Indoor agriculture is no longer just about nutrients, light and irrigation-it is a cyber-physical system where crop quality depends on data quality.
In this article we examine the sensors that are truly critical to achieving reliable, repeatable, and optimized yields, explaining why the next generation of plants-such as Tomato+ plants-integrate machine vision, continuous telemetry, spectrometers, and advanced IoT protocols.
The first category includes the parameters that condition plant physiology. Without their continuous monitoring, no algorithm can be truly reliable.
They are the heart of the microclimate. They determine:
transpiration rate
mold risk
stomatal efficiency
growth rate
In Tomato+ greenhouses, the collection of these data occurs continuously, with secure upload to the cloud to ensure an up-to-date evolutionary model.
An often ignored but crucial parameter:
below 17°C → metabolic slowdown
above 24°C → root stress, lower oxygenation
Sensors built into the Tomato+ circuit allow automatic corrections, feeding AI-controlled dynamic Growth Plans.
Essential for photosynthesis. Even without forcing enrichment, monitoring it allows:
prevent CO₂-poor zones in multilayers
properly modulate the photoperiod
optimize leaf yield
Measures the concentration of nutrient salts.
Typical errors in unmonitored plants:
over-fertilization (accumulation in recirculation)
osmotic stress
decrease in yield
Continuous reading, as implemented in the Tomato+ system, allows adaptive feeding curves.
Closely related to actual nutrient availability.
Changes of 0.3-0.4 points are enough to reduce iron, calcium and magnesium uptake.
The most underestimated sensor of all.
An abnormality in flow is often the first sign of:
blockages
excessive roots
failing pumps
Tomato+ telemetry allows you to diagnose the problem before it becomes visible on the plant.
This is where the most radical transformation of indoor agriculture takes place. It is no longer a matter of "measuring the environment," but of measuring the plant directly.
Tomato+ greenhouses use 6-channel LEDs with a spectrometric sensor that detects:
actual light intensity at the floor
variations caused by plant morphology
shading, stress or uneven growth
This allows the AI to automatically correct the light curve, making each cycle more accurate than the previous one.
Each plane has built-in chambers that analyze:
wafer position and status
vegetative vigor
leaf color and stress indices
early occurrence of diseases
Images are compressed and sent to the cloud to feed the Dynamic Growth Plan engine, which optimizes parameters in real time.
Essential in multilayer.
They allow avoidance of:
contact with LEDs
localized microclimates
PPFD deficiencies or excesses
In an IoT ecosystem like Tomato+, sensors are not limited to the environment. They also monitor the infrastructure.
temperature of electronic boards
status of pumps
water pressure
diagnostics of LED modules
firmware integrity
Using AWS, MQTT, and a containerized architecture, any anomaly is recognized and handled with self-correction mechanisms or automatic notifications to the grower.
It is no longer enough to have a plant that "measures."
You need a plant that:
collects high-frequency data,
interprets it with AI,
automatically updates growth parameters.
This is what transforms a greenhouse from a simple piece of hardware to an AI-first platform.
And it is why Tomato+ is not a greenhouse with software, but an AI-powered SaaS company that uses the greenhouse as an execution tool.
Critical sensors in indoor agriculture are not just for monitoring: they are for growing better crops every cycle, thanks to a continuous flow of data that feeds AI.
The future of agrotech depends not on hardware, but on the quality and depth of telemetry.
And in this, Tomato+ systems are designed to build a cumulative competitive advantage over time.
Thank you for reading this article. Keep following us to discover new content on hydroponics, vertical farming, and smart agriculture.
Tomato+ Team