Spatial Spectral Mapping
By measuring the full spectral power distribution at regular intervals across the greenhouse—for example, every square metre—the lighting environment becomes a detailed, quantifiable “spectral map.” Unlike PAR or lux readings, wavelength-resolved measurements reveal how blue, red, far-red, green, and UV photons vary across the canopy due to fixture layout, shading, ageing LEDs, optical losses, or plant density.
These data are used to generate 2D heatmaps and 3D spectral surfaces, showing exactly where specific wavelengths are excessive, deficient, or imbalanced. The result is a precise visualisation of the spectral environment that plants actually experience, enabling identification of micro-zones that influence growth, morphology, uniformity, and stress responses.
AI-Enhanced Crop Performance Modelling
When spatial spectral maps are paired with crop outcome data—such as yield, morphology, flowering timing, or quality metrics—machine learning models can identify which wavelength patterns drive specific biological responses. AI can uncover relationships between spectral ratios, phytochrome balance, blue-light activation, and species-specific growth behaviour that traditional measurements cannot detect.
This enables predictive modelling and optimisation: forecasting crop performance, recommending spectral adjustments, and improving uniformity and efficiency across the greenhouse. The integration of spectroradiometry with AI transforms lighting from a fixed input into a data-driven, biologically tuned system designed to maximise crop performance with measurable scientific evidence.