Vertical farming is considered to play a crucial role in future food supply. Until today, the high amount of electrical energy required for artificial lighting has been problematic in this context. Various possibilities for increasing efficiency through adapted lighting conditions have been and are being investigated. However, comparably little attention is paid to increasing utilance, i.e., the amount of photons that can effectively be used by the plant. In this work, a novel targeted lighting strategy is therefore proposed that allows for a dynamic adaptation of the luminaires’ light distribution to match the effective crop size at each stage of plant growth in a fully-automated manner. It is shown that the resulting utilance can significantly be increased compared to standard full-coverage lighting. Moreover, it is found that the proposed strategy is likely to consume less than half of the electrical energy usually required for the latter. An additional increase in system efficiency can be prognosticated and the potential energy savings are estimated based on assumptions of future LED generations derived from literature.

Precise determination of the photosynthetic photon flux density (PPFD) is important for illumination in modern horticultural systems. This paper presents two methods to calculate the PPFD of daylight spectra using low cost optic sensors. The first method uses the spectral sensitivity functions of spectral sensors to recreate the quantum sensitivity curve of quantum sensors. Two sets of spectral sensitivity functions are compared. The second method calculates the PPFD based on the calculated correlated colour temperature and a spectral reconstruction using the CIE daylight model. It is demonstrated that all methods offer a useful estimation of the PPFD with correspondingly similar daylight spectra, but the supposedly simpler method, which is based on weighting of the individual channels, is more stable against deviations from the CIE daylight model.

To meet the increasing demand for mass-produced microalgae, production processes must be optimised and monitored. The herein described optical sensors provide an instantaneous and direct opportunity to monitor biological and biochemical information for microalgae cultivation. Combination of expertise in natural science and engineering, as well as the application of novel data acquisition and analysis methods, are required to develop an appropriate system for industrial purpose. Prior to this, the correlation between biological and physical parameters must be determined. In this work, we combined colour science with the experimentally determined dataset from the cultivation of Microchloropsis salina. Our results indicate a resilient correlation between algal biomass and its specific pigment concentration with the determined reflection spectrum. Data evaluation allowed us to establish identification models for contactless quantification of the respective biological parameter in large-scale automated bioreactors.

Accurate light simulations using virtual plant models are essential for analyzing how plant structures influence the micro-light climate within canopies. Such simulations are increasingly important in applications including remote sensing, greenhouse optimization, and synthetic data generation for agricultural systems. However, many current models simplify leaf optical behavior by assuming purely diffuse reflectance, thereby neglecting the spectral and angular variability described by the bidirectional reflectance distribution function (BRDF). To address this limitation, the spectral BRDF of cucumber leaves was experimentally measured and corresponding Phong reflectance model parameters were determined for use in the GroIMP simulation environment. These parameters were optimized to replicate the angular and spectral reflectance distribution patterns and evaluated against a diffuse reflectance model. The Phong model successfully reproduced key features of the BRDF, particularly the increased diffuseness in the green and far-red spectral regions, although deviations in hemispherical reflectance emerged at high incidence angles. The resulting Phong parameters offer a practical method for incorporating wavelength- and direction-dependent reflectance into virtual plant simulations. These parameters can be adapted to other reflectance values of leaves with similar optical properties using hemispherical reflectance measurements, enabling more realistic light modeling in virtual canopies. Within a 30–60° incidence, the Phong BRDF reduced per-wavelength error relative to a diffuse baseline across all spectral regions.

Virtual plant models combined with ray-tracing simulations are an established tool for evaluating plant–light interactions. Current approaches often simplify leaf surface properties by assuming diffuse reflectance behavior, despite experimental evidence that leaf reflectance is direction-dependent across much of the visible spectrum. This study investigates whether incorporating measured, spectrally resolved and direction-dependent (BRDF) reflectance properties into these models affects simulation outcomes. Using virtual 3D cucumber (Cucumis sativus) plant models with PhongShader-based optical leaf characteristics for BRDF consideration, light absorption and local photon flux densities were simulated under a wide range of lighting conditions, including diffuse and directed sunlight scenarios. While total light absorption at the leaf level is only marginally affected (mean absolute percentage error, MAPE < 2%), spectral distortions in leaf surroundings, especially under direct light, exceeded 8% in the blue wavelength range. Beyond their relevance for estimating photosynthetic rates, such distortions directly affect the spectral composition within the canopy, which is particularly critical in greenhouse applications where optical sensors are used to monitor spectral ratios and, therefore, require the accurate prior simulation of canopy light conditions. This is particularly relevant for setups with directional artificial lighting. The findings suggest that BRDF modeling is not critical for calculating photosynthetic rates under most conditions, but is required in spectral analyses or for optimizing artificial lighting designs.

Accurate and continuous monitoring of plant development is crucial for managing closed cultivation systems effectively. Optical reflectance-based sensors offer a non-destructive, scalable, and cost-efficient method to track plant growth, physiological status and stress responses. However, existing vegetation indices predominantly utilize wavelengths in the near-infrared ( &gt; 800nm) range, a spectral range typically absent in horticultural LED lighting systems, limiting their applicability in horticultural LED-lit environments. This study investigates spectral metrics within the visible spectrum (440nm - 740nm) that robustly correlate with leaf order, a proxy for leaf age, in cucumber ( Cucumis sativus ) plants grown under controlled artificial lighting conditions. We systematically compared adaxial and abaxial leaf surfaces, identifying significant spectral variations associated with leaf order. Results demonstrate that abaxial leaf reflectance strongly correlates with leaf order, especially in the blue and red spectral regions. While adaxial reflectance showed weaker correlations, specific reflectance ratios (e.g., ρ ₇₂₀ /ρ ₇₀₀ ) enhanced this relationship, providing metrics suitable for sensor integration. These findings supply metrics for the development of simple optical sensors optimized for growth monitoring in greenhouse or indoor farming systems.