Analysis of Cross-Influence of Microclimate, Lighting, and Soil Parameters in the Vertical Farm

Abstract

Urban vertical farming is an innovative solution to address the increasing demand for food in densely populated cities. With advanced technology and precise monitoring, closed urban vertical farms can optimize growing conditions for plants, resulting in higher yields and improved crop quality. However, to fully optimize closed urban vertical farming systems, research is needed to enhance crop yields and reduce the growing season. The present study is focused on the research of the mutual influence of microclimate parameters, such as temperature, humidity, and carbon dioxide concentration, as well as the spectral composition of light, humidity, and amount of peat in the substrate. The research was conducted within the cultivation of the “Innovator” potato variety at the experimental automated vertical farm of the “Fundamentals of Biotechnology” of the Russian Academy of Sciences. Based on the correlation and Fourier analysis of the dependences of soil moisture and carbon dioxide concentration on time, it is shown that after watering potatoes, there is a 56 h delayed decrease in the concentration of carbon dioxide in the cultivation room, which can be explained by a delayed increase in the intensity of the photosynthesis process. Moreover, a comparison of CO2 dependence on time with the lighting dynamics at the scale of one day indicates the presence of the intrinsic daily biological rhythm of the CO2 absorption rate that does not depend on the external lighting conditions. In addition, by analyzing the dependencies of microclimate parameters and the spectral composition of the lighting over time, it was found that switching on lighting influences the microclimate parameters, which can be explained by the heating of LEDs used for lighting. Moreover, the multiple regression analysis of microclimate parameters and soil moisture showed that an increase in peat content in the substrate leads to a transition from the decisive influence of air humidity on soil moisture to the dominant influence of air temperature. The obtained results reveal the complex mutual influence of the parameters determining the growing conditions within automated closed vertical farms. Consideration of this influence is necessary when optimizing the conditions of vegetation and the development of intelligent plant-growing systems.

Keywords

• potato
• vegetation
• machine learning
• closed urban farm
• carbon dioxide
• LED