A r t i s t ’ s A r t i c l e
PlantConnect
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This paper describes PlantConnect, a real-time interactive system that
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. Part of larger investigations
into alternative models for the creation of shared experiences and
understanding with the natural world, the work is presented as a
concrete implementation of a possible model based upon reciprocal
interplay and information flows between human and nonhuman worlds.
For some time, artists have increasingly utilized computa-
tional media technologies to create interfaces with living
organisms and the natural environment [1] in what artists
Rasa Smite and Raitis Smits have called “emerging techno-
ecological” practices [2]. Many of these artworks feature
systems that might be referred to as biocybernetic inter-
faces: the bridging of the living world with the computa-
tional world. Artists marshal technologies such as robotics,
bioenergy technologies, machine learning, and computer
vision to translate the often-unseen processes of nonhuman
organisms to human sensory ratios, in speculative attempts
at creating some kind of meaningful and aesthetically potent
connection between the two. Smite and Smits’s notion of
techno-ecologies extends French philosopher Félix Guat-
tari’s notable work The Three Ecologies to include our cur-
rent immersion within technologies [3]. Guattari argues for
more subjectivity in science. In extending the definition of
ecology to encompass social relations and human subjectiv-
ity as well as environmental concerns, Guattari states that
the boundaries between nature and technology need to be
collapsed if we are to address the ecological crisis properly.
Learning to think “transversally,” or across disciplines and
systems of ideas, is a crucial step toward the goal of devel-
oping the alternative ontologies, epistemologies, and social
relations necessary for ecological sustainability. As Italian
Carlos Castellanos (artist, researcher, educator), Rochester Institute of Technology,
School of Interactive Games and Media, One Lomb Memorial Drive, Rochester, NY
14623, U.S.A. Email: carlos.castellanos@rit.edu. Website: www.ccastellanos.com.
ORCID: 0000-0002-5734-4723.
See https://direct.mit.edu/leon/issue/56/4 for supplemental files associated with
this issue.
philosopher Rosi Braidotti notes, this transversality must
“include the relational dependence on multiple nonhumans
and the planetary dimension as a whole” [4]. Exemplified by
Smite and Smits’s own Biotricity—which features the sonifi-
cation of bacterially generated electricity and microbial fuel
cell (MFC) technology [5]—these works can act as cultural
interfaces to this transversality, bridging diverse groups and
practices (including humans and nonhumans) to establish
new dialogues around sustainability. Overall, the integra-
tion of biological systems has had an almost visceral appeal
to artists, since systems may exhibit unexpected or uncon-
ceived patterns of behavior that purely digital or mechani-
cal systems may not. In addition, many artists are attracted
to the thematic blurring of boundaries between digital and
biological worlds as ways of experiencing the enigmatic
“otherness” of nonhuman species [6]. Whether referred to
as bioart, environmental art, or by a myriad of other names,
the focus on human-nonhuman-environmental interactions
resonates across these practices. Through various types of
process-driven practices that feature combinations of liv-
ing matter and emerging technologies, artists not only are
exploring how these systems can serve as vectors of novelty
and unexpected variety but also are forging new aesthetics
and systems of ideas focused on showcasing alternative pos-
sibilities of human-nonhuman relations in the age of climate
change and environmental degradation.
The interactive installation PlantConnect (Fig. 1) explores
the possibilities of these technologically mediated encounters
between human and nonhuman agents by combining plants,
bacteria, and MFC technology in a real-time system that fea-
tures the human act of breathing, the bioelectrical and pho-
tosynthetic activity of plants, and computational intelligence
to link the two together. In PlantConnect the photosynthetic
activity from an array of plant-microbial fuel cells (P-MFCs)
and the bioelectrical activity of bacteria in the plants’ soil
are measured and translated into light and sound patterns
using machine learning. Part of larger investigations into al-
ternative models for the creation of shared experiences and
©2023 ISAST
https://doi.org/10.1162/leon_a_02306
LEONARDO, Vol. 56, No. 4, pp. 337–343, 2023 337
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[7]? Can we create an ontological model of the world as one
consisting of open-ended interactions and reciprocal inter-
play between all the life and matter in it, one that accounts
for the agency of matter and all the living things on Earth in
addition to humans and intelligent machines [8]?
A prImEr on mIcroBIAl FuEl cEll TEchnology
MFCs are an emerging bioenergy technology for generat-
ing electricity from biomass using microorganisms found in
diverse environments such as wastewater, soil, and lakes [9].
Essentially, MFCs are batteries. They convert chemical energy
to electrical energy via the action of anaerobic bacteria that
metabolize organic matter. Generally, MFCs are used under
the conditions of an aerobic cathode with air or oxygenated
water and an anaerobic anode in wastewater or other organic
matter (Fig. 2). The organic matter is metabolized by the bac-
teria, generating electrons and protons. The electrons attach to
the MFC’s anode, while at the cathode, oxygen together with
electrons and protons are chemically reduced to water. Posi-
tive hydrogen ions are also released and are directed through
the membrane to the cathode side. In dual-chamber designs,
a proton exchange membrane is used as a separator between
the cathode and anode, while single-chamber designs rely on
the organic material (e.g. soil) as a natural separator, where the
bottom is under anaerobic conditions and the top is aerobic
(the cathode is exposed to air or oxygenated water). In addi-
tion to generating power, MFCs can also be used as part of or
in conjunction with waste processing systems and remediation
of contaminated lakes and rivers [10]. Overall, MFCs offer a
very different approach to power generation and wastewater
treatment, as the treatment process can become a method of
capturing energy in the form of electricity or hydrogen gas
rather than a drain on electrical energy.
Fig. 1. PlantConnect was exhibited in 2019 at the Asia Culture Center in
Gwangju, South Korea, as part of the Arts & Creative Technology (ACT)
Festival and the International Symposium on Electronic Art (ISEA 2019).
(© Carlos Castellanos)
understanding with the natural world, the project explores
complexity and emergent phenomena by harnessing the ma-
terial agency of nonhuman organisms and the capacity of
emerging technologies as mediums for information trans-
mission, communication, and interconnectedness between
the human and nonhuman. By staging interactions among
human, nonhuman, and machine agencies, PlantConnect
contributes to dialogues not only on sustainable futures but
also in reimagining the nature of our relationship to the en-
vironment and the nonhuman world more broadly. Might we
be able to construct experiences using computational intel-
ligence and living organisms that feature what science and
technology scholar Andrew Pickering calls a “performative
ontology” that does not separate people and (living) things
external circuit (load)
Cathode
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Anode
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electron flow
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anaerobic
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anaerobic chamber
(mud)
aerobic chamber
(water)
semi-permeable
proton exchange membrane (PEM)
Fig. 2. Single-chamber (left) and dual-chamber MFC designs. (© Carlos Castellanos)
338 Castellanos, PlantConnect
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1 PLANT-BASED
MICROBIAL FUEL CELLS
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The core element of the system is based on an array of plant micro-
bial fuel cells (P-MFC). Plants produce organic matter via photosyn-
thesis under the influence of light. Much of this organic matter ends
up in the soil where it is metabolized by anaerobic bacteria (such as
those from the genus Geobacter), resulting in the release of elec-
trons (electricity). To capture these electrons, the chamber design
includes graphite granules and felt, gold or stainless steel elec-
light patterns
trodes attached to teflon-coated copper wire and perhaps a
semi-permeable membrane to separate the anode and cathode
portions of the P-MFC. Voltages between 100-800mV are typically
BLOB DETECTION
3 COMPUTER VISION
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 pro-
duced by the lights, relative to the back-
ground. This data is then sent to a machine
learning algorithm.
CLUSTERING
ALGORITHM
1.0
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produced and, along with CO2 and photosynthesis levels, are used
he sy
as real-time analog signals by the system. In essence, the P-MFCs
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function as a sensors, generating data about the plants’ bioelectri-
ensors, generating
cal activity.
relay module
VOLTAGE &
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ALGORITHM
2 BIOSIGNALS, C02 &
PHOTOSYNTHESIS
TO LIGHT PATTERNS
co2 sensor
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DIGITAL
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microcontroller
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ANALOG
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amplifier
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DIGITAL
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microcontroller
co2 sensor
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 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 spatial-
ization module of the system (see section 5). Meanwhile the voltage signals
from the P-MFCs are amplified so they can be read by a standard microcon-
troller. 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.
4 PATTERN RECOGNITION
Clustering – a form of unsupervised machine learning
– is applied to the blob data. This recognizes similari-
ties and differences in the repeating light patterns,
classifies them into groups or clusters. This data is
then sent to a Max/MSP application that will use the
data to trigger changes in instrumentation, frequency,
duration, amplitude and spatialization state.
INTERPOLATION
PARAMETERS
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PLANTCONNECT
Fig. 3. PlantConnect system diagram. (© Carlos Castellanos)
0.2
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5 SOUND SCORE
The light patterns and real-time clustering are trans-
lated into a series of 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 is used to select
instruments and alter their amplitude, duration,
frequency, and spectral parameters, as well as select
a spatialization state.
MAX/MSP
INSTRUMENTS
SPATIALIZATION
synthesizers
speaker array
PlantConnect uses an array of 16 P-MFCs as the core el-
ement of the system. P-MFCs use naturally occurring and
known processes around the roots of plants (typically aquatic
plants) to produce electricity [11]. The plant produces organic
matter via photosynthesis under the influence of sunlight.
Most of this organic matter ends up as root material or exu-
dates in the soil, where it is metabolized by anaerobic bacte-
ria, resulting in the release of electrons as described above.
ThE PlantConneCt SySTEm
As shown in Fig. 3, a participant blowing or whistling into
a carbon dioxide (CO2) sensor located within the array of
plants causes the CO2 levels to surpass a baseline thresh-
old. This in turn triggers an array of 16 grow lights and a
set of software sound instruments. Participants thus receive
an immediate visual and sonic response. The lights are di-
rected at the plants (from 2 m above) and thus contribute to
their photosynthesis. There is one light for each plant. The
photosynthesis levels are obtained from housings containing
a plant and a CO2 sensor placed near it (discussed below).
When the light above the plant turns on, it causes the CO2
levels near the plant to decrease. These levels are translated
into interpolation parameters for the software sound instru-
ments and spatialization module of the system. Meanwhile,
the voltage signals from the P-MFCs are read by a standard
microcontroller and analyzed to find the minimum and
maximum voltage values. These thresholds determine the
on/off patterns of the lights when they are triggered by hu-
man breath/CO2. Once the CO2 levels on the breath sensor
fall below the baseline threshold, the lights turn off. This can
take anywhere from 1 to 10 seconds.
Using two digital video cameras and a simple blob detec-
tion algorithm, the system then detects the on/off state of
the lights in the light array, relative to the background. These
data are then sent to a clustering algorithm that performs
rudimentary pattern recognition. The data are then sent to
the sound instruments and spatialization module to create
the generative sound environment. In this way, the machine
learning algorithm—and by extension the plants—select
instruments and alter their amplitude, duration, pitch, and
other parameters.
To initiate a response from the system, a participant will
typically blow or whistle into the CO2 sensor located in the
center of the space. This triggers each grow light to turn on
but only if the voltage of its associated P-MFC is above the
requisite threshold. The result is an unpredictable and varied
pattern of lights and sound that is experienced as a reaction
by the plants to human breath and light. The entire sound,
computer vision, and machine learning portion of the sys-
tem was built using Cycling ’74 Max [12]. The project runs
on two Apple Macintosh computers. One computer (the
“CV/ML” computer) handles the computer vision and ma-
chine learning tasks, while the other (the “sound” computer)
handles generative sound and communication with the mi-
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Castellanos, PlantConnect 339
crocontroller. Data are sent from the CV/ML computer as
User Datagram Protocol (UDP) messages from Max over a
standard Ethernet connection to the sound computer that is
also running Max, with the sound instruments loaded. The
sensor readings, P-MFC voltage readings, and light control
system were built on the Arduino microcontroller platform.
The following subsections detail each element of the system
mentioned above.
plants and plant Housings
The plants used in this project are Oryza sativa, commonly
referred to as Asian rice. The P-MFCs were built using carbon
fiber as the anode and cathode material. The anodes are at-
tached to insulated copper wire inside a waterproof acrylic
housing (Fig. 4). They are then submerged about 5 cm below
the surface, so they are not exposed to oxygen. The plants
are housed in enclosures made of wood and clear vinyl (Fig.
1). Although the housings serve an aesthetic purpose in the
piece, they are necessary for properly measuring changes in
CO2 absorption from the plants themselves, irrespective of
changes in the surrounding CO2 levels in the space. They also
provide sufficient ventilation to allow for adequate airflow
(thus not risking the CO2 level continually rising inside the
housing) [13].
namic thresholds. A total of four thresholds are generated,
one for each light and P-MFC in the group. These thresholds
determine which lights activate and thus determine the on/
off patterns of the light array. Each of these values will set
each light in the group to an active state successively in a
clockwise manner when it is surpassed. When a participant
blows on the CO2 breath sensor (and the sensor value goes
above the predetermined threshold), it will trigger the ac-
tive lights to turn on. The lights themselves are 20-watt LED
grow lights that emit a warm white color. They are connected
through two 8-channel relays, which are controlled by the
Arduino. When plants are actively photosynthesizing, they
absorb greater amounts of CO2 than when they are not pho-
tosynthesizing (e.g. at night). In PlantConnect, the P-MFCs’
levels of photosynthesis are obtained by measuring CO2 near
the plants. The sensor returns the CO2 levels in parts per mil-
lion and sends the data to the Arduino, along with the breath
CO2 sensor. This sensor is also connected via serial/RS-232
to the Arduino. Here, we keep a running median of the nine
most recent CO2 readings. This helps to establish a baseline
level with respect to the surrounding environment. Thus, the
threshold for triggering lights and sound is a predetermined
level above this baseline (20 ppm by default). CO2 readings
are taken at a rate of two per second.
Biosignals and light control
computer Vision
All signal acquisition and light control are handled by a
single Arduino Mega 2560 microcontroller [14]. Acquiring
voltages from the P-MFCs is a simple matter of connecting
each cathode (which in this case is the positive lead) to an
analog input of the Arduino. However, the voltages are not
acquired from each individual P-MFC. Instead, groups of
four P-MFCs are wired together in series to make a single
voltage source that is then connected to an Arduino analog
input. As there are 16 P-MFCs, this amounts to four groups
of four P-MFCs (hereafter referred to as “P-MFC groups”)
and thus a total of four voltage sources.
While the system is running, the voltage signals in each
P-MFC group are analyzed and used to generate a set of dy-
Fig. 4. P-MFCs showing the rice plant, carbon fiber anode and cathode, and
waterproof acrylic housings. (© Carlos Castellanos)
A simple blob detection algorithm is used to differentiate
the lights from the background. This is a relatively sim-
ple task as the piece is installed in a rather dark space. To
achieve blob detection easily and reliably within the Max
environment, a third-party library, cv.jit [15], was used. The
cv.jit.blobs.centroids object returns a list of blob centroid co-
ordinates. Two USB digital video cameras [16] were mounted
between the plants and the grow lights (just over two me-
ters from the floor) to provide the video feeds. They were
pointed directly at the lights and connected to the CV/ML
computer running Max. The two video feeds were combined
and together produced a streaming image that captured all
the lights in the space. The video feed was then algorithmi-
cally cut up into four rows and eight columns for a total of
32 cells. In its default configuration, the video image is 640 ×
360 pixels. Thus, each grid is 80 × 90 pixels. This grid is the
reason for using blob detection (as opposed to simply read-
ing the on/off states from the microcontroller). The size of
the lights and the fact that each light panel takes up to 500
milliseconds to reach full brightness means that a single light
may register as more than one blob since it may spill over to
another row or column. Both factors serve to add an element
of variety and aleatoric behavior to the system (for example,
the system may very quickly switch between several different
cluster assignments for the same light pattern, resulting in an
erratic, “glitchy” sound).
Blobs are analyzed, and the x/y coordinate of each blob’s
centroid (center of mass) is returned. A list of 32 binary num-
bers corresponding to the location of each centroid within the
grid of 32 cells is then output, with 0 being “off ” and 1 being
“on.” This list determines which “voice” of the sound instru-
340 Castellanos, PlantConnect
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ment (which essentially corresponds to pitch) gets played. For
example, if the first light is turned on and the blob centroid
is located at pixel location (40, 55), index 0 (the first item in
the list) will be set to 1 and thus will trigger the sound instru-
ment to play its lowest pitch. Depending on how the system
is configured, the duration of each triggered voice is set to a
fixed amount at runtime or is determined by whether the blob
detection algorithm recognizes the light (essentially if the light
is on, its corresponding voice stays on). Pitches (or which voice
gets played) are arranged left-to-right and top-to-bottom in
the 4 × 8 grid. Thus, the first voice 1 would be the top left and
voice 32 would be the bottom right of the grid.
clustering/pattern recognition
The same list of 32 binary numbers that is sent to the sound
instruments is also sent to the machine learning module.
We then apply a fuzzy c-means clustering algorithm to the
data. Fuzzy c-means clustering (FCM) [17] is a method of
clustering (a type of unsupervised machine learning) that
allows a given data point to belong to more than one cluster.
A membership grade (in our case, a floating-point number
between 0.0 and 1.0) is calculated for each data point that in-
dicates the degree to which that point belongs to each cluster.
Frequently used in pattern recognition tasks, FCM assigns
membership in a cluster by calculating the distance between
the cluster centroid and the data point. The closer the data
point is to the cluster centroid, the higher its membership
grade for that cluster (i.e. the closer it is to 1.0).
In PlantConnect, we use the ml.fcm object from the ml.*
package for Max [18]. We first initialize the object by assign-
ing it a fuzz coefficient of 1.05, selecting the number of clusters
to calculate (in our case, four) and a termination threshold
of 0.01 (the default). The fuzz factor effects how “crisp” or
“fuzzy” the cluster memberships are (higher numbers return
fuzzier membership grades), while the termination thresh-
old affects the speed and accuracy of the cluster calculation
(higher values produce quicker, more approximate clusters).
We then generate 1,000 random data points as a training set.
Each data point has 32 dimensions (corresponding to the
possible location of each centroid within the grid of 32 cells)
and consists of ones and zeros. Once this is done and the
live video feed is turned on, the system is ready to perform
real-time clustering of incoming light patterns. When the
system is running and new data on the light on/off patterns
are received, a query is made to the ml.fcm object, which then
outputs a list of four membership grades (one for each clus-
ter, between 0.0 and 1.0). These numbers are used to set the
volume of each sound instrument (0.0 = minimum volume,
1.0 = maximum volume). In essence, the FCM algorithm is
used as a kind of intelligent mixer for the sound instruments,
generating a variety of sounds that would be unlikely or even
impossible for a human-controlled mixer to achieve.
generative Sound
The real-time data representing the shifting light patterns
along with the output of the FCM algorithm are translated
into a series of UDP messages that control the sound in-
struments and a spatialization module within the Max en-
vironment. These messages essentially function as note on/
off messages to “play” the instruments. Five sound instru-
ments have been constructed, each with its own distinct
timbre. Four of these instruments correspond to the four
cluster memberships generated by the FCM algorithm (and
will henceforth be referred to as the “cluster instruments”).
These instruments require human interaction (via breath/
CO2) to be activated. A fifth instrument is the default instru-
ment. It plays continuously, requiring no human action to
be activated.
The default sound instrument simply maps the voltage lev-
els from each P-MFC group to pitch (the higher the voltage,
the higher the pitch). In addition, any transient spikes in the
CO2 levels from any of the P-MFCs are sonified by the default
instrument (and are heard as transient spikes in the pitch).
The cluster instruments receive CO2 levels as well. However,
in this case we add up the CO2 level of each plant of each
P-MFC group and get an average of those readings. Then
we take the five most recent averages and obtain the median
value. These values are then used as interpolation parameters
for the spatialization module (discussed below).
Finally, the CO2 readings are also collected and used to
construct an envelope function that is used as a modulation
source for the cluster instruments. Each instrument uses this
modulation data differently. For example, one instrument
uses it to crossfade between different wavetables, while an-
other uses it to alter the depth factor (the amount of deviation
around a center frequency) of a modulation oscillator and to
crossfade between two control signals.
PlantConnect also features 8-speaker sound spatialization
using circular panning. By default, sounds related to readings
taken from each P-MFC group are sent to the two adjacent
speakers closest to that group. In addition, whenever a light
is triggered above a particular P-MFC in a group, the sound
instrument will be heard on the two adjacent speakers clos-
est to that group, in a manner like left-to-right panning—
the idea being that the sound instrument is heard near the
P-MFC whose lights are currently on (and thus triggering
sound). CO2 levels of each P-MFC influence the amount of
spatialization spread between all the speakers. The median
value of the five most recent averaged CO2 readings of each
P-MFC group is used to determine the amount that the trig-
gered sound instrument spreads from its “home” location
(the two adjacent speakers closest to it) to the other speakers.
When triggered, the sound spreads in both a clockwise and
counterclockwise direction from this home location.
dIScuSSIon
Observing participant interactions with PlantConnect re-
vealed what I believe to be a consistent pattern of behavior.
After the initial surprise of the triggering of lights and sound,
participants would observe closely, often by walking around
and looking up as well as down and close to the plants. Some-
times they would lean in to observe particulate elements (e.g.
sensor placement, soil). Most viewers stayed with the work
considerably longer than is typical for most artworks [19] and
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Castellanos, PlantConnect 341
did so in a manner (looking around, perhaps confused but
curious or even delighted) that suggests that they appreciated
(if only partially “getting”) what was going on with the work.
This of course is not atypical for interactive or “intelligent”
artworks. Initial curiosity related to the functioning of the
work reveals cultural anxieties about technology and a desire
to understand the presumably fixed rules related to its opera-
tion. Yet in this case the mixing of living and computational
systems seems to add a kind of heightened yet relaxed curios-
ity to the piece. Perhaps participants feel more comfortable
in simply accepting the mystery of living things and their
interactions with a technological system and are thus more
inclined to think about (and accept) organic and ecological
explanations as the drivers of complexity.
The Asian rice plant was chosen because it was read-
ily available, as it is grown plentifully in the Gwangju area
(where the piece was first exhibited) and because it has been
used by researchers in several P-MFC designs with some suc-
cess [20]. As materials for an electronic artwork, the plants,
mud, and bioelectricity made for an altogether novel experi-
ence that is qualitatively different from a more conventional
electronic art setup. Like many technological artworks, this
piece requires a fair amount of maintenance and setup time.
However, unlike most technological artworks, the “messi-
ness” and process of replenishment (e.g. adding water) and
care (especially in the days and weeks before the exhibition)
added weight to the sense that we were not just tapping into
an information source but were embedding ourselves within
already occurring organic processes. Making, exhibiting, and
observing tended to collapse into each other. This, I believe,
was born in part from what I felt were material expressions
of what I see as intimately bound up with the agency of the
plants, mud, and bacteria: their autonomy—their ability to
simply be, their tropic tendencies, and their adaptation to
environmental conditions. Almost regardless of what we do,
the plants will absorb CO2, and the bacteria will generate
even tiny amounts of electricity for some time. The challenge
is in transducing enough of these processes (e.g. capturing
enough voltage) to make a compelling experience for the au-
dience while continuing to let the plants and bacteria exercise
their agency. PlantConnect is in some ways an experiment
in accounting for these kinds of contingencies—and in fact
these possibilities are ultimately what the work is about.
concluSIon
In his influential paper “The Aesthetics of Intelligent Sys-
tems” [21], art theorist Jack Burnham offers a consideration
of art that utilizes intelligent systems as establishing a dia-
logue that can expand the horizons of the art experience by
enabling us to tap into the information-rich environment.
The crucial insight offered by Burnham is his assertion that
this emerging expansion of the art experience “encourages
the recognition of man [sic] as an integral part of his envi-
ronment” [22]. Burnham stated his belief that “the ‘aesthetics
of intelligent systems’ could be considered a dialogue where
two systems gather and exchange information so as to change
constantly the state of the other” [23]. While in PlantConnect
there are more than two systems involved, the framework of
reciprocal dialogue (and agency) is still very much at play.
More than just using digital technologies to bridge the living
world with the computational world, PlantConnect asserts
that if we are to reimagine the nature of our relationship to
the environment and the nonhuman world, we must work
from a posture of designing systems and experiences that
bring this environmental embeddedness, reciprocity, and co-
performing agencies into high relief.
PlantConnect can be seen as a contribution to this con-
versation via notions of interspecies and machine agency. In
addition to Pickering’s performative ontology, we can also
say that PlantConnect stages multiple intersecting agencies at
once—plant, bacteria, human, and machine—all operating in
different spatial and temporal scales. This “microperforma-
tivity,” or decentering of human sensory ratios in biologically
focused works [24], helps establish a kind of equity between
plant, bacteria machinic, and human, as it requires some level
of acknowledgment by humans that there are invisible (yet
vital) processes happening (e.g. bacterial voltages and meta-
bolic processes, photosynthesis) whose translation to human
scale is subtle and sometimes seemingly unchanging (e.g.
bacterial voltages may stay the same for hours). PlantConnect
can also be seen as enabling what curator Jens Hauser refers
to as “co-corporeality” between the actors involved [25], re-
defining notions of body and blurring lines between system
and environment by changing focus from “mesoscopic ac-
tions to its microscopic functions, from physical gestures to
physiological processes, and from staged diegetic time to real
performative time” [26].
Whether “speaking” to plants or “listening” to bacteria,
computational, biological, and environmental technologies
all have cultural and aesthetic dimensions that call for fur-
ther artistic and critical exploration. Indeed, the convergence
or intermingling of computational, nonhuman, and human
agencies may be a template for aesthetic experiences that
highlight this performative ontology and microperforma-
tivity, showing us the unpredictability and dynamic potency
that living organisms can exhibit while showcasing possibili-
ties for new ways of human understanding of these organ-
isms and the environment. In PlantConnect, bioelectricity,
light, sound, CO2, photosynthesis, and computational intel-
ligence form a circuit that enhances informational linkages
between human, plant, bacteria, and the physical environ-
ment, enabling a mode of interaction that is experienced not
just as a technologically enabled act of translation, but as an
embodied flow of information.
342 Castellanos, PlantConnect
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acknowledgments
PlantConnect was created by Carlos Castellanos and Bello Bello. Thank
you to the Asia Culture Center in Gwangju, South Korea, for its support.
references and notes
1 Linda Weintraub, To Life! Eco Art in Pursuit of a Sustainable Planet
(Berkeley, CA: University of California Press, 2012).
14 See www.arduino.cc.
15 See www.jmpelletier.com/cvjit/.
16 See Mobius Maxi: www.mobius-cam.com/en/mobius-maxi-27k
-c-29_23/.
17 J.C. Dunn, “A Fuzzy Relative of the ISODATA Process and Its Use in
Detecting Compact Well-Separated Clusters,” Journal of Cybernetics
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2 Rasa Smite and Raitis Smits, “Emerging Techno-Ecological Art
Practices—Towards Renewable Futures,” Acoustic Space, No. 11
(2014) pp. 129–139.
18 Benjamin D. Smith and Guy E. Garnett, “Unsupervised Play:
Machine Learning Toolkit for Max,” in New Interfaces for Musical
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3 Félix Guattari, The Three Ecologies (London: Athlone Press, 2000).
4 Rosi Braidotti, Posthuman Knowledge (Medford, MA: Polity Press,
2019).
5 Rasa Smite and Raitis Smits, Biotricity. Bacteria Battery (bacteria,
water, custom housing, custom electronics) (2012): www.rixc.org/en
/projects/0/biotricity-bacteria-battery/ (accessed 15 August 2022).
See also Smite and Smits [2] p. 138.
6 Wim van Eck and Maarten H. Lamers, “Hybrid Biological-Digital
Systems in Artistic and Entertainment Computing,” Leonardo 46,
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7 Andrew Pickering, The Cybernetic Brain: Sketches of Another Future
(Chicago: University of Chicago Press, 2010).
8 Andrew Pickering, “Being in an Environment: A Performative Per-
spective,” Natures Sciences Sociétés 21, No. 1, 77–83 (2013).
19 Sarah Cascone, “The Average Person Spends 27 Seconds Looking at
a Work of Art. Now, 166 Museums Are Joining Forces to Ask You to
Slow Down,” Artnet News (4 April 2019): news.artnet.com/art-world
/slow-art-day-2019-1508566 (accessed 15 August 2022).
20 Md Azizul Moqsud et al., “Compost in Plant Microbial Fuel Cell for
Bioelectricity Generation,” Waste Management 36 (2015) pp. 63–69.
21 Jack Burnham, “The Aesthetics of Intelligent Systems,” in On the
Future of Art (New York: Viking Press, 1970) pp. 95–122.
22 Burnham [21] p. 100.
23 Burnham [21] p. 96, emphasis in original.
24 Jens Hauser, “Microperformativity and Biomediality,” Performance
Research 25, No. 3, 12–24 (2020).
9 Bruce E. Logan, Microbial Fuel Cells (Hoboken, NJ: Wiley-
25 Hauser [24] p. 15.
Interscience, 2008).
10 Junyeong An et al., “Floating-Type Microbial Fuel Cell (FT-MFC)
for Treating Organic-Contaminated Water,” Environmental Science
& Technology 43, No. 5, 1642–1647 (2009).
11 Marjolein Helder et al., “Concurrent Bio-Electricity and Biomass
Production in Three Plant-Microbial Fuel Cells using Spartina an-
glica, Arundinella anomala and Arundo donax,” Bioresource Technol-
ogy 101, No. 10, 3541–3547 (2010).
12 See www.cycling74.com.
13 John T. Murphy, Jay M. Ham, and Clenton E. Owensby, “Design
and Testing of a Novel Gas Exchange Chamber,” Academic Research
Journal of Agricultural Science and Research 2, No. 3, 34–46 (2014).
26 Hauser [24] p. 12, emphasis in original.
Manuscript received 4 April 2022.
Carlos Castellanos is an interdisciplinary artist, re-
searcher, and assistant professor at the School of Interactive
Games and Media (IGM), Rochester Institute of Technology.
He holds a PhD from the School of Interactive Arts & Technol-
ogy (SIAT), Simon Fraser University, and an MFA from the
CADRE Laboratory for New Media, San Jose State University.
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