What Does AI Do in the Greenhouse? Forecasting, Climate, Crop

What does AI do in the greenhouse?

AI in the greenhouse predicts and proposes; the grower still steers.

Four things a grower can measure: a harvest forecast per week that the sales desk can sell against, climate and irrigation setpoints proposed from the crop's response instead of the calendar, scouting that finds pests and disease before the scout does, and labour planned to the crop rather than the other way round. This guide says what each does, what it costs and where the grower's eye still wins.

By Tom Joseph · Last updated: 14 September 2026

Four jobs AI does in a modern greenhouse

Job	What AI does	What the grower keeps
Harvest forecasting	Predicts kilos per week per compartment from crop cameras, climate computer data and history	Adjusts for what the cameras cannot see; sells against the number
Crop steering	Proposes climate, irrigation and lighting setpoints from the crop's measured response	Sets the strategy, approves the changes, overrides on experience
Scouting	Finds pests, disease and stress on camera and drone images before they spread	Confirms and decides the treatment, within the IPM plan
Labour and logistics	Plans picking, pruning and packing to the forecast	Runs the team, handles the day that does not go to plan

What AI does not do in the greenhouse

It does not replace the grower. A model trained on three seasons has not seen the fourth; the week the crop turns for a reason nobody measured is the week the grower earns their keep. It does not fix sensors that drift or a climate computer whose data nobody has cleaned; the audit starts with the data the Priva, Hoogendoorn or Ridder system already holds. And a forecast is only worth what the sales desk does with it: a number nobody plans against is a screen, not a system.

Horticulture is not among the sectors the CBS AI Monitor covers, so there is no adoption figure to quote; the crop, the data and the grower's own records are the evidence base.

What it costs a grower

Three fixed steps: a €2,500 audit on a season of climate, harvest and labour data, which shows which forecast the business would act on and whether the data supports it; a €20,000 Production-ready MVP that forecasts one crop or one compartment next to the grower's own estimate for eight weeks; and production from €50,000, integrated with the climate computer, the harvest registration and the sales planning. Excl. VAT, all code and models handed over. See AI for agriculture and horticulture and the crop quality case.

Frequently asked questions

Can AI predict the harvest?

Yes, per week and compartment, from crop cameras, climate data and history, with an error margin the audit measures against the grower's own estimate. It is a number to plan sales and labour against; the grower corrects it for what the cameras cannot see.

Does AI control the climate computer?

It proposes setpoints from the crop's measured response; the grower approves them, and the climate computer (Priva, Hoogendoorn, Ridder) executes. Fully autonomous control exists in research and pilot settings; in a commercial greenhouse the grower's approval stays in the loop.

Can cameras find pests and disease?

Yes, on camera and drone images, often before a scout would, for the pests and symptoms the model has been trained on. The scout confirms and the treatment follows the integrated pest management plan.

What data does a greenhouse need for this?

A season of climate-computer data, harvest registration per compartment and labour hours, plus crop images where forecasting or scouting is the goal. The audit checks whether the data is clean enough before anything is built.

What does AI cost a grower?

At Crux Digits: a €2,500 audit, a €20,000 Production-ready MVP for one crop or compartment, and production from €50,000, all excl. VAT, with code and models handed over. Cameras are quoted separately at cost.