Exploring Plant Phenotyping through Displacement Current Energy Harvesters-Based Self-Powered Active Sensors

The phenotyping of plants is becoming more relevant to effectively managing the expectations associated with a product with certified quality, enhancing profitability, and increasing field and crop productivity. Although several solutions have been proposed to characterize plants in physical and biochemical aspects, the main contributions have been related to costly dedicated instruments. This work presents an instantaneous estimator for plant functional traits by harnessing the harvested power from an electric field energy harvester (EFEH). Specifically, we establish the detected correlation between twenty vegetation indices associated with water content and the opencircuit voltage (VOC) and short-circuit current (ISC) of an EFEH assembled with natural leaves. To this end, several 10×3 cm2 EFEHs were assembled using natural leaves sourced from two distinct species: Magnolia Obovata and Ravenala Madagascariensis. Each EFEH underwent a four-stage dehydration process. The primary outcome of this work is the exploration of VOC and ISC to retrieve fuel moisture content (FMC) and equivalent water thickness (EWT) based on machine learning models. The results indicated that the electrical parameter with the highest coefficient of determination was ISC, which presented an R2 of up to 0.7691 and 0.7639 to retrieve FMC and EWT, respectively.