Effectiveness of IoT and Deep Learning for Detection and Severity Assessment of Postelectrotermes militaris in Tea Plantations
Organizations: Sri Lanka Institute of Information Technology, New Kandy Road, Malabe, Sri Lanka · University of Kelaniya, Kandy Road, Dalugama, Kelaniya, Sri Lanka · Tea Research Institute of Sri Lanka, Talawakelle, Sri Lanka · Murdoch University, 90 South St, Murdoch, Perth, Western Australia, Australia
Abstract
Tea plantations are vulnerable to Postelectrotermes militaris, commonly known as the Upcountry Live Wood Termite (ULWT), which can cause substantial damage when infestations remain undetected. This study proposes an IoT-enabled acoustic monitoring framework integrated with deep learning for early detection and severity assessment of ULWT infestations in tea plantations. Research Method: Audio signals were captured non-invasively from tea trunks using a high-sensitivity microphone connected to a Raspberry Pi-based IoT device, with geographic coordinates recorded for spatial tracking. After trimming, resampling, and segmentation, 2,000 ten-second samples were obtained, comprising 1,000 healthy and 1,000 infested samples, and divided into 1,600 training, 200 validation, and 200 test samples. The dataset used in this study is publicly available on Kaggle (Senevirathna et al. 2026). Fourier-derived spectrograms trained a CNN for infestation classification and probability estimation. A weighted severity model combined CNN probability, mean acoustic amplitude, and nearby infested plants within 5 m, with geospatial mapping used to visualize infestation distribution. Findings and Values: Field trials in a ULWT-affected tea plantation in Pundaluoya demonstrated feasibility under realistic environmental noise. On the held-out test set, the CNN achieved 81.5% accuracy, 80.6% precision, 83.0% recall, 81.8% F1-score, and 0.819 ROC-AUC. Beyond binary infestation detection, the framework introduced quantitative severity assessment using infestation probability, acoustic amplitude, and nearby infested plants. The resulting severity and geospatial outputs can support plantation managers in identifying high-risk areas, prioritizing field inspections, and implementing more timely and targeted control measures.
Figures & tables
| Ref. | Crop | Detection task | Method | Performance |
| Kadethankar et al. (2021) | Coconut | Pest detection | CNN, Faster R-CNN | 84.64% |
| Kong et al. (2022) | Multi-crop | Pest detection | Deep learning, Fe-Net | 85.29% |
| Hewawitharana et al. (2023) | Tea | Disease detection | CNN, YOLOv8, Mask R-CNN | 98% recall |
| Wanninayake et al. (2023) | Rice | Disease & pest detection | YOLOv5, CNN, ML | 89–99% |
| Chen et al. (2024) | Tea | Pest detection | MAM-IncNet | 81.44% recall |
| He et al. (2024) | Tea | Pest detection | YOLOv7, SSD, R-CNN | +2.58% mAP |
| Ref. | Target crop/material | Detection task | Method | Reported accuracy |
| Nanda et al. (2019) | Wood | Subterranean termite detection | LSTM-based deep learning | 97.16% |
| Escola et al. (2020) | Coffee | Cicadid pest detection | SVM | 96.91% |
| Ali et al. (2023) | Agricultural crops | Classification of 10 pest species | CNN–BiLSTM | 98.91% |
| Ekramirad et al. (2023) | Apples | Codling moth detection | AdaBoost | 94.00% |
| Torky et al. (2023) | Agricultural crops | Red palm weevil detection | VGGish, SVM, DT, NB, LR, and DL models | 94.50% |
| Shetty and Kumar (2025) | Agricultural crops | Cicada, beetle, termite, and cricket classification | XGBoost, Random Forest, KNN | 97.00% |
| Condition | Location | Class | Type | Total | Train | Val | Test |
| Infested & Healthy | Pundaluoya | Healthy | Collected | 200 | 160 | 20 | 20 |
| Segmented | 800 | 640 | 80 | 80 | |||
| Infested | Collected | 200 | 160 | 20 | 20 | ||
| Segmented | 800 | 640 | 80 | 80 |
Passive Acoustic Monitoring
Passive acoustic monitoring uses unattended microphones and hydrophones to detect, classify, and track wildlife and other sounds over time.
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