intensity,
photoperiod,
nutrients,
climate parameters to keep the plant on optimal growth curves.
Each new Tomato+ installation adds data to the network and improves forecast accuracy for everyone.
It is a network effect applied to agriculture: more greenhouses → more data → more accurate models.
Expected weight per plant
Cycle days
PPFD and photoperiod
Water and air temperature
If environment and light fluctuate, forecasting is useless.
Each variety has a characteristic curve.
Check weekly:
predicted vs. actual biomass
growth delays
any stresses
In the Tomato+ system, the Growth Plan updates itself by correcting any delays.
Predicting yield and cycles in vertical farming is not a theoretical exercise:
it is a technical process based on data, environmental control and predictive models.
The future (already a reality for Tomato+) is no longer "estimating," but calculating in real time:
how much the plant will grow,
when it will be ready,
what yield will be obtained,
and how to optimize the next cycle.
Truly industrial production arises only when yield is no longer a surprise, but a guaranteed value.
Thank you for reading this article. Keep following us to discover new content on hydroponics, vertical farming, and smart agriculture.
Tomato+ Team