Biocompatible Wearable Electrodes on Leaves toward the On-Site Monitoring of Water Loss from Plants

Abstract

Impedimetric wearable sensors are a promising strategy for determining the loss of water content (LWC) from leaves because they can afford on-site and nondestructive quantification of cellular water from a single measurement. Because the water content is a key marker of leaf health, monitoring of the LWC can lend key insights into daily practice in precision agriculture, toxicity studies, and the development of agricultural inputs. Ongoing challenges with this monitoring are the on-leaf adhesion, compatibility, scalability, and reproducibility of the electrodes, especially when subjected to long-term measurements. This paper introduces a set of sensing material, technological, and data processing solutions that overwhelm such obstacles. Mass-production-suitable electrodes consisting of stand-alone Ni films obtained by well-established microfabrication methods or ecofriendly pyrolyzed paper enabled reproducible determination of the LWC from soy leaves with optimized sensibilities of 27.0 (Ni) and 17.5 kΩ %–1 (paper). The freestanding design of the Ni electrodes was further key to delivering high on-leaf adhesion and long-term compatibility. Their impedances remained unchanged under the action of wind at velocities of up to 2.00 m s–1, whereas X-ray nanoprobe fluorescence assays allowed us to confirm the Ni sensor compatibility by the monitoring of the soy leaf health in an electrode-exposed area. Both electrodes operated through direct transfer of the conductive materials on hairy soy leaves using an ordinary adhesive tape. We used a hand-held and low-power potentiostat with wireless connection to a smartphone to determine the LWC over 24 h. Impressively, a machine-learning model was able to convert the sensing responses into a simple mathematical equation that gauged the impairments on the water content at two temperatures (30 and 20 °C) with reduced root-mean-square errors (0.1% up to 0.3%). These data suggest broad applicability of the platform by enabling direct determination of the LWC from leaves even at variable temperatures. Overall, our findings may help to pave the way for translating “sense–act” technologies into practice toward the on-site and remote investigation of plant drought stress. These platforms can provide key information for aiding efficient data-driven management and guiding decision-making steps.

A leaf-mounted capacitance sensor for continuous monitoring of foliar transpiration and solar irradiance as an indicator of plant water status

“…At present, research is focusing on the development of lightweight, plant-wearable sensors capable of monitoring physiological and environmental parameters. Sensors presented so far aim at measuring various parameters at the leaf surface, such as humidity (Lan et al, 2020), vapour pressure deficit (Yin et al, 2021) or loss of leaf water content (Barbosa et al, 2022) . This present sensor aims instead specifically at detecting vapour condensation as an indicator of foliar transpiration, simultaneously monitoring solar irradiance as its main driving force.…”

Recent progress and future perspectives of polydopamine nanofilms toward functional electrochemical sensors

“…The development of electrochemical devices for the agricultural field has recently shown its importance for monitoring the health of plants and leaves [ 82 ]. Salicylic acid (SA), for example, plays an important role in plants, and changes in its concentration may indicate levels of environmental adaptability and the pathological state of the plant.…”

Machine learning toward high-performance electrochemical sensors

“…The scale bar in (4) means 5.0 mm. Adapted from [44], Copyright 2022 with permission from ACS diluted to insignificant contents [37]. Despite the existence of a plethora of efficient strategies to enhance sensitivity such as the use of nanomaterials toward current amplification [38][39][40][41], the adoption of bare electrodes is desirable by supporting scalable, simple, and low-cost sensing methods [42].…”

“…Significant ML-assisted improvement in the S/N ratio was recently described by our group [44]. We proposed an impedimetric wearable sensor for determining the loss of water contents (LWC) from soy leaves at different temperatures along 24 h, namely, 12 h at 30 °C and then 12 h at 20 °C.…”