An Application of System Dynamics to Characterize Crop Production for Autonomous Indoor Farming Platforms (AIFP)

Abstract

Smart farming using technology-monitored controlled environment agriculture (CEA) has recently evolved to optimize crop growth while minimizing land use and environmental impacts, especially for climate-threatened regions. This study focuses on characterizing crop production using system dynamics (SD) modeling, which is a relatively new approach in CEA settings. Using tomatoes in a hydroponic growing system, we explore an alternative food resource potentially accessible to underserved areas in rural and/or urban settings under abrupt climate variability. The designed autonomous indoor farming platforms (AIFP) are equipped with the Internet of Things (IoT) to monitor the physiological parameters, including electrical conductivity (EC), pH, and water temperature (WT) associated with plant growth. Two varieties of tomato (Solanum lycopersicum) plants were used in this study with two different nutrient inputs (N-P-K ratios of 2-1-6 and 5-5-5) to assess the nutrient application impact on yield, especially focusing on the early stages of tomato to conceptualize and parametrize SD modes. Repeated measure analysis was conducted to investigate the effects of the environmental factors (EC, pH, and WT) in response to changing plant nutrients. The results show that different nutrient compositions (N-P-K ratios) have a noticeable effect on both pH and WT (p < 0.001) as opposed to EC. The study indicates that the proposed AIFP would be a promising solution to produce other crops for indoor farming in a changing climate. We anticipate that the proposed AIFP along with SD tools will be widely adopted to promote indoor farming in changing climates, ultimately contributing to community resilience against food insecurity in disadvantaged areas for years to come.