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AI-powered system detects termites in tea plantations

Researchers have developed an IoT-enabled system using deep learning to detect and assess the severity of Postelectrotermes militaris, also known as the Upcountry Live Wood Termite (ULWT), in tea plantations. The framework captures audio signals from tea trunks via a Raspberry Pi-based IoT device and uses a Convolutional Neural Network (CNN) trained on spectrograms to classify infestations. Field trials in Pundaluoya demonstrated the system's feasibility in noisy environments, achieving 81.5% accuracy in binary detection and providing quantitative severity assessments to aid plantation managers in targeted control measures. AI

IMPACT This research demonstrates a novel application of AI for agricultural pest detection, potentially improving crop yields and reducing pesticide use.

RANK_REASON The cluster describes a research paper detailing a novel application of IoT and deep learning for pest detection. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

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AI-powered system detects termites in tea plantations

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The cluster describes a research paper detailing a novel application of IoT and deep learning for pest detection. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · D. K. C. Senevirathna, A. A. E. Nanayakkara, H. M. C. K. Kulathunga, J. K. D. P. Nadula, R. M. Mapatuna, Malithi Nawarathne, Jaliya L. Wijayaraja, P. D. Senanayake, Samitha Vidhanaarachchi, Kalpani Manathunga ·

    Effectiveness of IoT and Deep Learning for Detection and Severity Assessment of Postelectrotermes militaris in Tea Plantations

    arXiv:2608.27480v1 Announce Type: cross 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 ac…