Moisture Detection in Tree Trunks in Semiarid Lands Using Low-Cost Non-Invasive Capacitive Sensors with Statistical Based Anomaly Detection Approach

Ashraf Ali*, Ahmad Ali, Hussein Abaluof, Wafaa Al-Sharu, Omar A. Saraereh, Andrew Ware

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

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    Abstract

    This paper focuses on building a non-invasive, low-cost sensor that can be fitted over tree trunks growing in a semiarid land environment. It also proposes a new definition that characterizes tree trunks’ water retention capabilities mathematically. The designed sensor measures the variations in capacitance across its probes. It uses amplification and filter stages to smooth the readings, requires little power, and is operational over a 100 kHz frequency. The sensor sends data via a Long Range (LoRa) transceiver through a gateway to a processing unit. Field experiments showed that the system provides accurate readings of the moisture content. As the sensors are non-invasive, they can be fitted to branches and trunks of various sizes without altering the structure of the wood tissue. Results show that the moisture content in tree trunks increases exponentially with respect to the measured capacitance and reflects the distinct differences between different tree types. Data of known healthy trees and unhealthy trees and defective sensor readings have been collected and analysed statistically to show how anomalies in sensor reading based on eigenvectors and eigenvalues of the fitted curve coefficient matrix can be detected.
    Original languageEnglish
    Article number2100
    Number of pages18
    JournalSensors
    Volume23
    Issue number4
    DOIs
    Publication statusPublished - 13 Feb 2023

    Keywords

    • sensors
    • capacitve sensor
    • wood trunks
    • oscillator
    • dielectric material
    • smart agriculture
    • LoRa
    • anomaly detection
    • eigenvectors
    • eigenvalues
    • covariance matrix

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