On the impact of light spectrum on lettuce biophysics: a dynamic growth model for vertical farming

Current crop growth models, whether process-based or data-driven, rarely incorporate spectral light composition, limiting their applicability in highly controlled environments such as vertical farming. This work enhances the predictive performance of a well-established process-based lettuce growth model by exploiting existing experimental evidence on the role of light spectrum in plant development. To this end, we introduce a parameter γ, modelled as a function of key spectral features (i.e. the Blue:Red and Far-Red:Red ratios), selected through machine-learning techniques. The resulting adjusted model (aVH opt[jls-end-space/]) is then validated on an independent literature dataset, showing a substantial reduction in prediction error compared to the reference model, with a more than 60% decrease in RMSE. The application of the aVH opt model to a commercial dataset confirms its capability to capture key spectral effects, but also reveals its sensitivity to environmental and biological variability not fully accounted for in the current formulation.