RAYN Growing Systems Introduces Lyda Luminaires, Horticultural Bar Lighting Solution
Lyda offers a 5000 K Sun Spectrum or a White + Deep Red option, depending on a facility’s spectral requirements. The luminaires feature smooth dimming capabilities from 10-100% and may be dimmed to ‘off’, resulting in a minimum quiescent standby power (<0.5 W).
RAYN Growing Systems, a leading manufacturer of lighting and control equipment for horticultural environments, introduces new Lyda luminaires. This cost-effective bar lighting solution delivers the latest in LED technology to greenhouses and research facilities.
Lyda offers a 5000 K Sun Spectrum or a White + Deep Red option, depending on a facility's spectral requirements. The luminaires feature smooth dimming capabilities from 10-100% and may be dimmed to ‘off', resulting in a minimum quiescent standby power (<0.5 W).
With a lightweight and narrow design, Lyda is easy to install, while also limiting shading on crops in greenhouses. It has the flexibility to be used in overhead, suspended-wire, and horizontal, interlighting applications. Lyda can also be ganged together in arrays of any size with no de-rating of capacity.
Lyda luminaires are long-lasting, IP66 rated, and convection cooled. Users can also adjust the beam angle to fit their needs with optional lenses.
With the addition of Lyda to the RAYN product line, growers can access affordable horticultural lighting in a small, adaptable package. Lyda is fully compatible with the remainder of RAYN's suite of advanced lighting, sensing, and control solutions.
Analysis of images when objects vary in color or size
How can a camera be taught to reliably detect deviations from the norm if they are not or not completely predictable? Rule-based image processing would have to capitulate - with the AI system IDS NXT, on the other hand, such a challenge can be easily solved from now on. In the new IDS NXT 3.0 release, IDS is making anomaly detection available to all customers as a third AI method, in addition to object detection and classification. You can even use only "GOOD" training images for training anomaly detection. In addition, relatively little training data is required compared to the other AI methods. This simplifies the development of an AI vision application and is well suited for evaluating the potential of AI-based image processing for new projects.