Belgralux delivers spectrally accurate light measurement for greenhouses and controlled-environment agriculture. We quantify light intensity, spectral distribution, and uniformity at crop level, providing reliable data to evaluate lighting systems, support research, and inform evidence-based commercial decisions.
Spectrally accurate, on-site measurement of light at crop level across the full wavelength range.
Quantitative analysis of intensity, spectral composition, and spatial uniformity using validated methods.
Clear, decision-ready reports supporting system comparison, optimisation, and compliance.
We use spectrally resolved measurements rather than broadband or manufacturer-quoted values, ensuring wavelength-specific accuracy that reflects what crops actually receive.
All measurements are taken at plant level, capturing real spatial and spectral variation within the growing environment rather than idealised fixture outputs.
We assess lighting systems independently of manufacturer claims, enabling objective comparison across LED technologies, spectra, and layouts.
Results are delivered in clear, quantitative formats suitable for engineering decisions, research validation, and commercial evaluation.
Our approach aligns with both scientific measurement standards and real-world greenhouse constraints, bridging the gap between lab accuracy and commercial practicality.
Our approach is built around the high spectral resolution of our measurement instrumentation. By resolving light into fine wavelength bands rather than broad averages, our spectrometer captures subtle spectral features that directly influence plant response. This level of resolution enables accurate differentiation between lighting technologies, precise assessment of spectral tuning, and reliable validation of manufacturer specifications — providing insight that conventional light measurements cannot deliver.
Belgralux applies AI-based classification techniques to post-processed spectral and spatial light data. By analysing high-resolution light maps, AI is used to identify patterns, group similar spectral profiles, and classify lighting zones based on uniformity and spectral characteristics. This approach supports objective comparison, change detection over time, and clearer interpretation of complex datasets, while all conclusions remain grounded in physically measured, spectrally accurate data.