@inproceedings{20180a81e21047009dd10d17fed58e95,
title = "Poster: Networked Multimodal Sensor Framework for Shrimp Health and Behavior Analysis",
abstract = "This study presents a multimodal sensing and deep learning framework to enhance monitoring shrimp health and behavior. By integrating real-time water quality sensors, acoustic monitoring, and computer vision, we track key parameters closely tied to feed consumption in healthy and diseased conditions in shrimp. Over a 5-week growth period, we analyzed shrimp feeding behavior through acoustic signals, and performed a controlled disease challenge to identify unique patterns associated with healthy and diseased condition in shrimp. The resulting data can help offer actionable insights for farm operators to develop cost effective feeding strategies and reduce cost associated with aquafeed use for both commercial applications and small individual farm owners.",
keywords = "Aquaculture, Environmental Monitoring, Multimodal Sensing",
author = "Ng, \{Calvin Alexander\} and Sage Lyon and Sheree Pagsuyoin and Schofield, \{Paul J.\} and Dhar, \{Arun K.\} and Yan Luo",
note = "Publisher Copyright: {\textcopyright} 2025 Copyright held by the owner/author(s).; 31st Annual International Conference on Mobile Computing and Networking, ACM MobiCom 2025 ; Conference date: 04-11-2025 Through 08-11-2025",
year = "2025",
month = nov,
day = "21",
doi = "10.1145/3680207.3765681",
language = "English (US)",
series = "ACM MobiCom 2025 - Proceedings of the 2025 the 31st Annual International Conference on Mobile Computing and Networking",
publisher = "Association for Computing Machinery, Inc",
pages = "1344--1346",
booktitle = "ACM MobiCom 2025 - Proceedings of the 2025 the 31st Annual International Conference on Mobile Computing and Networking",
}