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Machine learning-enabled acoustic sensing for RPW infestation detection.

S Saranya1, Betty Martin2

  • 1Department of ECE, SASTRA Deemed to be University, Thanjavur, 613401, Tamilnadu, India.

Scientific Reports
|November 3, 2025
PubMed
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Early detection of Red Palm Weevil (RPW) is crucial for palm health. This study introduces a novel auditory system using signal processing and deep learning, achieving 98.02% accuracy in identifying RPW infestations.

Area of Science:

  • Agricultural Entomology
  • Machine Learning Applications
  • Acoustic Signal Processing

Background:

  • Red Palm Weevil (RPW) infestation poses a significant threat to palm cultivation, often leading to severe damage before detection.
  • Current detection methods are frequently invasive or lack early-stage sensitivity, impacting effective pest management strategies.

Purpose of the Study:

  • To develop a non-invasive auditory detection system for the early identification of Red Palm Weevil (RPW) infestations.
  • To leverage advanced signal processing and deep learning techniques for enhanced RPW detection accuracy and reliability.

Main Methods:

  • Acoustic signals from palm trees were pre-processed to minimize noise.
  • Linear Predictive Coding (LPC) extracted spectral features indicative of RPW activity.
Keywords:
Bidirectional long short term (Bi-LSTM)Cosine similarityLinear predictive coding (LPC)MATLABRed palm weevil (RPW)

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  • Cosine similarity refined feature matching, followed by Bidirectional Long Short-Term Memory (Bi-LSTM) network classification.

    • Main Results:

    • The proposed system achieved a high accuracy of 98.02% in detecting RPW.
    • The integrated approach demonstrated superior performance compared to traditional detection methods.
    • The system proved robust against noise, interference, and signal distortion.

    Conclusions:

    • The novel integration of LPC features, cosine similarity, and Bi-LSTM enables highly reliable, non-invasive early detection of RPW.
    • This auditory detection system offers a promising advancement for sustainable palm production and pest management.
    • Further large-scale field validation is recommended to confirm wider applicability.