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.

Lighting is not only a key mediator for the perception of the architectural space but also plays a crucial role regarding the long-term well-being of its human occupants. Future lighting solutions must therefore be capable of monitoring lighting parameters to allow for a dynamic compensation of temporal changes from the optimal or intended conditions. Although mostly based on synthetic data, previous studies adopting small, low-cost, multi-band color sensors for this kind of parameter estimation have reported some promising preliminary results. Building up on these findings, the present work introduces a new methodology for estimating the absolute spectral irradiances of real-world lighting scenarios from the responses of a 10-channel spectral sensor by using a convolutional neural network approach. The lighting scenarios considered here are based on a tunable white floor lamp system set up at three different indoor locations and comprise combinations of LED, fluorescent, tungsten, and daylight lighting conditions. For white light mixtures of the various spectral components, the proposed reconstruction methodology yields estimates of the spectral power distribution with an average root-mean-square error of 1.6%, an average Δu′v′ of less than 0.001, and an average illuminance accuracy of 2.7%. Sensor metamerism is discussed as a limiting factor for the achievable spectral reconstruction accuracy with certain light mixtures.

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.