In indoor systems and modern vertical farming, growing without data is impossible.
Sensors, software and artificial intelligence generate an enormous amount of information, but the real competitive advantage is not collecting data: it is knowing how to interpret it correctly.
Data-driven farming is not an abstract concept: it is the ability to transform biological and environmental signals into operational decisions that can be measured, replicated, and improved over time.
In this article we look at what data really matters, how to read it, and how to use it to increase yield, quality, and production stability.
Data-driven farming is an approach in which:
every growth parameter is measured
every variation is tracked over time
every decision is based on evidence, not hunches
In practice, the plant becomes a continuous source of data, not just a final output (the crop).
This approach is critical because:
it reduces systemic errors
makes production scalable
enables continuous optimization
enables artificial intelligence
To read growth data correctly, it is essential to distinguish them by level.
These are the most immediate and most widely used:
air temperature
relative humidity
COâ‚‚
air flows
water temperature
These data describe the context in which the plant is growing, but they still do not tell how the plant is responding.
Common mistake: optimizing only the environment without observing the biological response.
Here we begin to get into the physiology of the plant:
EC
pH
water consumption
nutrient uptake
changes over time
An isolated datum is of little value.
Variation in the datum over time is what signals stress, excess or deficiency.
Example:
Stable EC + slowing growth = non-nutritional problem
Rapidly declining EC = plant in high metabolic activity
This is the most advanced and most underestimated level:
growth velocity
leaf development
color and texture
visual patterns
uniformity among plants
This is where computer vision and AI come into play, enabling it to read signals invisible to the human eye before the problem becomes apparent.
It is on this level that data-driven farming becomes truly predictive.
Many plants collect data but do not generate value.
Why?
unrelated data
absence of history
lack of reference models
no feedback loop
The result is a dashboard full of numbers, but no automatic or suggested decisions.
Data become useful only when they are:
contextualized
comparable
linked to the plant's response
In evolved data-driven farming, the goal is not to "monitor," but to build and improve Growth Plan.
An effective Growth Plan:
defines environmental and nutritional targets
observes the plant's actual response
automatically corrects parameters
improves cycle after cycle
Here the data becomes operational, not just informational.
AI does not replace the agronomist, but it does what the human cannot do:
analyze millions of datapoints
find correlations that are not obvious
anticipate stresses and yield declines
adapt parameters in real time
In the Tomato+ model, each greenhouse is a node that:
collects environmental, nutritional and visual data
sends it to the cloud
helps train increasingly accurate growth patterns
The value is not in the individual plant, but in the network of distributed data.
A well-designed data-driven system enables:
reduce energy waste
avoid over-lighting
optimize cycles
prevent crop failures
standardize quality
This is especially relevant today, where energy and operational stability are the real bottleneck in vertical farming.
Data-driven farming changes one fundamental thing:
👉 you no longer grow a plant, you grow a growth model.
The plant becomes the physical validation of a digital system that learns, corrects and improves.
And this is the step that transforms a greenhouse from a "farming machine" to a scalable technology platform.
Those who do not read data, react to problems.
Those who interpret them correctly, prevent them.
In the vertical farming of the future, crop quality will depend less and less on the human hand and more and more on the ability to read, correlate and use growth data.
This is where the real competition lies.
Thank you for reading this article. Keep following us for new content on hydroponics, vertical farming, and smart agriculture.
Tomato+ Team