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Climate, irrigation and yield

Greenhouse Farming

Greenhouse performance depends on thousands of climate and irrigation decisions made throughout the crop cycle. Many of those decisions still depend heavily on grower experience and manual adjustment. When that knowledge is not captured systematically, it becomes difficult to understand why one crop performed better than another, reproduce a successful strategy at another site, or maintain the same level of control when an experienced grower is unavailable.

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What goes wrong, and what it costs

Yield inconsistency
Each greenhouse site experiences varying degrees of yield inconsistency due to fluctuating environmental conditions, affecting the quality and quantity of produce.
Resource management
The grower constantly juggles water, nutrients and energy to hit quality targets and reduce waste, but lacks precise control over these resources.
Qualified expertise and retention
Finding skilled expertise that can follow strict growing protocols is a challenge, resulting in a knowledge gap and operational inefficiencies.
Technology optimization
The grower's hands-on approach leaves them stretched thin, with less time to focus on strategic planning and research for yield improvement.
Operational visibility
With a manual, piecemeal approach to data collection and analysis, the grower has limited visibility into the real-time performance of the growing operations.

Proof

2×
Winner, Autonomous Greenhouse Challenge 2018 and 2022, a live crop each time
17%
Higher net profit than the expert growers 2018 competition, with 6% higher yield
19.5%
Yield increase on cucumber against a control zone grown manually alongside it
17%
Energy savings in the same commercial trial

Tested in international competition and in commercial production, and the 2018 competition run was the only AI entry to finish ahead of the expert growers on both yield and net profit. That is the difference between software that recommends a climate strategy and a system that calculates and applies control decisions, continuously.

What that gives a grower

Control that considers both climate and crop
KoPilot combines greenhouse climate models with crop information and grower-entered crop registration, so control decisions can reflect both environmental conditions and crop development.
Crop registration where the measurements are taken
Growers can record crop measurements and observations from a phone or tablet in the greenhouse, reducing the delay between observing the crop and making that information available for analysis and control.
Operational context in one place
Climate data, crop measurements, photographs, alarms and maintenance history can be connected by compartment and time, making it easier to understand what happened and why.
Alerts that reach the right person
When a climate or equipment condition needs attention, notifications can be delivered directly to the people responsible rather than remaining on a control-room screen.

Already evaluating another autonomous growing system? See how Koidra compares with Blue Radix and Hoogendoorn.

One record for the greenhouseThe climate computer and the grower's tablet both write into one record, held by compartment and time. From it an alert goes to the person responsible, and KoPilot adjusts the climate with the crop in view.Climate computertemperature, humidity, screens, irrigationOne recordby compartment and timeGrower's tabletcrop measurements, photos, notesAlertto the person responsibleKoPilotclimate control that sees the crop
The climate computer and the grower write into the same record. That is what lets an alarm reach a phone, and what lets control see the crop.

For a hi-tech greenhouse with a climate computer

A hi-tech greenhouse starts with DataPilot, because the climate computer is there to connect and the growers’ crop registration comes with it, or with Maintenance Management, which needs nothing connected. AI Advisory needs both and comes with both, and KoPilot runs the climate on top of that record.

The path for a hi-tech greenhouseA hi-tech greenhouse with a climate computer starts with DataPilot or with Maintenance Management. Both lead to AI Advisory, which needs both, and KoPilot runs the climate on top.DataPilotclimate computer connected,crop registration on a tabletAI Advisorya weekly summary,and what to changeMaintenance Managementscreens, vents and pumps,kept simpleKoPilotclimate setpoints,continuously
Two ways to start, one layer that needs both, and control on top. Each product pays for itself before the next exists, so the path can stop at any point.

For a mid- or low-tech greenhouse

A greenhouse without an advanced climate computer takes a different path. DripPilot is a separate product: autonomous irrigation on its own controller, for the houses where water is the biggest lever and there is no climate computer to build on. It is not a step on the path above, and not an add-on to KoPilot.

DripPilot installed in a greenhouse. The Koidra irrigation controller on the wall beside the pump and valve pipework, the labelled valve relays it drives, and the tomato rows it waters.
DripPilot on the wall of a greenhouse that runs without an advanced climate computer.

What each product gives a greenhouse like yours

Two ways in, a layer that needs both, and control on top. Each is a complete product with its own payback. Here is what they do here rather than in general.

  1. Start here

    DataPilot

    Climate computer data, irrigation activity, drain percentage and the crop measurements your growers enter sit together by compartment and time. A change in the crop can be traced to what the climate did, and an alarm reaches the person who has to respond rather than a control-room screen.

    A grower in a greenhouse holding a tablet showing Koidra dashboards

  2. Start here

    Maintenance Management

    Screens, vents, pumps, boilers and drip lines each keep their own service history and check dates, so a problem is dealt with before it reaches the crop. There is no maintenance department to run: a job goes to the grower who handles it, or to the service company you call, and closes with a photo.

    Maintenance in a greenhouse, kept simpleA pump stops, a screen sticks or a boiler check falls due. A job opens against that piece of equipment and goes to whoever handles it, your own team for the small things or the service company you call for the rest, and closes with a photo. The history stays with the equipment.Something needs attentiona pump stops, a screen sticks,a boiler check falls dueOne job, against that equipmentwhat, where, by whenYour own teamthe small thingsThe service company you callboiler, screens, climate computerClosed with a photothe history stays with the equipment

  3. Comes with both

    AI Advisory

    A regular summary of what the crop experienced, daily light integral, 24-hour average temperature and vapor pressure deficit, against the strategy you intended, with a recommendation on what to adjust. The target you name, as a number or in words, is what the recommendation aims at.

    AI Advisory in a greenhouseFrom the climate record and the crop registration it produces a weekly summary of what the crop experienced against the intended strategy, answers a grower's question in plain words, and recommends what to adjust toward the target the grower set.Youyour strategy, your target,your questionsThe greenhouse recordclimate computer, crop registration,irrigation, equipment checksWeekly summarydaily light integral, 24-hour temperature,vapor pressure deficit, against your strategyYour question, answeredwhy did compartment 3 fall behind?What to adjusttoward the target you named

  4. Autonomous control

    KoPilot

    Screen, vent, heating and other climate setpoints adjusted continuously from current conditions, crop objectives and the weather forecast, inside limits you set for the greenhouse. The strategy runs the same on every shift, whoever is on.

    How KoPilot runs a greenhousePlant and climate sensors in the greenhouse feed the climate computer, which sends the greenhouse data to KoPilot. KoPilot reads the weather forecast directly, and the grower adds crop registration, the strategy and the limits. KoPilot decides the growing conditions round the clock and sends setpoints back to the climate computer, which moves the screens, vents, heating, lighting and CO2.greenhouse datadataforecastcrop registration, strategyand limitssetpointsPlant and climate sensorsWeather forecastGrowerClimate computerKoPilot, round the clockScreens, vents, heating, lighting, CO2

  5. Mid- and low-tech greenhouses

    DripPilot

    For greenhouses that run without an advanced climate computer. DripPilot uses irrigation, climate and root-zone information to manage irrigation timing, volume, drain percentage and electrical conductivity, on its own controller. It is a separate product, not a step on the path above and not an add-on to KoPilot.

    How DripPilot runs irrigationA pyranometer, a temperature and humidity sensor and a soil moisture sensor in the greenhouse send their readings over LoRa to a gateway, which feeds the DripPilot controller. The controller sends the readings to the AI engine and receives setpoints back, and drives the mixing valves and the water pump.What it movesIn the greenhouseLoRaLoRaLoRasensor readingsreadingssetpointscontrolcontrolPyranometerTemperature and humiditySoil moistureLoRaWAN gatewayDripPilot controllerAI engineMixing valvesWater pump

What we measure here

What our customers say

We wanted a ‘digital assistant grower’ — someone who never sleeps, reacts instantly to live data, and follows our instructions without bias. Our priority was speed and accuracy, especially for light and CO₂ strategies, because they drive a large part of the budget. KoPilot promised quicker decisions and tighter control in those areas. Dynamic lighting that factors in real-time electricity prices and radiation is a game-changer — saving energy without sacrificing crop performance.
Octavio Perez Rodriguez Director & Growing Operations, Nature Fresh Farms
I have seen firsthand the impact of Koidra’s DataPilot on controlled environment agriculture. The ‘actionable insights’ feature has been pivotal in translating real-time data into strategic actions, significantly supporting us to optimize growing conditions. What I like the most is that DataPilot allows me to easily show sensor readings from the climate controller and separately installed sensors in the same dashboard. Another feature I like is that creating new insights using raw data and advanced calculations is relatively simple.
Dr. Chieri Kubota Lead Project Director, The Ohio State University

See Koidra on your greenhouse data

Bring a week of readings from one compartment. We will show you what the platform sees, what it would have flagged, and what it would have scheduled.

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