Strengthening forecast reliability across the tomato sector

Wim Peters Kwekerijen, a Netherlands-based greenhouse tomato grower, has chosen IUNU's LUNA AI platform to deepen crop visibility and improve forecast stability across its operation. The deployment, which includes IUNU's autonomous imaging masts, will expand the scale and consistency of plant-level measurement beyond what traditional sampling can provide.

For Wim Peters, the decision is rooted in a straightforward operational goal: knowing with confidence what the crop will do several weeks from now.

"We already measure our crop carefully," says Wilbert van Bussel, Operations Manager at Wim Peters Kwekerijen. "But sampling remains sampling. By measuring at a larger scale, we reduce uncertainty and improve our forecast stability several weeks ahead."

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Closing the gap between measurement and prediction
Weekly manual scouting has long been the standard in high-wire tomato production. It gives growers a read on crop condition but leaves meaningful gaps in coverage. Expanding that coverage manually means more labour and more cost, but without sufficient sample size the data loses its power to steer the crop with confidence. For commercial operations, that tradeoff has historically been difficult to resolve.

LUNA AI addresses this by capturing consistent, structured data on truss development, fruit load, and crop balance across the full growing area. With broader measurement coverage, the team at Wim Peters gains a clearer picture of what is developing in the crop and a more stable foundation for the production forecasts that drive commercial planning.

This partnership with a leader like Wim Peters Kwekerienj establishes another key account for IUNU in the Netherlands, a global hub for greenhouse innovation and a strategic growth market for the company.

"The growers who forecast most reliably are the ones working from data they can trust at a scale that reflects their full operation," said Adam Greenberg, CEO of IUNU. "Most recently we have seen forecast accuracy improve by over 10% by adding in a sample size that is relative to the crop versus manual based crop registration."

Reducing the burden of manual registration
Labour availability and cost remain persistent challenges across the sector. Weekly scouting and manual crop registration are time-intensive, and the data they produce varies depending on who is doing the work and when. By expanding measurement coverage through LUNA AI, Wim Peters reduces its dependence on manual registration while gaining data that is more consistent and more complete than sampling alone can deliver.

A platform built for the long term
Beyond day-to-day visibility, LUNA AI builds a rolling crop history over time. For Wim Peters, this means each season informs the next, creating a compounding record that supports more structured decisions on variety management, cycle optimisation, and labour planning. As crop registration becomes more consistent and less dependent on manual scouting, the team can redirect that time toward higher-value work in the greenhouse.

The deployment reflects the company's ongoing commitment to grounding production decisions in data that is consistent, comprehensive, and actionable.