PlantConnect explores human-plant interaction via the human act of breathing, the bioelectrical and photosynthetic activity of plants and computational intelligence to bring the two together. The system measures the photosynthetic and bioelectrical activity from an array of plant microbial fuel cells (P-MFCs) and translates them into light and sound patterns using machine learning. Microbial fuel cells (MFCs) are an emerging bioenergy technology for generating electricity from biomass using microorganisms found in diverse environments such as wastewater, soil and lakes. P-MFCs use naturally occurring and known processes around the roots of plants (typically aquatic plants) to produce electricity. Plants produce organic matter via photosynthesis under the influence of light. Much of this organic matter ends up in the soil where it is metabolized by the naturally occurring anaerobic bacteria there. These metabolic processes result in the release of electrons — also known as electricity. Plants also take in carbon dioxide (CO2) during photosynthesis, using the energy derived from the Sun to convert the CO2 into biomass (food). A byproduct of this process is oxygen, which humans (and many other animals) need to breathe. As we breathe out, we release CO2 into the atmosphere, which plants utilize in the manner just discussed. Thus, there is a tight interaction and interdependence between human and plant, which this project attempts to highlight. The plants’ CO2 and photosynthesis levels, along with their electrical activity are used as real-time analog signals by the PlantConnect system. In essence, the P-MFCs function as sensors, generating data about the plants’ metabolic activities.

The primary mode of participant interaction with the system is via breath. When a participant blows or whistles into a CO2 sensor located within the array of plants, it triggers an array of 16 grow lights that are directed at the plants and thus contribute to their photosynthesis. The photosynthesis levels are obtained from small measuring chambers containing CO2 sensors attached to each plant. In addition to an instantaneous audible response to the decreasing CO2 levels caused by the increased photosynthesis, these photosynthesis levels are translated into interpolation parameters for the virtual sound instruments and spatialization module of the system. Meanwhile the voltage signals from the P-MFCs are amplified so they can be read by a standard microcontroller. These signals are then analyzed to find the minimum & maximum voltage values, which are used to generate a set of adaptive thresholds that are sent in binary code to the light array. These thresholds determine the on/off patterns of the lights when they are triggered by human breath/CO2.

Using a blob detection algorithm, the system detects the on/off state of the lights in the light array as well as the general shape produced by the lights, relative to the background. This data is then sent to a clustering algorithm – a form of unsupervised machine learning. This algorithm recognizes similarities and differences in the repeating light patterns and classifies them into groups or clusters. Essentially performing rudimentary pattern recognition. This data is then sent to a Max/MSP application via OSC/UDP messages that control a set of virtual instruments and a spatialization module within the Max/MSP environment. In this way, the machine learning algorithm — and by extension the plants — select instruments and alter their amplitude, duration, frequency, and spectral parameters. They also select a spatialization state.

PlantConnect is a human-plant interaction system that combines the electrophysiological and photosynthetic activities of plants, the breathing of human interlocutors and the analytical abilities of intelligent computational systems to connect plant and human on a perceptual and physiological level. Part of larger investigations into alternative models for the creation of shared experiences and understanding with the natural world, the project explores complexity and emergent phenomena by harnessing the material agency of non-human organisms and the capacity of emerging technologies as mediums for information transmission, communication and interconnectedness between the human and non-human. The system measures the photosynthetic and bioelectrical activity from an array of plant microbial fuel cells (P-MFCs) and translates them into light and sound patterns using machine learning. Bioelectricity, light, sound, CO2, photosynthesis and computational intelligence form a circuit that enhances informational linkages between human, plant, bacteria and the physical environment, enabling a mode of interaction that is experienced not just as a technologically-enabled act of translation but as an embodied flow of information. In doing so, PlantConnect affords a novel experience of otherness, a heightened experience of otherwise mundane, unseen — but nevertheless vital — interdependent biological processes.

GitHub (https://github NULL.com/carloscastellanos/PlantConnect)