@inproceedings{e4a417932f2e4e0d8bee882172e486ab,
title = "Invasive insect pest monitoring using low-cost, field deployable, machine-learning-assisted sensor systems",
abstract = "The Asian citrus psyllid (ACP), Diaphorina citri Kuwayama (Hemiptera: Liviidae), is a citrus pest that vectors the bacterium that causes huanglongbing (HLB) disease between citrus trees. It has become a very large problem to the US citrus growers. Male ACP find females by vibrating the substrate (branch) to call them. The females vibrate a response and the males track these responses to find them in a citrus tree. We have created three ACP call recognition systems–one using Matlab, one using TensorFlow implemented on a Raspberry Pi, and one using Edge Impulse implemented on a RP2040 microcontroller. All three systems recognized calls with an accuracy greater than 79.5%. A demonstration on a single, long recording of two ACP vibrating to each other using the RP2040 system shows that it would be useful in a live implementation.",
author = "McNeill, {Seth A.} and Aviad Golan and Heeirthan Shanthan and Mankin, {Richard W.} and Yabin Liao",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Bioinspiration, Biomimetics, and Bioreplication XIV 2024 ; Conference date: 25-03-2024 Through 26-03-2024",
year = "2024",
doi = "10.1117/12.3010873",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Martin-Palma, {Raul J.} and Mato Knez and Akhlesh Lakhtakia",
booktitle = "Bioinspiration, Biomimetics, and Bioreplication XIV",
address = "United States",
}