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...
Uložené v:
| Vydané v: | Aquatic conservation Ročník 35; číslo 1 |
|---|---|
| Hlavní autori: | , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Oxford
Wiley Subscription Services, Inc
01.01.2025
|
| Predmet: | |
| ISSN: | 1052-7613, 1099-0755 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | 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. |
|---|---|
| AbstractList | 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. 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. |
| Author | Knight, Ian A. Steen, Andrew M. Schad, Aaron N. Cheng, Jing‐Ru C. Dodd, Lynde L. Donohue, Griffin Hammond, Shea L. Hawkins, Jazmine L. Farthing, William F. Katzenmeyer, Alan W. Bellinger, Brent J. Rycroft, Taylor E. Jeong, Han S. Sistrunk, Virginia A. |
| Author_xml | – sequence: 1 givenname: Han S. surname: Jeong fullname: Jeong, Han S. organization: U.S. Army Engineer Research and Development Center – sequence: 2 givenname: Aaron N. surname: Schad fullname: Schad, Aaron N. organization: U.S. Army Engineer Research and Development Center – sequence: 3 givenname: Jing‐Ru C. surname: Cheng fullname: Cheng, Jing‐Ru C. organization: U.S. Army Engineer Research and Development Center – sequence: 4 givenname: Griffin surname: Donohue fullname: Donohue, Griffin organization: Oak Ridge Institute for Science and Education – sequence: 5 givenname: Jazmine L. surname: Hawkins fullname: Hawkins, Jazmine L. organization: Oak Ridge Institute for Science and Education – sequence: 6 givenname: Andrew M. surname: Steen fullname: Steen, Andrew M. organization: U.S. Army Engineer Research and Development Center – sequence: 7 givenname: William F. surname: Farthing fullname: Farthing, William F. organization: U.S. Army Engineer Research and Development Center – sequence: 8 givenname: Ian A. surname: Knight fullname: Knight, Ian A. organization: U.S. Army Engineer Research and Development Center – sequence: 9 givenname: Lynde L. surname: Dodd fullname: Dodd, Lynde L. organization: U.S. Army Engineer Research and Development Center – sequence: 10 givenname: Alan W. surname: Katzenmeyer fullname: Katzenmeyer, Alan W. organization: U.S. Army Engineer Research and Development Center – sequence: 11 givenname: Virginia A. surname: Sistrunk fullname: Sistrunk, Virginia A. organization: U.S. Army Engineer Research and Development Center – sequence: 12 givenname: Shea L. surname: Hammond fullname: Hammond, Shea L. organization: U.S. Army Engineer Research and Development Center – sequence: 13 givenname: Brent J. surname: Bellinger fullname: Bellinger, Brent J. organization: City of Austin – sequence: 14 givenname: Taylor E. orcidid: 0000-0003-0427-3201 surname: Rycroft fullname: Rycroft, Taylor E. email: taylor.e.rycroft@usace.army.mil organization: U.S. Army Engineer Research and Development Center |
| BookMark | eNp1kM1OwzAQhC0EEm3hwBtY4gKHlLXjpPGxlF-piB-BOFqOs6GuUqe1U6reeAfekCchpZyQOM0cvhntTpfsutohIUcM-gyAn-mF6Q8AErFDOgykjGCQJLsbn_BokLJ4n3RDmAKATFnaIf62QNfYcm3dG71ZF95Wlabv6BtrNrbR1Dr6hLqiz3aG9NU2E6rpnTYT65COUXvXRr8-Ps91wIK-uAL9Sjfo6X0-RdPQC2xasbWjD75-83p2QPZKXQU8_NUeebm6fB7dROP769vRcBwZLkFEAnOhE5lBVgw4sEymukwEz3kmWYwsZ5iKHPOMp9wUhUAt81KKMuOxYbwoWdwjJ9veua8XSwyNmtlgsH3KYb0MKuYAMfCUZS16_Aed1kvv2utUzFImBE8S0VKnW8r4OgSPpZp7O9N-rRiozfqqXV_9rN-yZ1t2ZStc_w-q4eNom_gGeV-Iig |
| Cites_doi | 10.1109/CVPR.2016.91 10.1016/j.ecoinf.2023.102305 10.1002/nafm.10386 10.1016/j.array.2021.100057 10.1007/s11263-009-0275-4 10.1007/s11042-022-12502-1 10.3390/rs13193841 10.3390/rs14184487 10.3390/su12093544 10.3390/rs15020539 10.3390/rs12203431 10.3354/ab00450 10.3390/s22239536 10.1186/s40537-023-00701-y 10.1007/s10530-020-02434-y 10.1080/21642583.2021.1990159 10.1017/S1068280500010157 10.1672/0277-5212(2007)27[1144:MSMVUA]2.0.CO;2 10.1007/978-3-642-35749-7_1 10.1109/LRA.2023.3245405 10.1109/CVPR.2014.81 |
| ContentType | Journal Article |
| Copyright | 2025 John Wiley & Sons Ltd. |
| Copyright_xml | – notice: 2025 John Wiley & Sons Ltd. |
| DBID | AAYXX CITATION 7QH 7QL 7SN 7SS 7T7 7TN 7U9 7UA 8FD C1K F1W FR3 H94 H95 H99 L.F L.G M7N P64 7S9 L.6 |
| DOI | 10.1002/aqc.70054 |
| DatabaseName | CrossRef Aqualine Bacteriology Abstracts (Microbiology B) Ecology Abstracts Entomology Abstracts (Full archive) Industrial and Applied Microbiology Abstracts (Microbiology A) Oceanic Abstracts Virology and AIDS Abstracts Water Resources Abstracts Technology Research Database Environmental Sciences and Pollution Management ASFA: Aquatic Sciences and Fisheries Abstracts Engineering Research Database AIDS and Cancer Research Abstracts Aquatic Science & Fisheries Abstracts (ASFA) 1: Biological Sciences & Living Resources ASFA: Marine Biotechnology Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Marine Biotechnology Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Professional Algology Mycology and Protozoology Abstracts (Microbiology C) Biotechnology and BioEngineering Abstracts AGRICOLA AGRICOLA - Academic |
| DatabaseTitle | CrossRef Aquatic Science & Fisheries Abstracts (ASFA) Professional Virology and AIDS Abstracts Technology Research Database Ecology Abstracts Aqualine Water Resources Abstracts Biotechnology and BioEngineering Abstracts Environmental Sciences and Pollution Management Entomology Abstracts Oceanic Abstracts Bacteriology Abstracts (Microbiology B) Algology Mycology and Protozoology Abstracts (Microbiology C) ASFA: Aquatic Sciences and Fisheries Abstracts Aquatic Science & Fisheries Abstracts (ASFA) Marine Biotechnology Abstracts AIDS and Cancer Research Abstracts Engineering Research Database Aquatic Science & Fisheries Abstracts (ASFA) 1: Biological Sciences & Living Resources Industrial and Applied Microbiology Abstracts (Microbiology A) AGRICOLA AGRICOLA - Academic |
| DatabaseTitleList | Aquatic Science & Fisheries Abstracts (ASFA) Professional CrossRef AGRICOLA |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Biology Ecology Oceanography |
| EISSN | 1099-0755 |
| EndPage | n/a |
| ExternalDocumentID | 10_1002_aqc_70054 AQC70054 |
| Genre | article |
| GrantInformation_xml | – fundername: Aquatic Nuisance Species Research Program |
| GroupedDBID | .3N .GA .Y3 05W 0R~ 10A 1L6 1OB 1OC 1ZS 23M 31~ 33P 3SF 3WU 4.4 50Y 50Z 51W 51X 52M 52N 52O 52P 52S 52T 52U 52W 52X 5GY 5VS 66C 6J9 702 7PT 8-0 8-1 8-3 8-4 8-5 8UM 930 A03 AAESR AAEVG AAHBH AAHHS AAHQN AAMNL AANHP AANLZ AAONW AASGY AAXRX AAYCA AAZKR ABCQN ABCUV ABEML ABIJN ACAHQ ACBWZ ACCFJ ACCZN ACGFO ACGFS ACPOU ACRPL ACSCC ACXBN ACXQS ACYXJ ADBBV ADEOM ADIZJ ADKYN ADMGS ADNMO ADOZA ADZMN ADZOD AEEZP AEGXH AEIGN AEIMD AEQDE AEUQT AEUYR AFBPY AFFPM AFGKR AFPWT AFWVQ AFZJQ AHBTC AIAGR AITYG AIURR AIWBW AJBDE AJXKR ALAGY ALMA_UNASSIGNED_HOLDINGS ALUQN ALVPJ AMBMR AMYDB ASPBG ATUGU AUFTA AVWKF AZBYB AZFZN AZVAB BAFTC BDRZF BFHJK BHBCM BMNLL BMXJE BNHUX BROTX BRXPI BY8 CS3 D-E D-F DCZOG DDYGU DPXWK DR2 DRFUL DRSTM EBS ECGQY EJD F00 F01 F04 FEDTE G-S G.N GNP GODZA H.T H.X HF~ HGLYW HVGLF HZ~ IX1 J0M JPC KQQ LATKE LAW LC2 LC3 LEEKS LH4 LITHE LOXES LP6 LP7 LUTES LW6 LYRES M62 MEWTI MK4 MRFUL MRSTM MSFUL MSSTM MXFUL MXSTM N04 N05 N9A NF~ NNB O66 O9- OIG P2P P2W P2X P4D PALCI Q.N Q11 QB0 QRW R.K RIWAO RJQFR ROL RWI RX1 RYL SAMSI SUPJJ UB1 W8V W99 WBKPD WH7 WIB WIH WIK WNSPC WOHZO WQJ WRC WWD WXSBR WYISQ XG1 XV2 Y6R ZZTAW ~02 ~IA ~KM ~WT AAYXX AEYWJ AGHNM AGQPQ AGYGG AIQQE CITATION O8X 7QH 7QL 7SN 7SS 7T7 7TN 7U9 7UA 8FD C1K F1W FR3 H94 H95 H99 L.F L.G M7N P64 7S9 L.6 |
| ID | FETCH-LOGICAL-c2904-4eb4a59808d7201896af542b28913e1b1e64beb8262cdd4ea9bf94f823c12df13 |
| IEDL.DBID | DRFUL |
| ISICitedReferencesCount | 0 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001406990100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1052-7613 |
| IngestDate | Fri Jul 11 17:25:36 EDT 2025 Sun Nov 09 05:44:55 EST 2025 Sat Nov 29 07:40:30 EST 2025 Fri Jan 31 10:08:31 EST 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c2904-4eb4a59808d7201896af542b28913e1b1e64beb8262cdd4ea9bf94f823c12df13 |
| Notes | 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. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| ORCID | 0000-0003-0427-3201 |
| PQID | 3161442554 |
| PQPubID | 2029164 |
| PageCount | 8 |
| ParticipantIDs | proquest_miscellaneous_3200302618 proquest_journals_3161442554 crossref_primary_10_1002_aqc_70054 wiley_primary_10_1002_aqc_70054_AQC70054 |
| PublicationCentury | 2000 |
| PublicationDate | January 2025 2025-01-00 20250101 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – month: 01 year: 2025 text: January 2025 |
| PublicationDecade | 2020 |
| PublicationPlace | Oxford |
| PublicationPlace_xml | – name: Oxford |
| PublicationTitle | Aquatic conservation |
| PublicationYear | 2025 |
| Publisher | Wiley Subscription Services, Inc |
| Publisher_xml | – name: Wiley Subscription Services, Inc |
| References | 2023; 10 2021; 23 2017; 2017 2012 2006; 35 2020; 40 2023; 15 2023; 8 2020; 1911 2020; 12 2012; 16 2022; 22 2024 2010; 88 2021; 13 2021; 10 2022; 81 2022 2017; 55 2024; 9 2022; 14 2017 2015 2014 2022; 10 2023; 102305 2007; 27 e_1_2_9_30_1 e_1_2_9_31_1 e_1_2_9_11_1 e_1_2_9_10_1 e_1_2_9_13_1 e_1_2_9_12_1 Madesen J. D. (e_1_2_9_21_1) 2017; 55 Al‐Qizwini M. (e_1_2_9_2_1) 2017; 2017 e_1_2_9_15_1 e_1_2_9_14_1 e_1_2_9_17_1 e_1_2_9_16_1 e_1_2_9_19_1 e_1_2_9_18_1 e_1_2_9_20_1 e_1_2_9_22_1 e_1_2_9_24_1 e_1_2_9_23_1 e_1_2_9_8_1 e_1_2_9_6_1 e_1_2_9_5_1 e_1_2_9_4_1 e_1_2_9_3_1 e_1_2_9_9_1 e_1_2_9_26_1 e_1_2_9_25_1 e_1_2_9_28_1 Tan M. (e_1_2_9_27_1) 2020; 1911 e_1_2_9_29_1 Dai L. (e_1_2_9_7_1) 2024 |
| References_xml | – volume: 23 start-page: 1321 year: 2021 end-page: 1327 article-title: Detection of Anolis carolinensis Using Drone Images and a Deep Neural Network: An Effective Tool for Controlling Invasive Species publication-title: Biological Invasions – volume: 81 start-page: 20871 year: 2022 end-page: 20916 article-title: Underwater Object Detection: Architectures and Algorithms – A Comprehensive Review publication-title: Multimedia Tools and Applications – volume: 40 start-page: 145 year: 2020 end-page: 153 article-title: Using Recreation‐Grade Side‐Scan Sonar to Produce Classified Maps of Submerged Aquatic Vegetation publication-title: North American Journal of Fisheries Management – volume: 102305 year: 2023 article-title: Adoption of Unmanned Aerial Vehicle (UAV) Imagery in Agricultural Management: A Systematic Literature Review publication-title: Ecological Informatics – start-page: 1 year: 2012 end-page: 14 – volume: 10 year: 2021 article-title: Deep Learning for Object Detection and Scene Perception in Self‐Driving Cars: Survey, Challenges, and Open Issues publication-title: Array – year: 2024 – volume: 9 start-page: 1078 year: 2024 end-page: 1091 – volume: 88 start-page: 303 issue: 2 year: 2010 end-page: 338 article-title: The PASCAL Visual Object Classes (VOC) Challenge publication-title: International Journal of Computer Vision – volume: 14 start-page: 4487 issue: 18 year: 2022 article-title: Underwater Object Detection Based on Improved Efficientdet publication-title: Remote Sensing – volume: 8 start-page: 2134 issue: 4 year: 2023 end-page: 2141 article-title: Towards More Efficient Efficientdets and Real‐Time Marine Debris Detection publication-title: IEEE Robotics and Automation Letters – volume: 27 start-page: 1144 issue: 4 year: 2007 end-page: 1152 article-title: Mapping Salt Marsh Vegetation Using Aerial Hyperspectral Imagery and Linear Unmixing in Humboldt Bay, California publication-title: Wetlands – year: 2014 – volume: 55 start-page: 1 year: 2017 end-page: 22 article-title: A Review of Aquatic Plant Monitoring and Assessment Methods publication-title: Journal of Aquatic Plant Management – volume: 10 start-page: 61 issue: 1 year: 2023 article-title: Exploration of Issues, Challenges and Latest Developments in Autonomous Cars publication-title: Journal of Big Data – volume: 12 start-page: 3431 issue: 20 year: 2020 article-title: Detection of Invasive Species in Wetlands: Practical DL With Heavily Imbalanced Data publication-title: Remote Sensing – volume: 10 start-page: 264 issue: 1 year: 2022 end-page: 271 article-title: Ship Target Detection of Unmanned Surface Vehicle Base on Efficientdet publication-title: Systems Science & Control Engineering – volume: 16 start-page: 197 issue: 2 year: 2012 end-page: 202 article-title: Colonization, Regeneration Potential and Growth Rates of Fragments of the Exotic Aquatic Macrophyte Hydrilla verticillata publication-title: Aquatic Biology – volume: 35 start-page: 195 issue: 1 year: 2006 end-page: 208 article-title: The Economic Impacts of Aquatic Invasive Species: A Review of the Literature publication-title: Agricultural and Resource Economics Review – year: 2022 – volume: 12 start-page: 3544 issue: 9 year: 2020 article-title: Employing Machine Learning for Detection of Invasive Species Using Sentinel‐2 and Aviris Data: The Case of Kudzu in the United States publication-title: Sustainability – volume: 2017 start-page: 89 year: 2017 end-page: 96 article-title: Deep Learning Algorithm for Autonomous Driving Using GoogLeNet publication-title: IEEE Intelligent Vehicles Symposium (IV) – volume: 15 start-page: 539 issue: 2 year: 2023 article-title: Deep Object Detection of Crop Weeds: Performance of YOLOv7 on a Real Case Dataset From UAV Images publication-title: Remote Sensing – start-page: 580 year: 2014 end-page: 587 – volume: 13 start-page: 1 issue: 19 year: 2021 end-page: 23 article-title: Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review publication-title: Remote Sensing – year: 2017 – volume: 22 start-page: 9536 issue: 23 year: 2022 article-title: Sea Mine Detection Framework Using YOLO, SSD and EfficientDet Deep Learning Models publication-title: Sensors – year: 2015 – volume: 1911 start-page: 09070 year: 2020 article-title: EfficientDet: Scalable and Efficient Object Detection publication-title: CVPR. arXiv – ident: e_1_2_9_25_1 doi: 10.1109/CVPR.2016.91 – ident: e_1_2_9_13_1 doi: 10.1016/j.ecoinf.2023.102305 – ident: e_1_2_9_6_1 – volume: 1911 start-page: 09070 year: 2020 ident: e_1_2_9_27_1 article-title: EfficientDet: Scalable and Efficient Object Detection publication-title: CVPR. arXiv – ident: e_1_2_9_4_1 doi: 10.1002/nafm.10386 – ident: e_1_2_9_12_1 doi: 10.1016/j.array.2021.100057 – ident: e_1_2_9_8_1 doi: 10.1007/s11263-009-0275-4 – ident: e_1_2_9_9_1 doi: 10.1007/s11042-022-12502-1 – ident: e_1_2_9_23_1 doi: 10.3390/rs13193841 – ident: e_1_2_9_15_1 doi: 10.3390/rs14184487 – ident: e_1_2_9_19_1 – ident: e_1_2_9_14_1 doi: 10.3390/su12093544 – ident: e_1_2_9_10_1 doi: 10.3390/rs15020539 – start-page: 1078 volume-title: Edge‐Guided Representation Learning for Underwater Object Detection year: 2024 ident: e_1_2_9_7_1 – ident: e_1_2_9_5_1 doi: 10.3390/rs12203431 – ident: e_1_2_9_28_1 doi: 10.3354/ab00450 – ident: e_1_2_9_22_1 doi: 10.3390/s22239536 – ident: e_1_2_9_24_1 doi: 10.1186/s40537-023-00701-y – ident: e_1_2_9_3_1 doi: 10.1007/s10530-020-02434-y – ident: e_1_2_9_17_1 doi: 10.1080/21642583.2021.1990159 – volume: 2017 start-page: 89 year: 2017 ident: e_1_2_9_2_1 article-title: Deep Learning Algorithm for Autonomous Driving Using GoogLeNet publication-title: IEEE Intelligent Vehicles Symposium (IV) – ident: e_1_2_9_30_1 – ident: e_1_2_9_18_1 – ident: e_1_2_9_20_1 doi: 10.1017/S1068280500010157 – ident: e_1_2_9_16_1 doi: 10.1672/0277-5212(2007)27[1144:MSMVUA]2.0.CO;2 – volume: 55 start-page: 1 year: 2017 ident: e_1_2_9_21_1 article-title: A Review of Aquatic Plant Monitoring and Assessment Methods publication-title: Journal of Aquatic Plant Management – ident: e_1_2_9_26_1 doi: 10.1007/978-3-642-35749-7_1 – ident: e_1_2_9_31_1 doi: 10.1109/LRA.2023.3245405 – ident: e_1_2_9_11_1 doi: 10.1109/CVPR.2014.81 – ident: e_1_2_9_29_1 |
| SSID | ssj0009616 |
| Score | 2.39636 |
| Snippet | ABSTRACT
Standard tools for detection and identification of invasive macrophytes have limitations that may result in failure to detect patches of invasive... Standard tools for detection and identification of invasive macrophytes have limitations that may result in failure to detect patches of invasive vegetation.... |
| SourceID | proquest crossref wiley |
| SourceType | Aggregation Database Index Database Publisher |
| SubjectTerms | 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 |
| Title | Identifying Hydrilla verticillata in Real Time With a Machine Learning–Based Underwater Object Detection Program |
| URI | https://onlinelibrary.wiley.com/doi/abs/10.1002%2Faqc.70054 https://www.proquest.com/docview/3161442554 https://www.proquest.com/docview/3200302618 |
| Volume | 35 |
| WOSCitedRecordID | wos001406990100001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVWIB databaseName: Wiley Online Library Full Collection 2020 customDbUrl: eissn: 1099-0755 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0009616 issn: 1052-7613 databaseCode: DRFUL dateStart: 19960101 isFulltext: true titleUrlDefault: https://onlinelibrary.wiley.com providerName: Wiley-Blackwell |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1LS8QwEB50VRDBt7i-iOLBS7VJ0zbBk6_Fg2983UqSprqXqt2qePM_-A_9JSZpd1cPguBtICktmUznm2TmG4D1MGQaZzr1sI65R6nEnoio8FQY8cwXNGauQu76KD45Ybe3_GwAtru1MBU_RO_AzVqG-19bAxeys9UnDRVPajO2iGMQhojZt7QBQ_sXraujPudu5DqfGgRhQKRxW11iIZ9s9R7-6Y76GPM7UnWupjXxr4-chPEaYaKdaktMwYDOp2Gk6jn5ZqQDVUtjp0qLvKasnoGiqtl1dU_o8C0tbD8i9FLlXRuxFKidowsDLJGtG0E37fIeCXTssjE1qola7z7fP3aNZ0yR66j0aqBsgU6lPe1B-7p0iV85OquywmbhqnVwuXfo1R0ZPEW4Tz2qJRUhZz5LY4McGI9EFlIiib3s1FhiHVGppQlZiEpTqgWXGacZI4HCJM1wMAeN_CHX84BkkHKJpbCIiKpAMKZDX_qZpL7ASmVNWOsqJnmsiDeSimKZJGZVE7eqTVjqqiypba-TBNgGuSZUMsOrvWFjNfYqROT64dnMsTl5NvxkTdhwCvz9JcnO-Z4TFv4-dRFGiW0U7M5qlqBRFs96GYbVS9nuFCv1Rv0C88_sIQ |
| linkProvider | Wiley-Blackwell |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1fT9swED-xsmlo0v6xiW4MvGkPewnEjpPY0l4YbVW0trAKNt4i27GhL2ELaSfe-A58Qz4JtpO228MkpL2dZEeJbF_ud-e73wF8jGOmsdF5gHXKA0olDkRCRaDihJtQ0JT5Crnvg3Q0Yqen_GgFPs9rYWp-iEXAzWmG_187BXcB6d0la6j4pXZSBzkewCq1xyhuwWpn3DsZLEl3E9_61EIIiyKt3ZozC4Vkd_Hw3_ZoCTL_hKre1vSe_d9XPoenDcZEe_WheAErungJj-quk1dW6qpGenKotCga0up1KOuqXV_5hPpXeek6EqFZnXltxUqgSYHGFloiVzmCfkyqcyTQ0OdjatRQtZ7dXt98sbYxR76n0m8LZkt0KF28B3V05VO_CnRU54W9gpNe93i_HzQ9GQJFeEgDqiUVMWchy1OLHRhPhIkpkcRdd2ossU6o1NI6LUTlOdWCS8OpYSRSmOQGR6-hVVwUegOQjHIusRQOE1EVCcZ0HMrQSBoKrJRpw4f5zmQ_a-qNrCZZJpld1cyvahs253uWNdp3mUXYubnWWbLD7xfDVm_cZYgo9MXUznFZec4BZW345Hfw3y_J9r7te-HN_aduw-P-8XCQDQ5GX9_CGnFtg33kZhNaVTnV7-ChmlWTy3KrObV3GF7wEQ |
| linkToPdf | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV3NbtQwEB6VFiqExD9ioYBBHLiExo6T2BKX0u2qiGW7VLT0FvlnXPaSljQt6o134A15EmwnuwsHJCRuI9lRIo8n84098w3AyzwXSB3ahGIpE841TVTBVWLyQrpU8VLECrnDcTmZiKMjOV2BN_NamI4fYnHgFiwj_q-DgeOpdZtL1lD11bwuA-S4Ams8l4U3y7Xh_uhgvCTdLWLrUw8hPIr0fmvOLJSyzcXDf_qjJcj8HapGXzO69X9feRtu9hiTbHWb4g6sYH0XrnVdJy-9tGN66caeQVX3pNX3oOmqdmPlE9m9tE3oSEQuusxrL7aKzGqy76ElCZUj5POs_UIU-RDzMZH0VK3HP7__eOt9oyWxp9I3D2YbsqfDeQ8ZYhtTv2oy7fLC7sPBaOfT9m7S92RIDJMpTzhqrnIpUmFLjx2ELJTLOdMsXHci1RQLrlH7oIUZazkqqZ3kTrDMUGYdzR7Aan1S40MgOrNSU60CJuImU0JgnurUaZ4qaowbwIu5ZqrTjnqj6kiWWeVXtYqrOoCNuc6q3vrOqoyGMNcHS374-WLY2024DFE1npz7OSErLwSgYgCvogb__pJq6-N2FB79-9RnsD4djqrxu8n7x3Cdha7B8eBmA1bb5hyfwFVz0c7Omqf9pv0FuuDvjA |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Identifying+Hydrilla+verticillata+in+Real+Time+With+a+Machine+Learning%E2%80%93Based+Underwater+Object+Detection+Program&rft.jtitle=Aquatic+conservation&rft.au=Jeong%2C+Han%C2%A0S.&rft.au=Schad%2C+Aaron%C2%A0N.&rft.au=Cheng%2C+Jing%E2%80%90Ru%C2%A0C.&rft.au=Donohue%2C+Griffin&rft.date=2025-01-01&rft.issn=1052-7613&rft.eissn=1099-0755&rft.volume=35&rft.issue=1&rft.epage=n%2Fa&rft_id=info:doi/10.1002%2Faqc.70054&rft.externalDBID=10.1002%252Faqc.70054&rft.externalDocID=AQC70054 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1052-7613&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1052-7613&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1052-7613&client=summon |