Towards detecting red palm weevil using machine learning and fiber optic distributed acoustic sensing

Biwei Wang, Yuan Mao, Islam Ashry, Yousef Al-Fehaid, Abdulmoneim Al-Shawaf, Tien Khee Ng, Changyuan Yu, Boon S. Ooi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Red palm weevil (RPW) is a detrimental pest, which has wiped out many palm tree farms worldwide. Early detection of RPW is challenging, especially in large-scale farms. Here, we introduce the combination of machine learning and fiber optic distributed acoustic sensing (DAS) techniques as a solution for the early detection of RPW in vast farms. Within the laboratory environment, we reconstructed the conditions of a farm that includes an infested tree with ∼12 day old weevil larvae and another healthy tree. Meanwhile, some noise sources are introduced, including wind and bird sounds around the trees. After training with the experimental time-and frequency-domain data provided by the fiber optic DAS system, a fully-connected artificial neural network (ANN) and a convolutional neural network (CNN) can efficiently recognize the healthy and infested trees with high classification accuracy values (99.9% by ANN with temporal data and 99.7% by CNN with spectral data, in reasonable noise conditions). This work paves the way for deploying the high efficiency and cost-effective fiber optic DAS to monitor RPW in open-air and large-scale farms containing thousands of trees.

Original languageEnglish
Article number1592
Pages (from-to)1-14
Number of pages14
JournalSensors
Volume21
Issue number5
DOIs
Publication statusPublished - 1 Mar 2021

Keywords

  • Fiber optic acoustic sensing
  • Machine learning
  • Red palm weevil

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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