Identifying Uncertainties In Air Temperature Data Of An Indoor Farming System

A small growth chamber was used to grow lettuce for 5 weeks during four air temperature trials, and an automated control system was used to control the environmental conditions of the air and root zone for the plants. A sensor array made up of low-cost arduino connected sensors would collect aerial (air temperature, relative humidity, CO₂ concentration) and root-zone data (water temperature, pH, EC, DO) and control the hydroponic system and carbon dioxide enrichment, while a reference sensor was used to collect aerial environmental conditions (air temperature, relative humidity, CO₂ concentration) to compare with the low-cost data sets. We hypothesized that our alternative decomposition method would successfully identify uncertainty occurrences in the data collected throughout this experiment since this data had many gaps in data when the data collection system would stop functioning or for other uncertainties. The small size of the growth chamber would also make any agricultural operations (any actions where humans would enter/exit and be inside the small chamber for any time), sensor failures, or other such uncertainties have a significant impact on system operations and reliability, making this decomposition method necessary for data quality control. Only the air temperature data from the low-cost and reference sensors was used to test the alternative decomposition method since the standard decomposition methods failed to successfully de-seasonalize the data.