Abaxial leaf surface-mounted multimodal wearable sensor for continuous plant physiology monitoring

Abstract

Wearable plant sensors hold tremendous potential for smart agriculture. We report a lower leaf surface-attached multimodal wearable sensor for continuous monitoring of plant physiology by tracking both biochemical and biophysical signals of the plant and its microenvironment. Sensors for detecting volatile organic compounds (VOCs), temperature, and humidity are integrated into a single platform. The abaxial leaf attachment position is selected on the basis of the stomata density to improve the sensor signal strength. This versatile platform enables various stress monitoring applications, ranging from tracking plant water loss to early detection of plant pathogens. A machine learning model was also developed to analyze multichannel sensor data for quantitative detection of tomato spotted wilt virus as early as 4 days after inoculation. The model also evaluates different sensor combinations for early disease detection and predicts that minimally three sensors are required including the VOC sensors.

Full text

ENGINEERING
Abaxial leaf surface-mounted multimodal wearable
sensor for continuous plant physiology monitoring
Giwon Lee
1,2,3
, Oindrila Hossain
1
, Sina Jamalzadegan
1
, Yuxuan Liu
2
, Hongyu Wang
2
,
Amanda C. Saville
4
, Tatsiana Shymanovich
4
, Rajesh Paul
1
, Dorith Rotenberg
4,5
,
Anna E. Whiteld
4,5
, Jean B. Ristaino
4,5
, Yong Zhu
2
*, Qingshan Wei
1,5
*
Wearable plant sensors hold tremendous potential for smart agriculture. We report a lower leaf surface-attached
multimodal wearable sensor for continuous monitoring of plant physiology by tracking both biochemical and
biophysical signals of the plant and its microenvironment. Sensors for detecting volatile organic compounds
(VOCs), temperature, and humidity are integrated into a single platform. The abaxial leaf attachment position is
selected on the basis of the stomata density to improve the sensor signal strength. This versatile platform
enables various stress monitoring applications, ranging from tracking plant water loss to early detection of
plant pathogens. A machine learning model was also developed to analyze multichannel sensor data for quan-
titative detection of tomato spotted wilt virus as early as 4 days after inoculation. The model also evaluates
different sensor combinations for early disease detection and predicts that minimally three sensors are required
including the VOC sensors.
Copyright © 2023 The
Authors, some
rights reserved;
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American Association
for the Advancement
of Science. No claim to
original U.S. Government
Works. Distributed
under a Creative
Commons Attribution
NonCommercial
License 4.0 (CC BY-NC).
INTRODUCTION
The United Nations announced that 2020 is the International Year
of Plant Health (IYPH), emphasizing the importance of plant health
to end hunger, reduce poverty, and protect the environment. Ac-
cording to the Food and Agriculture Organization (FAO), it is esti-
mated that by 2050, the productivity of food needs to be increased
by about 60% to feed approximately 10 billion people all over the
world (1). Plant diseases cause around 20 to 40% of global crop
loss annually (2,3). Plant diseases not only cause notable loss in
food production but also reduce species diversity, affect mitigation
of people, increase control costs, and pose negative influence on
human health and global food security (1). In this regard, sensor
technologies for early disease diagnosis are essential to shorten
stakeholder response time, identify threat before pathogen
spreads, and reduce pesticide usage by optimizing application
timing and choice of pesticides.
Tomatoes are one of the most widely consumed agriculture
products (4). Tomato plants are susceptible to many different
types of pathogens, including fungi, viruses, and bacteria, which
substantially reduce the yield and quality of fruit (5,6). In addition
to biotic stress, abiotic stresses such as high nighttime temperature
due to climate change, mechanical damage, or inappropriate use of
pesticides could also result in crucial losses of tomato yield (7). In
this study, we choose tomatoes as a model system to design and test
new sensor technologies that can monitor and predict the status of
the tomato plant health and can be applied to other plants
and crops.
Recently, several different sensor technologies have been devel-
oped for plant health monitoring, such as imaging and spectro-
scopic methods (8,9), bionanosensors (10), and smartphone-
based devices (11,12). Imaging or spectroscopic sensors are
among the few possible solutions that are capable of real-time and
noninvasive monitoring. However, the imaging techniques are in-
direct by measuring the optical signature of the plant. Such mea-
surement has some obvious limitations, such as poor sensitivity/
selectivity and complicated processing of raw images or spectral
data. Other approaches, including remote sensing (13) and electro-
physiological sensing (14), have also been proposed for continuous
monitoring. However, remote sensing, in general, lacks spatial sol-
ution and is not specific for particular diseases (13), while electro-
physiological sensors have only been demonstrated for tracking
water stress or the nycthemeral rhythm of the plant (14). Therefore,
advanced sensor technologies that can track the status of plant
health in real-time and dissect various biotic/abiotic stresses are
needed to detect pathogens early, prevent disease outbreak, and
improve plant growth and yield.
In recent years, wearable electronics have been researched
widely. Applications of wearable technologies range from health
monitoring (15,16), human-machine interface (17), to soft robotics
(18). Wearable electronics for plant health monitoring are also
emerging. These flexible sensor devices can be attached to various
parts of host plants such as roots, stems, and leaves (19–21) to
monitor the plant’s microenvironment or physiological host re-
sponses. The physiological status of a plant is related to multiple
factors. Each plant grows via a set of biological processes such as
photosynthesis, transpiration, respiration, and gas exchange
through the regulation of leaf epidermal pores called stomata (22,
23). To accurately monitor plant health status, many of those bio-
logical processes and associated environmental conditions need to
be investigated simultaneously.
Several multimodal plant wearables have been demonstrated re-
cently for continuous plant health monitoring. Nassar et al. (24)
presented a multifunctional wearable plant sensor by integrating
1
Department of Chemical and Biomolecular Engineering, North Carolina State Uni-
versity, Raleigh, NC 27695, USA.
2
Department of Mechanical and Aerospace Engi-
neering, North Carolina State University, Raleigh, NC 27695, USA.
3
Department of
Chemical Engineering, Kwangwoon University, Seoul 01897, Republic of Korea.
4
Department of Entomology and Plant Pathology, North Carolina State University,
Raleigh, NC 27695, USA.
5
Emerging Plant Disease and Global Food Security Cluster,
North Carolina State University, Raleigh, NC 27695, USA.
*Corresponding author. Email: qwei3@ncsu.edu (Q.W.); yzhu7@ncsu.edu (Y.Z.)
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temperature- and humidity-sensing functions, which was used to
understand the optimal growth conditions by sensing surrounding
environments. Zhao et al. (25) developed a stretchable multifunc-
tional sensor device for detecting both microclimates (hydration,
temperature, and light intensity) and growth rate of the plant.
Most recently, Lu et al. (19) demonstrated a multimodal flexible
sensor system for measuring microclimates (relative humidity,
light intensity, and temperature) and the perception of leaf
surface humidity. This device was able to detect changes in leaf
surface humidity by a sensor array. However, no chemical or biolog-
ical information related to the status of plant health was monitored.
On the other hand, we have recently demonstrated a plant wearable
sensor that can measure leaf volatile emissions and be used to
diagnose plant diseases noninvasively for the first time (26).
However, the previously developed sensor patch only detects
plant volatile organic compound (VOC) signals and lacks multi-
functionality. To date, a truly multifunctional and real-time
sensor device that can track both biochemical (e.g., plant VOCs)
and biophysical (e.g., temperature, humidity, etc.) signals of the
plant and/or surrounding environments with high sensitivity and
specificity has not been demonstrated yet.
In this study, we demonstrate an abaxial leaf surface-attachable
multimodal wearable plant sensor patch that can continuously and
simultaneously measure leaf VOCs, leaf surface temperature/hu-
midity, and environmental humidity, with high sensitivity and se-
lectivity. This goal was achieved with several material innovations,
Fig. 1. A multimodal wearable plant sensor. (A) Schematic illustration of the sensor attached to a plant leaf. Our multimodal sensor is attached to the abaxial leaf
surface to simultaneously monitor various physiology data from the leaf. Blue and orange arrows represent emissions of water and VOCs through stomata, respectively.
Different colors of the leaf represent the variation of leaf surface temperature. (B) Overview of the wearable sensor design, which consists of four VOC sensors, one leaf
surface relative humidity sensor, one leaf temperature sensor, and one environmental humidity sensor. All seven individual sensors were integrated with AgNW inter-
connects on a PDMS substrate. (C) Photograph of the actual sensor. VOC sensors with different sensing materials are labeled. (D) Side view of the wearable sensor patch.
(E) Photographs of an actual sensor patch attached to the lower epidermis of the tomato leaf. The environmental humidity sensor (red arrow) is the only sensor mounted
outside the leaf surface area in the air near the plant.
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including the newly designed VOC sensing materials by using a
network of three dimensional (3D) structured nanowire and nano-
tube hybrid to enable sensitive plant VOC detection in real time and
gold-coated silver nanowires (Au@AgNWs) for high stability
against humidity and solvent exposure. In addition, this study
differs from our previous work (26) in several ways: (i) This new-
generation wearable sensor is multifunctional, incorporating VOC,
temperature, and humidity sensors for measuring both biochemical
and biophysical signals of plants simultaneously. (ii) The sensor lo-
cation was optimized to the lower surface of the leaf to maximize the
sensor performance. (iii) This wearable device was tested on live
tomato plants (cv. Mountain Fresh Plus) in both laboratory and en-
vironmentally controlled plant growth chamber environments to
detect various types of stressors such as abiotic stresses (e.g., me-
chanical injury, drought, overwatering, salinity, and absence of
light) and pathogen infections [e.g., tomato spotted wilt virus
(TSWV)]. (iv) An unsurprised machine learning framework was
developed to process multichannel real-time sensor data for quan-
titative disease assessment and prediction of the best sensor combi-
nation. To the best of our knowledge, this represents the first report
of a multifunctional wearable plant sensor coupled with machine
learning data analysis.
RESULTS
Design and characterization of multifunctional plant
wearable sensor
Stomata are the primary gate of leaf tissues to exchange various
types of molecules, including water, oxygen, carbon dioxide, and
VOCs. For this reason, the plant leaf is the primary location for
sensor attachment to monitor the plant biophysical properties.
While many previous wearable sensors are attached to the upper
surface of plant leaves (adaxial surface), our sensor patch is attached
to the lower epidermis of the leaf surface (abaxial surface) to max-
imize the sensing capability for capturing biologically relevant
targets that pass the stomata (e.g., VOCs and water molecules),
which have a higher density on the lower epidermis (Fig. 1A). In
addition to leaf VOCs, our wearable patch can also monitor
several biophysical parameters such as leaf surface humidity, leaf
temperature, and environmental humidity in parallel. To achieve
this goal, the sensor patch consists of seven sensors in total, includ-
ing four resistive VOC sensors, one capacitive leaf surface humidity
sensor, one resistive leaf temperature sensor, and one capacitive en-
vironmental humidity sensor (Fig. 1, B and C). Among them, the
leaf temperature sensor is in direct contact with the leaf surface;
VOC and leaf humidity sensors face the leaf with a small gap,
while the environmental humidity sensor extends from the leaf,
open to the environment (Fig. 1, D and E). Active sensing materials
(e.g., carbon nanotubes, Au@AgNWs, Nafion, etc.) are deposited
on the as-patterned interdigitated electrodes made of AgNWs that
are embedded below the surface of a soft polydimethylsiloxane
(PDMS) substrate (fig. S1). The detailed sensor fabrication
process is described in Materials and Methods and fig. S2.
The sensing materials for VOC detection are composed of a
hybrid network of Au@AgNWs and multiwalled carbon nanotubes
(MWCNTs) embedded in a hydrophobic sol-gel layer made of
methyltrimethoxysilane (MTMS) and tetramethyl orthosilicate
(TMOS; Fig. 2A). Both AgNWs and MWCNTs have a high aspect
ratio that can maintain the percolation state under deformation of
those materials. Moreover, these materials are highly conductive,
stable, and easy to process because of their well-established chem-
istry properties. Synthesized Au@AgNWs were characterized with a
transmission electron microscope (TEM) and an energy-dispersive
spectrometry (EDS) detector, as shown in Fig. 2B. Compared to
bare AgNWs, Au@AgNWs have a thin gold layer (~30 nm) on
the surface, enabling reaction with thiolated surface ligands. More-
over, the gold coating on AgNWs can improve stability under
various chemical environments (27). The Au@AgNWs were
coated with various halothiophenol ligands [e.g., fluorothiophenol
(FTP), chlorothiophenol (CTP), bromothiophenol (BTP), and io-
dothiophenol (ITP)] to selectively detect leafy aldehydes through
reversible halogen bonding (Fig. 2A) (26). The surface modifica-
tions were characterized with ultraviolet-visible (UV-vis) spectro-
scopy to optimize the surface ligand density (fig. S3). The
Au@AgNWs created a rough 3D surface layer that greatly increased
the effective sensor surface area for VOC capturing (Fig. 2C and fig.
S6). As shown in Fig. 2C, ligand-functionalized Au@AgNWs were
further wrapped and interconnected with MWCNTs because of the
size and modulus differences between the two materials, which to-
gether formed a robust heterogeneous sensing network. On top of
these active sensing materials, a hydrophobic and nanoporous sol-
gel layer was uniformly coated by drop casting (fig. S4). The water
contact angle of the sensing film was 82° and 111° before and after
the sol-gel coating, respectively (fig. S5). We also measured scan-
ning electron microscopy (SEM) images with top and cross-section-
al views to investigate the morphology of the VOC sensing
composite materials coated with the sol-gel layer (fig. S6). The
sol-gel coating substantially increases the mechanical stability of
the VOC sensor, reduces cross-talk to humidity interference
(Fig. 2F), and also minimizes sensor baseline drift (fig. S7), which
will be discussed in more detail later.
We then tested the performance of the VOC sensors (namely,
FTP, CTP, BTP, and ITP sensors) by measuring the electrical resis-
tance under the exposure of acetone or hexanal vapors, which are
used to mimic ketone and aldehyde-based VOCs emitted from the
plant (Fig. 2, D and E, and fig. S8) (28). The VOC sensors are op-
erated as chemiresistive sensors, where the electrical resistance of
MWCNTs varies upon the attachment of VOC molecules on
their surface, which creates a doping effect on the carbon nanoma-
terials (29). When solvent vapor concentration was reduced from
500 to 100 parts per million (ppm), the sensor response (ΔR/R
0
) de-
creased accordingly, as a result of the reduced interaction between
VOC gas and sensing materials (Fig. 2, D and E). Furthermore, for
both acetone and hexanal vapors, the sensitivity of FTP and CTP
sensors is the highest among the four, followed by BTP and ITP
sensors. This is due to the decreasing electronegativity of the
halogen atoms, following the order of F > C > B > I. For the final
wearable sensor patch, only FTP and CTP VOC sensors (termed
VOC_F and VOC_C, respectively) were integrated because of
their higher detection sensitivity.
To characterize the potential cross-talk from other stimuli such
as humidity and temperature, the VOC sensors were tested under
different humidity and temperature conditions. As shown in
Fig. 2F, the presence of a hydrophobic and gas-permeable sol-gel
layer on the top of the VOC sensors can prevent the interference
of water molecules up to 90% relative humidity (Fig. 2F, black
curve), whereas the control sensor without the sol-gel coating
showed a clear response to the environmental humidity (Fig. 2F,
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Fig. 2. Characterization of VOC sensors. (A) Optical photograph of VOC sensor and schematic of the sensing materials. Au@AgNWs with surface ligands and MWCNTs
were covered with a hydrophobic sol-gel layer. Surface ligands are halothiophenols immobilized on the gold surface, which selectively interact with leaf aldehydes. (B)
TEM and EDS images to compare the morphology of AgNWs and Au@AgNWs. (C) SEM image of the Au@AgNW and MWCNT composite showing MWCNTs were wrapping
on the Au@AgNW surface. (Dand E) Electrical resistance changes of differentsurface ligand–functionalized VOC sensors (e.g., FTP, CTP, BTP,and ITP) under exposure of (D)
acetone and (E) hexanal, respectively. Vapor concentrations are theoretical concentrations at the nozzle tip of the gas-mixing system. (F) Humidity interference test of the
VOC sensor with or without the sol-gel layer. The hydrophobic sol-gel layerprevents interaction of water molecules with the VOC sensor. (G) Temperature interference test
of the VOC sensor. HAADF, high-angle annular dark-field.
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red curve). The sol-gel layer also improved the repeatability of the
VOC sensor response by enhancing the stability of the sensing ma-
terials on PDMS substrate and reducing baseline signal drifting (fig.
S7). For temperature interference, the VOC sensor showed minimal
dependence on the external temperature (Fig. 2G), owing to the op-
posite temperature coefficient of resistance values of MWCNTs
(−0.33%/K) (30) and AgNWs (0.26%/K; fig. S9) (31). Although
the resistance of VOC sensors slightly fluctuated with the temper-
ature variation (from 40° to 60°C), this change was less than 0.7%
for 40°C and lower temperatures (Fig. 2G), exhibiting acceptable
temperature stability for real plant applications.
Figure 3A shows the sensing performance of the leaf temperature
sensor, composed of as-synthesized Au@AgNWs (without func-
tionalization) as the sensing agent. Because of the temperature sen-
sitivity of Au@AgNWs and the thermal expansion of PDMS
substrate, the nanowire-based temperature sensor can detect tem-
perature changes from room temperature to 60°C. To investigate
the effect of PDMS thermal expansion on temperature sensing,
we varied the elastic modulus of PDMS by tuning the mixing
ratio of prepolymer and curing agent from 5:1 to 20:1 (Fig. 3A).
As the mixing ratio increased to 20:1 (resulting in softer PDMS),
the sensitivity of the temperature sensor slightly increased
because of larger thermal expansion of PDMS, which decreased
the percolation between Au@AgNWs (32). Moreover, the interfer-
ence of different humidity levels or solvent exposure to the temper-
ature sensor was also examined (Fig. 3B). The slope of each response
curve did not change obviously when various humidity levels (rel-
ative humidity, 25, 50, and 75%) or 500 ppm of acetone were
applied, indicating the excellent stability of the temperature
sensor. These results can be explained by the protective effect of
the gold layer on the AgNW core, which does not interact with
water or common VOC vapors because of its chemical inertness.
The performance of the humidity sensors was also characterized.
In this case, Nafion film was used as the active humidity sensing
material by measuring its capacitance changes between two elec-
trodes. The thickness of the Nafion film was controlled to optimize
the detection sensitivity (Fig. 3C). Nafion is known to absorb water
molecules, resulting in an increase in the capacitance value by re-
placing air with water molecules within the film (33). As the thick-
ness of the film was increased, the sensitivity was enhanced because
of the higher capacity of absorbing water molecules (Fig. 3C).
However, when the thickness was above 5 μm, the film was delam-
inated from the substrate because of a larger stiffness mismatch
between the film and the substrate (34). Therefore, a 2-μm-thick
Nafion layer was selected as the optimized thickness. To test the
cross interference, we applied various temperatures (10°C, room
temperature, and 40°C) or 500 ppm of acetone vapor to the humid-
ity sensor (Fig. 3D). With the increase of temperature, larger
thermal movement of water molecules would induce higher polar-
ization, resulting in a larger capacitance (35). This means that the
absolute capacitance value (C) would change as a function of the
temperature. However, the relative capacitance response (ΔC/C
0
)
is quite consistent for temperatures between 10° and 40°C. In the
case of acetone exposure, the sensitivity of the humidity sensor
was reduced slightly because of water molecule absorption in the
presence of acetone. We also compared the sensor performance
against previously published multifunctional wearable sensor
patches, which demonstrated that our sensor platform is one of
the few that can detect biochemical and physical parameters in par-
allel (table S1).
Abaxial leaf surface attachment
Next, the influence of the sensor location, namely, the adaxial
(upper surface of leaf ) attachment versus the abaxial (lower
surface of leaf) attachment, was evaluated on a live tomato plant.
In Fig. 3E, the photographs show that the sensor can be directly at-
tached to the backside of the leaf. The difference in stomata density
between the adaxial and abaxial surfaces of the leaf was observed via
bright-field optical microscopy (Fig. 3F and fig. S10). As shown in
Fig. 3G, the density of stomata on the abaxial surface is approxi-
mately 73% higher than that of the adaxial leaf. This result agrees
with the previous study, where a higher density of stomata on the
lower surface of tomato leaf was reported (36). Moreover, we com-
pared various sensor responses from adaxial and abaxial leaf surfac-
es (fig. S11). For leaf surface humidity and VOC sensors, the sensor
responses with abaxial surface position were, on average, 10 to 20%
higher than those of adaxial surface (Fig. 3H). Here, the leaf surface
humidity sensor measured the leaf-emitted water under normal
growth conditions, while the VOC sensor signal was induced by
the leafy VOC profile change upon mechanical leaf cutting. The
signal difference was attributed to the difference in stomata
density between the upper and lower surfaces of the leaf
(Fig. 3G). For better sensor performance, we therefore chose the
abaxial epidermis of the leaf as our sensor attachment location for
the rest of the experiments in this work. In addition, we tested the
wearability and biocompatibility of sensor for nearly 3 weeks on a
live tomato plant. The data showed no apparent indication of plant
stress after sensor attachment (fig. S12).
Before testing the plant stress conditions in the next section, we
first monitored the leaf surface temperature and relative humidity of
a healthy tomato plant during sunny and rainy days continuously
using the wearable sensor patch (Fig. 3I). As the light intensity in-
creased during the daytime, the leaf surface humidity signals
reached the maximum in the middle of the day because of the in-
creased number of open stomata to release the water molecules to
the environment (Fig 3I). This indicates that the intensity of the
sunlight can modulate the opening of stomata on leaf surface
(37). The leaf temperature increased under the sunlight condition
during the daytime, followed by a decrease in temperature that is
attributed to the enhanced leaf transpiration (Fig 3I and fig. S13).
However, such a circadian rhythm was much less obvious during
rainy days when the plant was less influenced by the sunlight with
high relative humidity of the air (Fig 3I). The reading error of the
wearable humidity sensor was within 10% when compared to a
commercial environmental sensor (table S2). These results show
that this multimodal sensor patch can be potentially used for the
study of plant chronobiology in addition to disease and stress detec-
tion as described below.
Monitoring abiotic stresses on tomato plants
To demonstrate the usability of the multimodal sensor, the patch
was mounted on a live tomato plant for monitoring various
abiotic stresses, including drought, overwatering, salinity, and dark-
ness (Fig. 4). Figure 4A shows the schematic illustration of the ex-
perimental setup. Four stressors were introduced sequentially to the
same tomato plant wearing the sensor patch to minimize host dif-
ference. For this experiment, we used a multimodal wearable sensor
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Fig. 3. Characterization of temperature and humidity sensors. (A) Resistance changes under increasing temperature with various mixing ratio of PDMS (error bar
represents n= 3 measurements). The inset is an optical image of the actual temperature sensor. (B) Humidity and vapor interference tests for the temperature sensor. The
temperature response was measured under relative humidity (RH) of 25, 50, and 75% or in the presence of 500 ppm of acetone vapor (error bar represents n= 3 mea-
surements). (C) Capacitance changes under increasing humidity with various thickness of Nafion film. The inset is a photo of the actual humiditysensor. (D) Temperature
and vapor interference tests for the humidity sensor. The humidity was measured at diverse temperatures [10°C, room temperature (RT), and 40°C] or in the presence of
500 ppm of acetone vapor (error bar represents n= 3 measurements). (E) Photographs of the sensor attached underneath the leaf. Front view (top row) and side views
(bottom row). Blue arrows indicate the sensor. (F) Optical microscopy image of the stomata on the abaxial tomato leaf surface. Red arrows indicate the presence of
stomata on the abaxial leaf surface. (G) Comparison of stomata density between the upper and lower surface of the leaf. Error bars are SDs from five samples. (H)
Output signal differences of leaf surface humidity and VOC sensor with different sensor attachment positions. Error bars are SDs from five samples. (I) Real-time mon-
itoring of leaf surface relative humidity and leaf surface temperature of a healthy tomato plant. D and N represent the daytime and nighttime, while gray and carnation
colors indicate rainy day and sunny day, respectively.
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composed of four VOC sensors (CTP sensors: VOC_C1 and C2;
FTP sensors: VOC_F1 and F2), one leaf surface humidity sensor,
one environmental humidity sensor, and one leaf surface tempera-
ture sensor. By applying the sequential stimuli, the responses of the
sensor with multiple electrical signals were simultaneously moni-
tored by a multichannel data recorder for up to 14 days (Fig. 4B).
First, watering was prevented for 7 days to mimic a drought envi-
ronment. In this stage, the leaf surface humidity gradually reduced
as water was conserved inside the plant over time, while the VOC
signals increased slightly (Fig. 4B, first and second panel). Although
the plant was grown indoors, we were able to capture a rainy day
(day 3) by observing the depression of leaf transpiration that was
modulated by the different lighting conditions, similar to the
results in Fig. 3I. After 7 days, the plant was watered normally to
reduce the water stress, and the plant recovered for another day
before the next experiments. On day 9, excessive water was
applied to create an overwatering condition. Immediately, the
surface humidity of the leaf increased about 1.5 times as water
content inside the host decreased as increased water evaporation oc-
curred through the leaf surface (Fig. 4B, second panel). On day 10,
salinity stress was introduced by using 150 mM salted water. Typi-
cally, under high-salt conditions, the transpiration of the plant
would be reduced because of the reduction of water absorption
from roots as a result of the osmotic pressure difference between
soil and root (38). This was confirmed from our experimental
data, where the suppression of leaf surface humidity change and
rapid increase of leaf temperature due to reduced transpiration
was observed (Fig. 4B, second and fourth panel). For the last exper-
iment (day 12 and after), the plant was placed in complete darkness
by covering the plant with a box. In this case, notably increased
VOC emission, leaf surface humidity, and leaf temperature were ob-
served, which probably resulted from the induced stress due to the
lack of photosynthesis. The plant eventually died at the end of the
experiment while showing high levels of VOC emissions and in-
creased leaf temperature. The elevated leaf surface humidity after
light blocking appears contradictory to the conventional plant
physiology theory. However, we attribute this phenomenon to the
plausible evapotranspiration effect of a dying plant. To validate our
results, we also repeated the abiotic stress experiments with single
stressors (one stress at a time), and the plant responses were record-
ed by both wearable sensors (figs. S14 to 17) and a commercial leaf
porometer (figs. S18 and S19). Both devices showed good agree-
ment with each other. Briefly, the shortage and excessive watering
moderately decreased or increased the stomatal conductance and
leaf surface humidity, respectively (fig. S19 and table S3). Watering
with high salt concentration inhibited the transpiration because of
the imbalance of the osmotic pressure in roots (fig. S19 and table
S3). Last, blocking the light hindered the photosynthesis process
and therefore resulted in the reduction of leaf surface humidity
and temperature (fig. S19 and table S3). Each stress was measured
three times, and their sensor response trend was summarized in
table S3.
In addition, we also monitored the plant response to mechanical
damage on leaves by the wearable sensor patch (fig. S20). When
cutting or detaching the leaf, the VOC sensors detected more
obvious and immediate response signals than other sensors (e.g.,
temperature and humiditysensors; fig. S20), indicating that the bio-
chemical sensor was more sensitive than biophysical sensors in the
detection of acute plant tissue damages.
Monitoring biotic stress and early pathogen detection
Next, we tested the wearable sensor patch for monitoring different
pathogens on live tomato plants. TSWV (a viral pathogen) and early
blight (Alternaria linariae, a fungal pathogen) were selected because
of their prevalence in tomato production (fig. S21) (1,39,40). As
shown in fig. S22, the experiments were conducted in a growth
chamber at the North Carolina State University (NCSU) Phytotron,
maintained at a constant 23°C with a 16-hour photoperiod and
constant carbon dioxide concentration (400 ppm). TSWV inocula-
tion was first performed, and the plant response was monitored by
the wearable sensor. By measuring the electrical signals with the
wearable sensor, differentiable VOC signals can be seen after
Fig. 4. Plant wearable for monitoring abiotic stresses. (A) Schematic illustration
of the experimental setup for sequential abiotic stress monitoring, including
drought, overwatering, salinity, and no light. (B) Real-time sensor data from the
same tomato plant exposed to various abiotic stresses. VOC_C1 and C2 correspond
to CTP sensors, while VOC_F1 and F2 are FTP sensors. Black arrows represent
specific times for applying different conditions. Carnation color indicates the
daytime of measurement. Gray color represents the rainy day.
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around 5 days post inoculation (dpi; inoculated on day 4 and detect-
able VOC signals on day 9; n= 3 replicated measurements; Fig. 5A
and fig. S23). To compare our electrical sensing method with the
conventional molecular diagnostic method, a TSWV-specific real
time reverse-transcription loop-mediated isothermal amplification
(RT-LAMP) assay was performed in parallel (Fig. 5B and Materials
and Methods). According to the real-time RT-LAMP assay results,
successful inoculation of the plant generated consistent positive
nucleic acid test results 7 dpi (Fig. 5B), which is later than the wear-
able sensor patch. More quantitative early detection data were de-
termined by the machine learning analysis described below. In
addition, a rapid change of VOC signals and a decrease in leaf
Fig. 5. In-situ measurement of tomato plant health under biotic stresses (pathogen infections) in phytotron. (A) Real-time wearable sensor data of a live tomato
plant after TSWV inoculation. VOC_C1 and C2 correspond to CTP sensors, while VOC_F1 and F2 are FTP sensors. Black arrows represent specific time for conducting the
inoculation. Carnation color indicates the daytime of measurement. (B) Real-time LAMP assay results, verifying the presence of TSWV pathogens after different days of
inoculation. After 7 dpi, a positive result was detected by the RT-LAMP assay for three of four plants. (C) Wearable VOC sensor data and the conventional Horsfall-Barratt
scale (black line) for the inoculation experiment with A. linariae. Heatmap of sensor data after 5 hours of different abiotic stresses(D) and heatmap of sensor data on days 3
and 6 of the tomato plant with various pathogens (E). ∆R/R
0
,∆LSH, and ∆LT represent the resistance change due to leaf VOC emission, leaf surface humidity variation
(LSH), and leaf temperature (LT) change, respectively. a.u., arbitrary units.
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surface relative humidity was captured right after the inoculation
(days 4 and 5). These signal perturbations were attributed to the me-
chanical damage to the leaf surface during the TSWV rub-inocula-
tion process. The VOC signals returned to the baseline on day 7,
indicating the recovery of the plant from mechanical damage. In ad-
dition to VOCs, leaf surface relative humidity rapidly decreased and
the leaf temperature slightly increased after inoculation [Fig. 5A
(second and fourth panel) and fig. S23], which can be related to
the mechanical damage of the leaf surface and also the closure of
the stomata after pathogen infection (41). When a plant is infected
by TSWV, stomata generally close as a result of invasion by the
pathogen, resulting in a lower transpiration rate (42). Although
less specific, these biophysical signals (e.g., leaf surface relative hu-
midity and temperature) are easy to monitor compared to VOC
signals and hence could be useful plant health indicators on
their own.
Monitoring host plant response using the patch sensors also
allowed us to determine whether inoculations were successful.
Failed inoculations resulted in response patterns that varied
widely from successful inoculations that developed into visible
symptoms (fig. S24). In the case of mock inoculation, the VOC
signals did not increase after 5 dpi (fig. S24). The rapid increase
of VOC signals immediately after the inoculation was still observed,
confirming that this instant VOC response is indeed induced by
mechanical damage to the leaf. Furthermore, during the entire
period, both leaf surface relative humidity and temperature re-
mained mostly constant, which also indicated that the plant was
not infected by TSWV. The unsuccessful inoculation was lastly con-
firmed by the RT-LAMP assay (fig. S25).
To demonstrate the feasibility of detecting fungal pathogen in-
fection using the same sensor patch, we also performed inoculation
experiments with A. linariae (n= 3 replicated measurements;
Fig. 5C figs. S26 and S27). Because the inoculation of this fungal
pathogen was performed by spraying the pathogen solution on
the leaf surface without mechanical damage, no instant VOC
signal changes were observed after inoculation in this case
(Fig. 5C and fig. S27). For A. linariae, our wearable sensor demon-
strated the capability of detecting pathogen infection before the
visual assessment method. Conventionally, the visual symptoms
were quantified using the Horsfall-Barratt scale for assessing
disease severity (table S4) (43). On the basis of the scale, the infec-
tion could be confirmed visually 4 dpi for A. linariae (Fig. 5C, black
squares). However, the wearable VOC sensor was able to capture
elevated VOC emissions after 2 days of infection, approximately 2
days before the visible assessment method. Leaf surface humidity
and temperature signals also showed different patterns before and
Fig. 6. Machine learning analysis of the real-time TSWV sensor data. (A) Schematic diagrams of the process of machine learning using PCA method. (B) A repre-
sentative graph showing 3D PCA analysis from days 0 to 15 for the six-sensor combination (VOC_C1, C2, F1, F2, H, and T). VOC_C1, C2, F1, F2, H, and T denote four VOC
sensors, leaf surface relativehumidity sensor, and leaf temperature sensor, respectively. (C) Average discriminability values with different numbers of sensors as a function
of infection days. Error bars represent the SDs of each number of sensors. (D) Discriminability with the best sensor composition for each number of sensors. VOC_C1, C2,
F1, F2, H, and T denote four different types of VOC sensors, leaf surface relative humidity sensor, and leaf temperature sensor, respectively.
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after infection (figs. S26 and S27). Together, these results (Fig. 5, A
and C) suggested that the wearable sensor technology was able to
detect different kinds of plant pathogens (viral and fungal) 2 to 3
days earlier than conventional detection methods, such as the
nucleic acid testing by LAMP assays (Fig. 5B) and the visible inspec-
tion in Fig. 5C. Our previous work also demonstrated that the wear-
able sensor could detect infection by the oomycete pathogen
Phytophthora infestans (26). As a comparison, healthy tomatoes
sprayed with water instead of the spore solution showed no
sensor response at all (fig. S28).
Moreover, by combining all the sensor signals, the wearable
sensor patch was capable of distinguishing biotic stress from
abiotic factors such as mechanical cutting, drought, overwatering,
salinity, and light deficiency. To demonstrate that, heatmaps were
generated using the sensor data at specific time points for differen-
tiating all abiotic and biotic stresses that have been screened. As
shown in Fig. 5D, the VOC, leaf surface relative humidity, and tem-
perature data were combined and depicted with different color
scales. Distinct electrical responses of the plant wearable sensor
under different abiotic stresses can be seen after a constant exposure
time period of 5 hours. Moreover, similar to the case of abiotic
stress, all three tested pathogens could be discriminated from each
other and also from the healthy plants by using the same sensor
patch through the combination of the sensor signals (VOC, leaf
temperature, and humidity) at specific time points (e.g., days 3
and 6; Fig. 5E). Such a distinct sensor pattern could be recognized
and classified by machine learning methods in the future for rapid
differentiation.
Machine learning for quantitative early detection and
prediction of best sensor combination
To quantitatively assess our multimodal sensors for the early detec-
tion of pathogens, an unsupervised machine learning approach
based on principal components analysis (PCA) was used to
analyze the real-time sensor data (Fig. 6) (44,45). PCA is one of
the most well-known statistical algorithms in data analysis and
image processing projects for multivariate variable dimensionality
reduction with impactful benefits such as feature selection and
event classification. In comparison with other conventional
methods such as t-distributed stochastic neighbor embedding (t-
SNE), the PCA approach is more applicable in multivariable
sensor systems (46). Figure 6A depicts the schematic illustrations
of the PCA-based data analysis pipeline for processing real-time
sensor data. For the demonstration, we used the TSWV inoculation
data (Fig. 5A) as an example. The multichannel wearable sensor
data from the same plant was first divided into different days
(e.g., days 0, 1, 2, 3, etc.). Day 0 data were used as the healthy
control and compared to other days. Data from different days
were clustered by PCA with reduced data dimensions. Then, the
centroid and Euclidean distance between two centroids of clusters
(two different days) were calculated (see details in Materials and
Methods). The separation of the clusters was quantitatively assessed
by a parameter called “discriminability”(D), as defined by the fol-
lowing equation
D¼E ðRSTD;1þRSTD;2Þ
where D,E, and Rdenote discriminability, Euclidean distance, and
radius (or SD) of the cluster, respectively.
As shown in Fig. 6B, a three-component PCA for a total of six
sensors [namely, VOC_C1, VOC_C2, VOC_F1, VOC_F2, leaf
surface relative humidity sensor (H), and temperature sensor (T)]
from days 0 to 15 shows the gradual separation of sensor signals
from day 0 (green dots) throughout the TSWV infection process.
In the early days, the most obvious cluster separation occurred on
day 5 (cyan dots) because of the mechanical damage induced by the
inoculating method (Fig. 6B). All PCA data with other sensor com-
binations (e.g., five sensors, four sensors, three sensors, etc.) are also
shown in fig. S29.
Using discriminability (capturing different signal changes), we
are able to quantitatively differentiate diseased plants from
healthy controls and determine the accurate early detection day.
Simply, if the discriminability value is positive, the two sensor clus-
ters are considered distinguishable, resulting in a positive diagnosis
result. On the other side, if the value is negative, that means the two
clusters under comparison are overlapped, resulting in a negative
detection result. We applied the PCA to all possible sensor combi-
nations using six individual sensors (total 63 combinations) and
calculated discriminability for each sensor combination (Fig. 6C
and fig. S30). Figure 6C shows the averaged discriminability value
for each sensor combination, and the SD represents the different
performance when the number of sensors is fixed but the sensor
composition is different. On the basis of the data, it clearly suggests
that the more sensor channels that were used, the higher the dis-
criminability value, which also means higher confidence in a posi-
tive detection. Excluding the day 5 data (positive but mainly because
of mechanical perturbation), the six-sensor channel combination
can clearly detect TSWV infection as early as day 8, which is 4
dpi, much earlier than the RT-LAMP results (7 dpi; Fig. 5B).
Figure 6C displayed a large error bar for each fixed number of
sensors, suggesting that the actual sensor channel combination is
equally if not more important to the total number of sensors. The
best combination for each number of sensors is presented in
Fig. 6D. According to the discriminability values, a minimum of
three sensors (VOC_C2, VOC_F1, and H) is needed for the early
detection of TSWV after 4 dpi (Fig. 6D, blue triangle). In addition,
the results suggest that for effective disease detection, the biochem-
ical VOC sensor is probably the most important sensor that is
needed in each sensor combination; in addition, the leaf surface hu-
midity sensor works slightly more effectively than the leaf temper-
ature sensor in disease detection (Fig. 6D). Such a machine learning
analysis can help find the most impactful sensor (and sensor com-
bination) for a particular application and potentially reduce the
total number of redundant sensors, which would be particularly
useful to reduce sensor cost while maintaining sensor performance.
DISCUSSION
Multifunctional and miniaturized sensor technology for continu-
ous plant physiology monitoring is of great interest for early
disease detection, stress sensing, and growth prediction. However,
many existing wearable sensor technologies can only detect physical
growth of the plant or environmental parameters of the atmosphere
(table S1). None of these sensor platforms has been developed to
monitor multiple signals that can inform plant health. Here, a mul-
timodal plant wearable sensor patch capable of detecting both bio-
chemical and biophysical parameters of individual plants, namely,
leafy VOCs, leaf surface humidity, leaf surface temperature, and
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environmental relative humidity, was demonstrated for continuous,
on-plant physiology monitoring. This versatile device used a newly
designed 3D nanohybrid sensing network to capture leaf VOC
signals and greatly minimized the cross-talkbetween VOC, temper-
ature, and humidity signals. The performance of each sensor com-
ponent integrated on the patch (e.g., detection sensitivity and range)
matches well with what has been demonstrated in the existing mul-
tiplexed sensor platforms and has room to approach those mono-
functional sensors (table S1). Moreover, our sensor was mounted
underneath the leaf surface (abaxial surface) to maximize the
output signals from the plant, which is different from many previ-
ous sensor applications. With this multimodal sensor patch, we
demonstrated the monitoring of various types of plant stresses
from drought, overwatering, salinity, light deficiency, mechanical
damage, and pathogenic infection (virus and fungus) in both labo-
ratory and greenhouse conditions. In particular, the wearable sensor
patch demonstrated capability for the early detection of plant path-
ogens (2 to 3 days earlier) when compared to conventional nucleic
acid LAMP-based reactions or visual assessment techniques.
In addition, a machine learning analysis framework based on the
PCA approach was developed to quantitatively determine the early
detection capability and screen the best combination among multi-
ple sensors. In recent years, applying machine learning approaches
such as supervised and unsupervised learning have been greatly in-
creased in the biosensor area because of their outstanding benefits
in data analysis and noise reduction. Specifically, integrating
machine learning with biosensors for plant disease detection,
stress phenotyping, and predictive analysis showed meaningful
results (47). In this research, we conducted PCA as one of the
most common unsupervised machine learning algorithms to
reduce the dimensions of multichannel sensor data and also classify
the roles of each sensor in combination to find out the best combi-
nation candidates to predict plant disease sooner. This data analyt-
ics–coupled sensor platform could be used for various applications
related to plant health monitoring and crop loss prevention in ag-
ricultural settings.
For practical field applications, the size of sensor patches should
be further miniaturized using higher-resolution fabrication tech-
niques such as photolithography, micromolding in capillaries
(even on curved surfaces) (48), or direct laser writing (49). The ge-
ometry of sensor patch also needs to be flexible to fit diverse shapes
of attaching leaves. In multimodal sensors, cross-sensitivity inhibits
the precise measurement of a specific target when multiple stimuli
are present at the same time. Therefore, it is critical to implement
decoupled sensing mechanisms such as different sensing materials,
sensor layouts, and signaling principles in future sensor design and
development (50). Moreover, a fully standalone sensor device will
be expected in the future, which requires the integration of other
essential components such as thin-film batteries, self-powered
units (51), and functional circuits (52) for wireless signal transmis-
sion with the sensing elements on the same patch. The robustness of
the sensor patch will also need to be thoroughly tested in the green-
house and field trails.
MATERIALS AND METHODS
Materials and reagents
All materials and reagents were used without further purification.
MWCNTs, Nafion, hexanal, and acetone were purchased from
Sigma-Aldrich. PDMS (SYLGARD 184) was purchased from
Dow Corning.
Synthesis of nanowires
The Au@AgNWs were synthesized by a modified chemical solvent
method based on the previously reported procedure (27). The
AgNWs are prepared by a modified polyol method and dispersed
in deionized (DI) water for the following steps (53).
For the preparation of solution A, 10 ml of AgNW aqueous sol-
ution (10 mg/ml), 70 ml of 5 weight % polyvinylpyrrolidone
aqueous solution (M
w
, 40,000; Sigma-Aldrich), 14 ml of 0.5 M
L-ascorbic acid aqueous solution (Sigma-Aldrich), 14 ml of 0.5 M
sodium hydroxide (Sigma-Aldrich), 3.5 ml of 0.1 M Na
2
SO
3
(Sigma-Aldrich) aqueous solution, and 80 ml of DI water were uni-
formly mixed with a glass rod. The prepared solution was denoted
as solution A.
For the preparation of solution B, 10 ml of 0.1 M sodium sulfite
aqueous solution, 3.5 ml of 0.5 M sodium hydroxide aqueous sol-
ution, and 100 ml of DI water were mixed first. Then, 1.5 ml of 0.25
M hydrogen tetrachloroaurate(iii) hydrate aqueous solution
(HAuCl
4
·xH
2
O; Sigma-Aldrich) was added to the mixed solution.
The solution was then stirred gently with a glass rod. The prepared
solution is denoted as solution B.
Solution B was then immediately but slowly poured into solution
A to produce a mixture that appeared light purple with a metallic
gloss. Then, the beaker of the mixed solution was sealed and left for
12 hours. After the reaction, the Au@AgNWs turned light brown
and precipitated out of the solution. The remaining solution
turned clear black. These nanowires were then collected and
washed with 95% ethanol three times using centrifugation (1000
rpm for 1 min) and dispersed in ethanol for further use.
Preparation of VOC sensors
The VOC sensor’s performance depends heavily on the amount of
ligands attached to the surface of the nanomaterials. To optimize
the concentration and the amount of ligands decorated to the
surface of Au@AgNWs, four different concentrations (0.01, 0.1, 1,
and 10 μM) of FTP ligands were tested. We found a higher tendency
of agglomeration of the nanowire solutions at the higher ligand con-
centration. However, if the amount of surface ligand is too low, then
the reactivity toward VOC analytes will also be reduced. Therefore,
0.1 μM ligand solutions were eventually chosen to functionalize
Au@AgNWs without notable aggregation. After that, we optimized
the amount of ligand solutions by changing the volume of ligand
solutions (e.g., 300, 500, and 1000 μl). The 500-μl solution was se-
lected to balance the ligand density and nanomaterial stability. Sim-
ilarly, 500 μl of 100 nM ITP, BTP, and CTP ligand solutions were
used to prepare other VOC sensors. Every functionalization reac-
tion was continued for 8 hours with slight shaking at room temper-
ature. The supernatant was then collected to measure their UV-vis
spectrum. After that, ligand-attached Au@AgNW solutions were
added to MWCNTs at a 5:1 mixing weight ratio.
The sol-gel film was prepared by mixing MTMS, TMOS, meth-
anol, and Nanopure water in the molar ratio of 1:1:11:5. The solu-
tion was stirred at room temperature for 2 hours. The final
formulation was diluted 10 times by adding methanol before
drop-casting.
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Fabrication of a multimodal wearable plant sensor
As shown in fig. S2, AgNWs were spray-coated on the polyimide
(PI) substrate using a stencil mask for patterning interdigitated elec-
trodes and interconnect. After patterning, PDMS solution was
poured to transfer AgNWs from PI to PDMS. The PI substrate
was removed when the PDMS was fully cured. With a patterned
AgNW substrate, sensing materials for each sensor (e.g.,
Au@AgNWs for temperature sensor, Nafion for leaf surface humid-
ity sensor and environmental humidity sensor, and functionalized
Au@AgNW+MWCNTs for VOC sensors) were selectively depos-
ited onto the interdigitated electrodes.
Characterization of the developed sensor
The electrical resistance changes were measured by a digital mul-
timeter (DAQ970A, Keysight) and recorded by the software Bench-
Vue 2018. The capacitance variation was captured by the Capacitive
to Digital Converter Evaluation Module (FDC1004EVM, Texas In-
struments). The temperature was controlled by a hot plate. Humid-
ity was produced with a wet nitrogen gas stream. VOC gases were
generated by bubbling nitrogen gas through the corresponding
organic solvents. The concentration of VOC vapors was modulated
by MKS mass flow controllers. For VOC sensor measurement, the
sensor was exposed to VOC vapors at a fixed concentration for 2
min, followed by pure nitrogen gas purging for another 2 min for
baseline recovery. Morphologies of sensing materials were mea-
sured by SEM (Thermo Fisher Scientific Quanta 3D FEG), TEM
(Thermo Fisher Scientific Talos F200X), and EDS (Thermo Fisher
Scientific Super-X EDS with the four silicon drift detectors).
Sensors were attached onto the leaf surface using double-sided
tape (2477p, 3M) and connected to the data acquisition system
with thin copper wires (FIXFANS) and silver adhesive epoxy
(MG Chemicals) for interconnection. A commercial sensor device
(Amprobe, THWD-5) was used for measuring relative humidity
and temperature of the environment. A leaf porometer (Decagon
Devices Inc., SC-1 leaf porometer) was used to measure the stomatal
conductance, leaf surface humidity, and leaf surface temperature of
the tomato plants under various abiotic stress conditions to validate
the sensor measurement. For the reproducibility of porometer mea-
surement, two healthy tomato plants (5 to 6 weeks old) for each
stress condition were used, and for each plant, three different leaflets
were measured to minimize signal variation. In total, six measure-
ments (from six different leaflets) were performed for each stressor,
and 12 plants were used for the experiments. For each measure-
ment, the porometer data were collected every 30, 60, or 120 min,
and the measurements were continued for 2 to 4 days for different
stressors.
Preparation of host tomato plants
Susceptible tomato plants (cv. Mountain Fresh Plus) were grown
from seed at the NCSU Phytotron with a combination of natural
light and artificial light (14-hour photoperiod) and a 26°C day
and 22°C night temperature cycle. Approximately 1 week before
the experiment, 5- to 6-week-old plants were transferred to a
growth chamber with a 16-hour photoperiod kept at 23°C (both
day and night) for the entire experimental period.
One plant was selected for each inoculation. The plant was then
placed in an inverted inoculation box lined with moist paper towels
to provide water and humidity throughout the experiment.
A. linariae inoculation
An isolate of A. linariae (JD1B) was maintained on potato dextrose
agar throughout the experiment. To generate conidia, pieces of agar
with active A. linariae culture were broken up in potato dextrose
broth and spread onto sporulation agar (0.2 g of CaCO
3
, 100 ml
of V8 juice, 20 g of Difco Bacto agar, and 1 liter of dH
2
O). Plates
were incubated at 20°C under constant light for 2 weeks. Conidia
production was then stimulated by brushing the plates with a dry
sterile cell spreader and incubating at room temperature for 1 to 2
days with the lids ajar in an inoculation box. To harvest conidia, 2
ml of sterile distilled water was added to the plate and brushed with
a cell spreader. The liquid was then removed and quantified using a
hemocytometer. The conidia solution was diluted to 2000 conidia/
ml using distilled water. Four plants were sprayed with 2 ml of the
conidia solution over the entire surface of the plant, while four
control plants were sprayed with 2 ml of distilled water. Plants
were covered with clear plastic bags to maintain humidity. In addi-
tion to monitoring by the sensor, visual symptoms were observed
daily for 1 week by measuring the percent leaf area diseased
(%LAD) using a modified Horsfall-Barratt scale (table S4).
TSWV inoculation
A week before the experiment, a 2-week-old tomato Mountain
Fresh seedling, susceptible to TSWV, was placed in a growth
chamber at 23°C with a 16-hour light/8-hour dark schedule. The
plant in a pot was also enclosed in a plastic container with paper
towels soaked in a nutrient solution. As the experiment started,
the seedling was inoculated with a wild-type TSWV strain that orig-
inated from California using a mechanical leaf-rub method. First, all
leaves were sprinkled with carborundum (39). Second, several
young leaves from TSWV-infected tomatoes were ground in an
ice-cold mortar with approximately 5 to 10 ml of sodium sulfite sol-
ution (63 mg per 50 ml of tap water) as a buffer. Next, two cotton
applicators were repeatedly dipped into the ground tissue mix and
rubbed on each leaf using a gloved hand to support the leaf and
ensure small wounds were made on the leaf surface. The leaf area
with the sensor attached was avoided. Ten minutes after this proce-
dure, the tomato plant was sprayed with DI water to remove the re-
maining carborundum. The plant was kept enclosed in a controlled
chamber for 14 days. Plants were fertilized with nutrient solution
three times per week. For the negative control, mock inoculation
was performed with healthy leaf tissue, and the experiment schedule
was kept the same.
TSWV LAMP assay
At the experiment termination, one young leaf was used for DNA/
RNA extraction. Polymeric microneedle patch was pressed on a leaf
and rinsed with 60 μl of DI water (12). To detect TSWV, 25-μl
LAMP reactions with EvaGreen fluorescent and hydroxy naphthol
blue colorimetric dye were performed on a Bio-Rad CFX96 real-
time machine (12). For each reaction, 2 μl of microneedle extraction
was used for analysis. For positive controls, we used 2 μl of RNA
extracted with Total RNA (Plant) Kit (IBI Scientific) from sympto-
matic tomato plants maintained in the laboratory. For no template
controls, 2 μl of molecular grade DI water was added to the reac-
tions. Positive reactions were detected by green fluorescence with
C
t
values recorded, as well as by a color change from violet to
light blue.
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Principal components analysis
We performed PCA, data analysis, and plots in the Project Jupyter
platform using Python programming language. Then, we found the
centroid of each cluster by using centroid function in the k-means
clustering approach to calculate the Euclidean distance. In a three-
component PCA space, Euclidean distance Dwas defined as
Dðp;qÞ ¼ ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
ðp1q1Þ2þ ðp2q2Þ2þ ðp3q3Þ2
q
where pand qare known as the centroids of cluster 1 and cluster 2,
respectively. The discriminability value (D) is calculated by sub-
tracting the summation of the SDs (R
STD,1
+R
STD,2
) of a pair of clus-
ters from the corresponding Euclidean distance (E)
D¼E ðRSTD;1þRSTD;2Þ
Positive discriminability values indicate the cluster separation
without overlaps. For four to six sensors, we applied three-compo-
nent PCA. For three and two sensor combinations, we applied two-
component and one-component PCA approaches, respectively. In
addition, we calculated the capture variance of each component
of PCA and showed them on each axis.
Supplementary Materials
This PDF le includes:
Figs. S1 to S30
Tables S1 to S4
References
View/request a protocol for this paper from Bio-protocol.
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Acknowledgments: We also thank A. Locke for providing the leaf porometer device. Funding:
We gratefully acknowledge the funding support from the NCSU Game-Changing Research
Incentive Program for the Plant Science Initiative (GRIP4PSI), USDA (no. 2019-67030-29311),
USDA APHIS Farm Bill grant (no. 3.0096), and NSF (nos. 1728370 and 2134664). Author
contributions: G.L., Y.Z., and Q.W. designed and initiated the project. Y.L and O.H. synthesized
and characterized AgNW, Au@AgNW, and functionalized Au@AgNW. G.L. fabricated the
multimodal wearable sensor and performed on-plant measurements. H.W. characterized the
morphology of sensing materials. A.C.S., T.S., J.B.R., and A.E.W. grew the tomato plants and
performed the inoculation of different pathogens and LAMP tests. S.J. conducted PCA of the
real-time sensor data. R.P.helped with the plant pathogen test in the laboratory. D.R. provided
valuable discussion on the experimental design and results analysis. G.L., Y.Z., and Q.W. wrote
the manuscript. All the authors read and revised the manuscript. Competing interests: The
authors declare that they have no competing interests. Data and materials availability: All
data needed to evaluate the conclusions in the paper are present in the paper and/or the
Supplementary Materials.
Submitted 1 August 2022
Accepted 16 March 2023
Published 12 April 2023
10.1126/sciadv.ade2232
Lee et al.,Sci. Adv. 9, eade2232 (2023) 12 April 2023 14 of 14
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Abaxial leaf surface-mounted multimodal wearable sensor for continuous plant
physiology monitoring
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Sci. Adv., 9 (15), eade2232.
DOI: 10.1126/sciadv.ade2232
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