Effectiveness of IoT and Deep Learning for Detection and Severity Assessment of Postelectrotermes militaris in Tea Plantations
Read the original on arXiv Machine Learning →The study presents an IoT-enabled acoustic monitoring system that uses a Raspberry Pi and high‑sensitivity microphone to capture audio from tea trunks, creating a dataset of 2,000 ten‑second samples (1,000 healthy, 1,000 infested). A CNN trained on Fourier‑derived spectrograms achieved 81.5% accuracy in classifying Postelectrotermes militaris infestations, and a weighted severity model combined CNN probability, acoustic amplitude, and proximity of infested plants to generate quantitative severity scores and geospatial maps. Field trials in Pundaluoya confirmed the system’s feasibility amid realistic environmental noise, offering plantation managers tools to identify high‑risk areas and prioritize inspections.
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