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...

Full description

Saved in:

Bibliographic Details
Published in:	Aquatic conservation Vol. 35; no. 1
Main Authors:	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:	Journal Article
Language:	English
Published:	Oxford Wiley Subscription Services, Inc 01.01.2025
Subjects:	Accuracy Aquatic plants Artificial intelligence Autonomous underwater vehicles computer software Control programs cost effectiveness deep CNN EfficientDet freshwater Freshwater plants Hydrilla verticillata Image analysis Image processing invasive macrophyte Learning algorithms Machine learning Macrophytes Object recognition Real time Remotely operated vehicles ROV SAV submerged aquatic plants Underwater Unmanned vehicles Vegetation Water resources Water resources management
ISSN:	1052-7613, 1099-0755
Online Access:	Get full text
Tags:	Add Tag No Tags, Be the first to tag this record!

Be the first to leave a comment!