Early Detection of Furniture-Infesting Wood-Boring Beetles Using CNN-LSTM Networks and MFCC-Based Acoustic Features

TL;DR

This paper presents a non invasive acoustic detection framework using CNN-LSTM networks and MFCC features for early identification of termite infestations in wooden structures, improving pest monitoring and damage prevention.

Contribution

It introduces a hybrid CNN-LSTM model that effectively captures spatial and temporal features of termite sounds, achieving high accuracy in early detection.

Findings

94.5% classification accuracy

Outperforms standalone CNN and LSTM models

Low false-negative rate for timely intervention

Abstract

Structural pests, such as termites, pose a serious threat to wooden buildings, resulting in significant economic losses due to their hidden and progressive damage. Traditional detection methods, such as visual inspections and chemical treatments, are invasive, labor intensive, and ineffective for early stage infestations. To bridge this gap, this study proposes a non invasive deep learning based acoustic classification framework for early termite detection. We aim to develop a robust, scalable model that distinguishes termite generated acoustic signals from background noise. We introduce a hybrid Convolutional Neural Network Long Short Term Memory architecture that captures both spatial and temporal features of termite activity. Audio data were collected from termite infested and clean wooden samples. We extracted Mel Frequency Cepstral Coefficients and trained the CNN LSTM model to classify the signals. Experimental results show high performance, with 94.5% accuracy, 93.2% precision, and 95.8% recall. Comparative analysis reveals that the hybrid model outperforms standalone CNN and LSTM architectures, underscoring its combined strength. Notably, the model yields low false-negative rates, which is essential for enabling timely intervention. This research contributes a non invasive, automated solution for early termite detection, with practical implications for improved pest monitoring, minimized structural damage, and better decision making by homeowners and pest control professionals. Future work may integrate IoT for real time alerts and extend detection to other structural pests.