Toward Digital Twin-Driven Mushroom Cultivation: A Framework for Optimizing Growth, Yield, and Quality

Abstract

Mushroom cultivation is already a highly controlled biological production process. Temperature, relative humidity, carbon dioxide concentration, ventilation, substrate conditions, watering, and cultivation time are carefully managed, yet most strategies still rely on predefined setpoints and empirical experience. In this editorial, we argue that digital twins could provide the next step: not another monitoring platform, but a continuously updated virtual representation of the crop-production system. Such a twin should combine environmental measurements, substrate information, crop development, historical production data, and predictive models to estimate the current crop state and evaluate alternative cultivation strategies. The objective would not simply be better environmental control, but adaptive optimization of yield, quality, resource consumption, and production time. Mushroom cultivation therefore offers an unusually suitable environment in which digital twins could evolve from an engineering concept into a practical tool for biological production.