The present work reports research efforts toward development and evaluation of a unified framework for automatic bioacoustic recognition of specific insect pests. Our approach is based on capturing and automatically recognizing the acoustic emission resulting from typical behaviors, e.g., locomotion and feeding, of the target pests. After acquisition the signals are amplified, filtered, parameterized, and classified by advanced machine learning methods on a portable computer. Specifically, we investigate an advanced signal parameterization scheme that relies on variable size signal segmentation. The feature vector computed for each segment of the signal is composed of the dominant harmonic, which carry information about the periodicity of the signal, and the cepstral coefficients, which carry information about the relative distribution of energy among the different spectral sub-bands. This parameterization offers a reliable representation of both the acoustic emissions of the pests of interest and the interferences from the environment. We illustrate the practical significance of our methodology on two specific cases: 1) a devastating pest for palm plantations, namely, Rhynchophorus ferrugineus Olivier and 2) a pest that attacks warehouse stored rice (Oryza sativa L.), the rice weevil, Sitophilus oryzae (L.) (both Coleoptera: Curculionidae, Dryophorinae). These pests are known in many countries around the world and contribute for significant economical loss. The proposed approach led to detection results in real field trials, reaching 99.1% on real-field recordings of R. ferrugineus and 100% for S. oryzae.
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Vol. 102 • No. 4