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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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
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Summary: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 to accurately identify and map invasive submerged aquatic vegetation (SAV) over large transects in a cost‐efficient manner has been identified by water resource managers as a pressing issue that requires an immediate solution. To help with this challenge, we have developed an artificial intelligence/machine learning (AI/ML)–based image analysis program to automatically detect a priority invasive macrophyte, hydrilla (Hydrilla verticillata), in real time. The AI/ML model, based on the existing AI model EfficientDet, was trained and tested on nearly 12,000 images of H. verticillata captured underwater using remotely operated vehicles (ROVs) and handheld cameras. Accuracy of the object detection model was evaluated based on the Microsoft Common Objects in Context (MS COCO) metric of mean average precision (mAP). Our model had a peak mAP@[0.5:0.05:0.95] of 58.2% and a mAP@[0.5] of 81.2% (with inference latencies between 50 and 100 ms). These results suggest that real‐time underwater identification of H. verticillata with our detection model is achievable at high accuracy, with further enhancement possible through integration with multiple commercially available underwater ROV platforms and continued training in environments with various combinations of invasive and native SAV assemblages.
Bibliography:Funding
This study was funded by the Aquatic Nuisance Species Research Program (ANSRP) of the US Army Engineer Research and Development Center. At the time of publication, the ANSRP Program Manager was Mr. Michael Greer and the Technical Director was Dr. Jennifer Seiter‐Moser.
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ISSN:1052-7613
1099-0755
DOI:10.1002/aqc.70054