Abstract
Electrical impedance spectroscopy has been suggested as a sensing method for plants. Here, a theoretical approach for electrical conduction via the plant stem is presented and validated, linking its living electrical characteristics to its internal structure. An electrical model for the alternating current conduction and the associated impedance in a live plant stem is presented. The model accounts for biological and geometrical attributes. It uses the electrically prevalent coupled transmission line model approach for a simplified description of the complicated vessel structure. It considers the electrode coupling to the plant stem (either Galvanic or Faradic), and accounts for the different interactions of the setup. Then the model is simplified using the lumped element approach. The model is then validated using a four-point probe impedance spectroscopy method, where the probes are galvanically coupled to the stem of Nicotiana tabacum plants. The electrical impedance data was collected continuously and the results exhibit an excellent fitting to the theoretical model, with a fitting error of less than 1.5% for data collected on various days and plants. A parametric evaluation of the fitting corresponds to the proposed physically based model, therefore providing a baseline for future plant sensor design.
Highlights
Food security for the increasing global population depends on advances in precision agriculture (FAO, 2017)
In this paper we present a model of a plant stem for electrical signal conduction, which can later be applied for a sensor system design and application
The experimental validation of the proposed model was carried out using four-point probe impedance spectroscopy setup, with electrodes galvanically coupled to the Nicotiana tabacum stem (Handbook, 2016)
Summary
Food security for the increasing global population depends on advances in precision agriculture (FAO, 2017). Among the strategies to improve crop yield, direct plant monitoring has been suggested. Such monitoring suggests incorporating new technologies, devices and data collection into common and prevalent agriculture practices (Luvisi, 2016; Walter et al, 2017; Fritz et al, 2018). This is an important factor in what is titled as “precision agriculture”. We focus on the approach to sense changes within the plant in a direct manner, as a measure of plant status and well-being
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