Understanding Plant Communication With Commercial Sensors

Plants communicate using terpenes, a form of volatile organic compounds (VOCs). These signaling chemicals are released due to abiotic and biotic factors, such as pathogens present in the air, defense against predators, or attraction of pollinators. Recent advances have shown that plants can be genetically modified to release terpenes in the presence of COVID-19. By combining genetically modified plants and VOC sensors, we can create energy efficient sensors that be genetically modified to sense different pathogens in the air. While some research exists on using plants as sensors, there are little to none that use commercially available cheap sensors to interpret the plant. Our study aims to integrate plant emissions into existing digital systems to interpret plant terpenes. We used 16 terpenes and the air quality sensors to determine which terpenes can be detected by these sensors. Monoterpenes such as Alpha-Terpinene, Citral, D-Limonene, and Cis-Beta Ocimene were picked up by the sensors. The detectable terpenes were used to explore how each of the terpenes spread in a room in an ideal world to see if they were identifiable. A simple mass based equation for VOC emissions from a concentration was used demonstrate physical world equations could not be used to classify the terpene in the room. Using the VOC data from the detectable terpenes and basil plants, we use machine learning to classify the chemical in the room. This paper provides the groundwork for how machine learning, VOC sensors, and plants can be combined to create new innovative sensors.