Universal modeling for optimizing leafy vegetable production in an environment-controlled vertical farm

An empirical growth-response model (GRM) that can accurately predict leafy vegetable (e.g., kailan) shoot fresh weight, in terms of photosynthetic photon flux density (PPFD) and certain cultivation duration counted from sowing, in an environment-controlled vertical farm, was developed. This GRM was constructed as the product of three independent models including light-time-biomass response model (LTBRM), dry-weight-based shoot/ seedling ratio (DSSR) and shoot fresh/dry weight ratio (SFDR), which were established separately, through using various mathematical models to fit the experimental growth data and selecting the optimal ones, respectively. The robustness verification, and the validation tests on GRM proved that this model is qualified for precisely forecasting kailan shoot fresh weight at the seedling stage. The framework built in this study can be introduced as a universal modeling approach in indoor farming to (i) quickly assess seedling productivity throughout the farm once PPFD distribution and duration are known, (ii) cooperate with artificial intelligence technology for growth prediction, (iii) confirm the transplantation date according to designated transplanting criteria to minimize electric energy loss, and (iv) increase final productivity by 29.41% and profit by 12.99% in vertical farms using an appropriate strategy (taking kailan as an estimated example). GRM could thus act as an excellent auxiliary tool for monitoring plant growth; it can also be a strategy to boost vegetable production in resource-dependent regions to handle unexpected food supply chain disruptions.