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Insight in your crops realtime fresh weight production

The new HortiSense grow module provides a grower with real-time insight in the performance of his crop: the module can calculate the assimilate production and from that also the fresh weight produce of his crop per m2. "This way a grower can see just how effectively his growth strategy works", Ad Zwinkels says.

It's starting to become a tradition: at the beginning of the year Hortilux releases an update or innovation to optimally deal with light. Last year the HortiSense platform was launched. This digital platform monitors and safeguards various grow light processes in the greenhouse through the use of sensors and PLCs. Currently over 100 hectares of greenhouse crops are connected to the HortiSense system, and the platform is ready for its next step. 

A part of the Hortilux’ team on the IPM Essen, showing the new module
Hortilux' Ad Zwinkels explains. "In the HortiSense Grow module, everything concerning light is visualised. The incoming light is put in relation to the CO2 and the climate. In this new update, we've added the calculation of the assimilate and the fresh weight production. It offers an insight in the effectivity of the crop: are you maximising the growth of your crop and if not, why aren't you?" 

It might sound like a simple addition, but a lot of maths and a complex system is behind this update. In collaboration with the plant specialist team of Plant Lighting and IT company First Consulting, the required plant physiological specs were entered into the system. They also made sure the system is self-learning, thanks to the data analysis machine learning system. 

"In the past, crop models were used on desks mainly, and were science material. By this transformation, growers can really work with it and profit from it", Sander Hogewoning (Plant Lighting) explains. "We measured and calculated the behaviour of light distribution and photosynthesis within a crop under various circumstances so that we get a 3d-picture of crop performance, established formulas for these reactions and incorporated them into the system." 

It was the first step, determining the crop behaviour, which was a considerable effort. "You have to know the plant by heart, and be able to determine the photosynthesis under various CO2 or light conditions. We also had to find out how light spreads out in the crop, since maturing leaves respond to light and CO2 in a different way. If you know all this, the production of assimilates can be calculated and by this, combined with factors such as respiration and assimilate distribution through the crop, we can calculate the fresh produce production."

For now this can be done for high-wired crops like tomato, other crops are expected soon.
Transforming plant physiological knowledge into digital models and making these models accessible is an important step for the horticultural industry, Sander and Ad agree. "Everybody knows the rules: 40 joules for a truss of tomatoes and 1 per cent more light is one per cent more production. We know reality is much more complex. Temperature, CO2, humidity and the LAI (Leaf Area Index, area (m2) of leaves per m2) all play an important role. From a certain level of light you see the extra amount of light you add is not transmitted into extra production. This is important information for growers who focus on their ROI. We've integrated and visualised it all in HortiSense."

By collecting more data and adding more machine learning, the model will become more and more accurate. "Growers are given the opportunity to steer with numbers instead of gut feeling." 

And this will be developed even further. "A more precise yield prediction gives you the opportunity to connect better with the market. That's an important focus for us. At Hortilux, our vision is to support growers completely and HortiSense can help us do that. It's not just about grow lights or about crops, it's about partnering with growers and helping them in every way with concrete information. That's being offered by HortiSense: why was your crop performing better or worse than normal on a certain point, and how can you use this information to improve your crop, your yield and your ROI." 

For more information:
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