Monitoring and Decision Support in Vertical Urban Farming : A Literature Review

Sammanfattning

Date: 2026-06-08 Level: Master's thesis in Industrial Engineering and Management, 30 ECTS Institution: School of Innovation, Design and Engineering, Mälardalen University Author: Franco Isha (2002-06-18) Title: Monitoring and Decision Support in Vertical Urban Farming: A Literature Review Examiner: Moris Behnam Keywords: vertical farming, monitoring, sensing technologies, decision support, processcontrol, controlled environment agriculture Research questions: RQ1: What types of monitoring data are identified in the literature asimportant for managing production conditions in vertical farming systems? RQ2: How aresensor-based monitoring technologies described in the literature as supporting process controland detection of deviations in vertical farming? RQ3: What gaps exist between the monitoringcapabilities described in the literature and the requirements of operational decision-making invertical farming systems? Purpose: The purpose of this thesis is to review and synthesize scientific literature on monitoringin vertical urban farming, with a focus on how plant-based, environmental, nutrient-related, andimaging-based data support process monitoring and operational decision-making in controlledproduction systems. The study examines how monitoring data and sensing technologies aredescribed in relation to process control, deviation detection, data integration, visualization, anddecision support. Method: The study was conducted as a structured qualitative literature review using secondarydata from scientific articles and other academic sources. Literature was collected throughdocumented searches in Scopus, ScienceDirect, and Google Scholar, supported by snowballingfrom relevant sources. The selection process was reported through search tables, screeningstages, and an adapted PRISMA 2020 flow diagram. The selected literature was analyzedthematically in relation to the three research questions. Three validation interviews wereconducted as a supplementary validation step to compare the literature-based findings withpractical experience from people familiar with vertical farming, controlled growing systems,monitoring, and decision support. Conclusion: The results show that monitoring in vertical farming is primarily based on fourcategories of data: environmental, nutrient-related, plant-based, and imaging-based.Environmental and nutrient-related measurements are the most operationally mature becausethey are easier to collect continuously and connect to process control. Plant-based and imagingbased data can provide more direct insight into crop response, but they are less integrated intoroutine decision-support systems. The study also shows that the main challenge is not theavailability of monitoring technologies, but the transformation of data into interpretation,diagnosis, recommendations, and process adjustment. The thesis concludes that vertical farmingneeds more integrated monitoring frameworks that connect sensing, data integration, operator interpretation, and operational decision-making.