Automatic acoustic detection of the red palm weevil

  • Authors:

  • J. Pinhas;V. Soroker;A. Hetzroni;A. Mizrach;M. Teicher;J. Goldberger

  • Affiliations:

  • Department of Mathematics, The Faculty of Natural Science, Bar-Ilan University, Ramat Gan 52900, Israel and Institute of Plant Protection, Agricultural Research Organization, The Volcani Center, B ...;Institute of Plant Protection, Agricultural Research Organization, The Volcani Center, Bet Dagan 50250, Israel;Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Bet Dagan 50250, Israel;Institute of Agricultural Engineering, Agricultural Research Organization, The Volcani Center, Bet Dagan 50250, Israel;Department of Mathematics, The Faculty of Natural Science, Bar-Ilan University, Ramat Gan 52900, Israel;School of Engineering, The Faculty of Natural Science, Bar-Ilan University, Ramat Gan 52900, Israel

  • Venue:
  • Computers and Electronics in Agriculture
  • Year:
  • 2008

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Abstract

The red palm weevil (RPW) is a key pest of horticultural and ornamental palm species in Asia, the Middle East and the Mediterranean region, currently dispersing in Mediterranean European countries, endangering the landscape. The RPW larvae bore deep into palm crowns, trunks and offshoots, concealed from visual inspection until the palms are nearly dead. Traded palm trees are intensively transported between and within countries, spreading the pest worldwide. Consequently, an urgent need exists to identify and monitor concealed RPW larvae. Acoustic signals of boring RPW larvae can be recorded from the infested palms using off-the-shelf recording devices, but the resolution of the signals emitted by healthy palms is often difficult to discriminate. The purpose of this research was to develop a mathematical method to automatically detect acoustic activity of RPW in offshoots and implement it in a prototype setup. The methodology applied was similar to techniques used in the field of speech recognition, utilizing Vector quantization (VQ) or Gaussian mixture modeling (GMM). The algorithm successfully achieved detection ratios as high as 98.9%. The study shows that it is feasible to detect RPW sounds using the mathematical method of speech recognition and commercial recording devices, which could be utilized to monitor trade and transportation of offshoots.