Identifying Hydrilla verticillata in Real Time With a Machine Learning–Based Underwater Object Detection Program

ABSTRACT Standard tools for detection and identification of invasive macrophytes have limitations that may result in failure to detect patches of invasive vegetation. These undetected growths can spread rapidly, leading to significant disruption of invasive macrophyte control programs. The ability t...

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Veröffentlicht in:	Aquatic conservation 2025-01, Vol.35 (1), p.n/a
Hauptverfasser:	Jeong, Han S., Schad, Aaron N., Cheng, Jing‐Ru C., Donohue, Griffin, Hawkins, Jazmine L., Steen, Andrew M., Farthing, William F., Knight, Ian A., Dodd, Lynde L., Katzenmeyer, Alan W., Sistrunk, Virginia A., Hammond, Shea L., Bellinger, Brent J., Rycroft, Taylor E.
Format:	Artikel
Sprache:	eng
Schlagworte:	Accuracy Aquatic plants Artificial intelligence Autonomous underwater vehicles Control programs deep CNN EfficientDet Freshwater plants Hydrilla verticillata Image analysis Image processing invasive macrophyte Learning algorithms Machine learning Macrophytes Object recognition Real time Remotely operated vehicles ROV SAV Underwater Unmanned vehicles Vegetation Water resources Water resources management
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