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 (V<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</inf>) and short-circuit current (I<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SC</inf>) of an EFEH assembled with natural leaves. To this end, several 10×3 cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 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 V<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</inf> and I<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SC</inf> 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 I<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SC</inf>, which presented an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of up to 0.7691 and 0.7639 to retrieve FMC and EWT, respectively.

Authors

Institutions

Publication Details

Published
2024-11-03
DOI
https://doi.org/10.1109/iecon55916.2024.10905108
Citations
1
Primary Topic
Modular Robots and Swarm Intelligence
Type
article
Field-Weighted Citation Impact
0.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Oswaldo Menéndez, Juan Villacrés, Fernando Auat Cheein
1 citations
Modular Robots and Swarm Intelligence
0.21
article

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

Oswaldo Menéndez, Juan Villacrés, Fernando Auat Cheein
article en
1 citations

Abstract

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 (V<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</inf>) and short-circuit current (I<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SC</inf>) of an EFEH assembled with natural leaves. To this end, several 10×3 cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> 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 V<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">OC</inf> and I<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SC</inf> 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 I<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">SC</inf>, which presented an R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of up to 0.7691 and 0.7639 to retrieve FMC and EWT, respectively.

Universidad Católica del Norte (CL), Heriot-Watt University (GB), University of California, Davis (US)
AC3
Affordable and clean energy
Openalex Percentile: Top 48%
Modular Robots and Swarm Intelligence
0.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.