Real-time biodiversity analysis using deep-learning algorithms on mobile robotic platforms
Ecological biodiversity is declining at an unprecedented rate. To combat such irreversible changes in natural ecosystems, biodiversity conservation initiatives are being conducted globally. However, the lack of a feasible methodology to quantify biodiversity in real-time and investigate population d...
Saved in:
| Published in: | PeerJ. Computer science Vol. 9; p. e1502 |
|---|---|
| Main Authors: | , , |
| Format: | Journal Article |
| Language: | English |
| Published: |
San Diego, USA
PeerJ. Ltd
25.08.2023
PeerJ Inc |
| Subjects: | |
| ISSN: | 2376-5992, 2376-5992 |
| Online Access: | Get full text |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Abstract | Ecological biodiversity is declining at an unprecedented rate. To combat such irreversible changes in natural ecosystems, biodiversity conservation initiatives are being conducted globally. However, the lack of a feasible methodology to quantify biodiversity in real-time and investigate population dynamics in spatiotemporal scales prevents the use of ecological data in environmental planning. Traditionally, ecological studies rely on the census of an animal population by the “capture, mark and recapture” technique. In this technique, human field workers manually count, tag and observe tagged individuals, making it time-consuming, expensive, and cumbersome to patrol the entire area. Recent research has also demonstrated the potential for inexpensive and accessible sensors for ecological data monitoring. However, stationary sensors collect localised data which is highly specific on the placement of the setup. In this research, we propose the methodology for biodiversity monitoring utilising state-of-the-art deep learning (DL) methods operating in real-time on sample payloads of mobile robots. Such trained DL algorithms demonstrate a mean average precision (mAP) of 90.51% in an average inference time of 67.62 milliseconds within 6,000 training epochs. We claim that the use of such mobile platform setups inferring real-time ecological data can help us achieve our goal of quick and effective biodiversity surveys. An experimental test payload is fabricated, and online as well as offline field surveys are conducted, validating the proposed methodology for species identification that can be further extended to geo-localisation of flora and fauna in any ecosystem. |
|---|---|
| AbstractList | Ecological biodiversity is declining at an unprecedented rate. To combat such irreversible changes in natural ecosystems, biodiversity conservation initiatives are being conducted globally. However, the lack of a feasible methodology to quantify biodiversity in real-time and investigate population dynamics in spatiotemporal scales prevents the use of ecological data in environmental planning. Traditionally, ecological studies rely on the census of an animal population by the "capture, mark and recapture" technique. In this technique, human field workers manually count, tag and observe tagged individuals, making it time-consuming, expensive, and cumbersome to patrol the entire area. Recent research has also demonstrated the potential for inexpensive and accessible sensors for ecological data monitoring. However, stationary sensors collect localised data which is highly specific on the placement of the setup. In this research, we propose the methodology for biodiversity monitoring utilising state-of-the-art deep learning (DL) methods operating in real-time on sample payloads of mobile robots. Such trained DL algorithms demonstrate a mean average precision (mAP) of 90.51% in an average inference time of 67.62 milliseconds within 6,000 training epochs. We claim that the use of such mobile platform setups inferring real-time ecological data can help us achieve our goal of quick and effective biodiversity surveys. An experimental test payload is fabricated, and online as well as offline field surveys are conducted, validating the proposed methodology for species identification that can be further extended to geo-localisation of flora and fauna in any ecosystem. Ecological biodiversity is declining at an unprecedented rate. To combat such irreversible changes in natural ecosystems, biodiversity conservation initiatives are being conducted globally. However, the lack of a feasible methodology to quantify biodiversity in real-time and investigate population dynamics in spatiotemporal scales prevents the use of ecological data in environmental planning. Traditionally, ecological studies rely on the census of an animal population by the "capture, mark and recapture" technique. In this technique, human field workers manually count, tag and observe tagged individuals, making it time-consuming, expensive, and cumbersome to patrol the entire area. Recent research has also demonstrated the potential for inexpensive and accessible sensors for ecological data monitoring. However, stationary sensors collect localised data which is highly specific on the placement of the setup. In this research, we propose the methodology for biodiversity monitoring utilising state-of-the-art deep learning (DL) methods operating in real-time on sample payloads of mobile robots. Such trained DL algorithms demonstrate a mean average precision (mAP) of 90.51% in an average inference time of 67.62 milliseconds within 6,000 training epochs. We claim that the use of such mobile platform setups inferring real-time ecological data can help us achieve our goal of quick and effective biodiversity surveys. An experimental test payload is fabricated, and online as well as offline field surveys are conducted, validating the proposed methodology for species identification that can be further extended to geo-localisation of flora and fauna in any ecosystem.Ecological biodiversity is declining at an unprecedented rate. To combat such irreversible changes in natural ecosystems, biodiversity conservation initiatives are being conducted globally. However, the lack of a feasible methodology to quantify biodiversity in real-time and investigate population dynamics in spatiotemporal scales prevents the use of ecological data in environmental planning. Traditionally, ecological studies rely on the census of an animal population by the "capture, mark and recapture" technique. In this technique, human field workers manually count, tag and observe tagged individuals, making it time-consuming, expensive, and cumbersome to patrol the entire area. Recent research has also demonstrated the potential for inexpensive and accessible sensors for ecological data monitoring. However, stationary sensors collect localised data which is highly specific on the placement of the setup. In this research, we propose the methodology for biodiversity monitoring utilising state-of-the-art deep learning (DL) methods operating in real-time on sample payloads of mobile robots. Such trained DL algorithms demonstrate a mean average precision (mAP) of 90.51% in an average inference time of 67.62 milliseconds within 6,000 training epochs. We claim that the use of such mobile platform setups inferring real-time ecological data can help us achieve our goal of quick and effective biodiversity surveys. An experimental test payload is fabricated, and online as well as offline field surveys are conducted, validating the proposed methodology for species identification that can be further extended to geo-localisation of flora and fauna in any ecosystem. |
| ArticleNumber | e1502 |
| Audience | Academic |
| Author | Maski, Prajwal Thondiyath, Asokan Panigrahi, Siddhant |
| Author_xml | – sequence: 1 givenname: Siddhant surname: Panigrahi fullname: Panigrahi, Siddhant organization: Department of Engineering Design, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India – sequence: 2 givenname: Prajwal surname: Maski fullname: Maski, Prajwal organization: Department of Engineering Design, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India – sequence: 3 givenname: Asokan surname: Thondiyath fullname: Thondiyath, Asokan organization: Department of Engineering Design, Indian Institute of Technology Madras, Chennai, Tamil Nadu, India |
| BookMark | eNp1kktv1DAUhSNUJErpkn0kNrDIYMdxnKxQVfEYqRJSgQ0b69q-ST1y4sH2VMy_x-lUiCCwF3595-j66jwvzmY_Y1G8pGQjBBVv94hhV-m4oZzUT4rzmom24n1fn_2xf1ZcxrgjhFBO8-jPi--3CK5KdsJSWW_sPYZo07GEGdwx2lgeop3H0iDuK4cQ5uUEbvTBprspln4uJ6-swzJ45ZPV5d5BGnyY4ovi6QAu4uXjelF8-_D-6_Wn6ubzx-311U2lOatTxYF2jKPhnWAdNgOnmpGhFqho2-IgKFe8Fkpw0xsURA3MKENUYwiqATrFLortydd42Ml9sBOEo_Rg5cOFD6OEkCtzKBshGG9I27YMmxZVT2uC0IBuWN8C8uz17uS1P6gJjcY5BXAr0_XLbO_k6O8lJU3Pe1Fnh9ePDsH_OGBMcrJRo3Mwoz9EWXdt0_WiYyKjr07oCLk2Ow8-W-oFl1eirSlltOkztfkHlafByeocgiF3fy14sxJkJuHPNMIhRrn9crtmqxOrg48x4PD7q5TIJVfyIVdSR7nkKvPsL17bBMn6pRvW_Uf1C5zw1g4 |
| CitedBy_id | crossref_primary_10_1016_j_array_2025_100412 crossref_primary_10_1371_journal_pcbi_1012520 crossref_primary_10_3390_biology14050520 |
| Cites_doi | 10.1038/s41598-019-44565-w 10.1016/j.compag.2018.01.009 10.3996/JFWM-20-076 10.23919/JCIN.2019.8917884 10.1111/2041-210X.13504 10.3389/fpls.2016.01419 10.3390/s21020343 10.1016/B978-0-12-816034-3.00008-0 10.1111/2041-210X.13901 10.1071/WR04003 10.1644/07-MAMM-A-011.1 10.1016/j.crvi.2011.04.004 10.3390/s21185987 10.3390/s22020497 10.1371/journal.pone.0214168 10.1016/j.compag.2023.107707 10.1038/s41467-022-27980-y 10.1109/TITS.2020.3014013 10.7717/peerj.1831 10.1038/sdata.2015.26 10.1016/j.mehy.2020.109761 10.1109/ACCESS.2020.2964608 10.3390/rs12010182 10.1093/biosci/biab073 |
| ContentType | Journal Article |
| Copyright | COPYRIGHT 2023 PeerJ. Ltd. 2023 Panigrahi et al. 2023 Panigrahi et al. 2023 Panigrahi et al. |
| Copyright_xml | – notice: COPYRIGHT 2023 PeerJ. Ltd. – notice: 2023 Panigrahi et al. – notice: 2023 Panigrahi et al. 2023 Panigrahi et al. |
| DBID | AAYXX CITATION ISR 7X8 5PM DOA |
| DOI | 10.7717/peerj-cs.1502 |
| DatabaseName | CrossRef Gale In Context: Science MEDLINE - Academic PubMed Central (Full Participant titles) DOAJ Directory of Open Access Journals |
| DatabaseTitle | CrossRef MEDLINE - Academic |
| DatabaseTitleList | CrossRef MEDLINE - Academic |
| Database_xml | – sequence: 1 dbid: DOA name: DOAJ Directory of Open Access Journals url: https://www.doaj.org/ sourceTypes: Open Website – sequence: 2 dbid: 7X8 name: MEDLINE - Academic url: https://search.proquest.com/medline sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 2376-5992 |
| ExternalDocumentID | oai_doaj_org_article_47735406663e46eb9120ea4ac4396ae5 PMC10495972 A762113149 10_7717_peerj_cs_1502 |
| GroupedDBID | 53G 5VS 8FE 8FG AAFWJ AAYXX ABUWG ADBBV AFFHD AFKRA AFPKN ALMA_UNASSIGNED_HOLDINGS ARAPS ARCSS AZQEC BCNDV BENPR BGLVJ BPHCQ CCPQU CITATION DWQXO FRP GNUQQ GROUPED_DOAJ HCIFZ IAO ICD IEA ISR ITC K6V K7- M~E OK1 P62 PHGZM PHGZT PIMPY PQGLB PQQKQ PROAC RPM 7X8 PUEGO 5PM |
| ID | FETCH-LOGICAL-c532t-5a1835ed58738e4f51c30f27eb166ef715b527b75d9de70bf3dbd0b4d0ebfa8b3 |
| IEDL.DBID | DOA |
| ISICitedReferencesCount | 6 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001059148300001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 2376-5992 |
| IngestDate | Fri Oct 03 12:38:05 EDT 2025 Tue Nov 04 02:06:19 EST 2025 Thu Sep 04 18:03:45 EDT 2025 Tue Nov 11 10:26:57 EST 2025 Tue Nov 04 18:38:25 EST 2025 Thu Nov 13 16:26:19 EST 2025 Sat Nov 29 05:31:00 EST 2025 Tue Nov 18 22:27:07 EST 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Language | English |
| License | https://creativecommons.org/licenses/by/4.0 This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c532t-5a1835ed58738e4f51c30f27eb166ef715b527b75d9de70bf3dbd0b4d0ebfa8b3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
| OpenAccessLink | https://doaj.org/article/47735406663e46eb9120ea4ac4396ae5 |
| PQID | 2864897837 |
| PQPubID | 23479 |
| PageCount | e1502 |
| ParticipantIDs | doaj_primary_oai_doaj_org_article_47735406663e46eb9120ea4ac4396ae5 pubmedcentral_primary_oai_pubmedcentral_nih_gov_10495972 proquest_miscellaneous_2864897837 gale_infotracmisc_A762113149 gale_infotracacademiconefile_A762113149 gale_incontextgauss_ISR_A762113149 crossref_primary_10_7717_peerj_cs_1502 crossref_citationtrail_10_7717_peerj_cs_1502 |
| PublicationCentury | 2000 |
| PublicationDate | 2023-08-25 |
| PublicationDateYYYYMMDD | 2023-08-25 |
| PublicationDate_xml | – month: 08 year: 2023 text: 2023-08-25 day: 25 |
| PublicationDecade | 2020 |
| PublicationPlace | San Diego, USA |
| PublicationPlace_xml | – name: San Diego, USA |
| PublicationTitle | PeerJ. Computer science |
| PublicationYear | 2023 |
| Publisher | PeerJ. Ltd PeerJ Inc |
| Publisher_xml | – name: PeerJ. Ltd – name: PeerJ Inc |
| References | Witmer (10.7717/peerj-cs.1502/ref-42) 2005; 32 Hassaballah (10.7717/peerj-cs.1502/ref-11) 2020; 22 Panigrahi (10.7717/peerj-cs.1502/ref-31) 2021; 21 Dronecode (10.7717/peerj-cs.1502/ref-6) 2021 Betke (10.7717/peerj-cs.1502/ref-2) 2008; 89 Krizhevsky (10.7717/peerj-cs.1502/ref-16) 2012; 25 Arablouei (10.7717/peerj-cs.1502/ref-1) 2023; 207 Palani (10.7717/peerj-cs.1502/ref-30) 2022 Parham (10.7717/peerj-cs.1502/ref-33) 2017 Ucar (10.7717/peerj-cs.1502/ref-38) 2020; 140 Panigrahi (10.7717/peerj-cs.1502/ref-32) 2022 Kim (10.7717/peerj-cs.1502/ref-15) 2020 Microsoft (10.7717/peerj-cs.1502/ref-27) 2023 Tamil Nadu Tourism (10.7717/peerj-cs.1502/ref-35) 2021 Mazzia (10.7717/peerj-cs.1502/ref-23) 2020; 8 Mohanty (10.7717/peerj-cs.1502/ref-28) 2016; 7 Ferentinos (10.7717/peerj-cs.1502/ref-8) 2018; 145 Tuia (10.7717/peerj-cs.1502/ref-37) 2022; 13 Villon (10.7717/peerj-cs.1502/ref-41) 2018; 6 Van Horn (10.7717/peerj-cs.1502/ref-39) 2018 Meng (10.7717/peerj-cs.1502/ref-25) 2020; 12 Google (10.7717/peerj-cs.1502/ref-10) 2021 Integrated Marine Observing System (IMOS) (10.7717/peerj-cs.1502/ref-14) 2023 Goëau (10.7717/peerj-cs.1502/ref-9) 2018 HumanSignal (10.7717/peerj-cs.1502/ref-13) 2021 Lostanlen (10.7717/peerj-cs.1502/ref-21) 2019; 14 Norouzzadeh (10.7717/peerj-cs.1502/ref-29) 2021; 12 Kutugata (10.7717/peerj-cs.1502/ref-18) 2021; 12 Zhao (10.7717/peerj-cs.1502/ref-43) 2019; 4 Howard (10.7717/peerj-cs.1502/ref-12) 2017 Le Maho (10.7717/peerj-cs.1502/ref-20) 2011; 334 Bjerge (10.7717/peerj-cs.1502/ref-3) 2021; 21 Maski (10.7717/peerj-cs.1502/ref-22) 2021 McEvoy (10.7717/peerj-cs.1502/ref-24) 2016; 4 Swanson (10.7717/peerj-cs.1502/ref-34) 2015; 2 Chalmers (10.7717/peerj-cs.1502/ref-5) 2021 Duhart (10.7717/peerj-cs.1502/ref-7) 2019 Tao (10.7717/peerj-cs.1502/ref-36) 2017 Villon (10.7717/peerj-cs.1502/ref-40) 2022; 22 Kuenzi (10.7717/peerj-cs.1502/ref-17) 1998; 26 Miao (10.7717/peerj-cs.1502/ref-26) 2019; 9 Borowiec (10.7717/peerj-cs.1502/ref-4) 2022; 13 Lahoz-Monfort (10.7717/peerj-cs.1502/ref-19) 2021; 71 |
| References_xml | – volume: 9 start-page: 8137 issue: 1 year: 2019 ident: 10.7717/peerj-cs.1502/ref-26 article-title: Insights and approaches using deep learning to classify wildlife publication-title: Scientific Reports doi: 10.1038/s41598-019-44565-w – year: 2023 ident: 10.7717/peerj-cs.1502/ref-27 article-title: Megadetector AI for Earth – year: 2017 ident: 10.7717/peerj-cs.1502/ref-33 article-title: Animal population censusing at scale with citizen science and photographic identification – volume: 145 start-page: 311 year: 2018 ident: 10.7717/peerj-cs.1502/ref-8 article-title: Deep learning models for plant disease detection and diagnosis publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2018.01.009 – year: 2021 ident: 10.7717/peerj-cs.1502/ref-10 article-title: Open Images Dataset v6 – volume: 12 start-page: 412 issue: 2 year: 2021 ident: 10.7717/peerj-cs.1502/ref-18 article-title: Automatic camera-trap classification using wildlife-specific deep learning in Nilgai management publication-title: Journal of Fish and Wildlife Management doi: 10.3996/JFWM-20-076 – volume: 4 start-page: 38 issue: 3 year: 2019 ident: 10.7717/peerj-cs.1502/ref-43 article-title: DeepCount: crowd counting with Wi-Fi using deep learning publication-title: Journal of Communications and Information Networks doi: 10.23919/JCIN.2019.8917884 – start-page: 1 year: 2021 ident: 10.7717/peerj-cs.1502/ref-5 article-title: Modelling animal biodiversity using acoustic monitoring and deep learning – start-page: 8769 year: 2018 ident: 10.7717/peerj-cs.1502/ref-39 article-title: The inaturalist species classification and detection dataset – start-page: 49 year: 2021 ident: 10.7717/peerj-cs.1502/ref-22 article-title: Plant disease detection using advanced deep learning algorithms: a case study of papaya ring spot disease – volume: 12 start-page: 150 issue: 1 year: 2021 ident: 10.7717/peerj-cs.1502/ref-29 article-title: A deep active learning system for species identification and counting in camera trap images publication-title: Methods in Ecology and Evolution doi: 10.1111/2041-210X.13504 – volume: 7 start-page: 1419 year: 2016 ident: 10.7717/peerj-cs.1502/ref-28 article-title: Using deep learning for image-based plant disease detection publication-title: Frontiers in Plant Science doi: 10.3389/fpls.2016.01419 – volume: 21 start-page: 343 issue: 2 year: 2021 ident: 10.7717/peerj-cs.1502/ref-3 article-title: An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning publication-title: Sensors doi: 10.3390/s21020343 – start-page: 128 year: 2022 ident: 10.7717/peerj-cs.1502/ref-30 article-title: Real-time joint angle estimation using mediapipe framework and inertial sensors – start-page: 239 volume-title: Biomedical information technology year: 2020 ident: 10.7717/peerj-cs.1502/ref-15 article-title: Deep learning in biomedical image analysis doi: 10.1016/B978-0-12-816034-3.00008-0 – volume: 13 start-page: 1640 issue: 8 year: 2022 ident: 10.7717/peerj-cs.1502/ref-4 article-title: Deep learning as a tool for ecology and evolution publication-title: Methods in Ecology and Evolution doi: 10.1111/2041-210X.13901 – volume: 32 start-page: 259 issue: 3 year: 2005 ident: 10.7717/peerj-cs.1502/ref-42 article-title: Wildlife population monitoring: some practical considerations publication-title: Wildlife Research doi: 10.1071/WR04003 – volume: 89 start-page: 18 issue: 1 year: 2008 ident: 10.7717/peerj-cs.1502/ref-2 article-title: Thermal imaging reveals significantly smaller Brazilian free-tailed bat colonies than previously estimated publication-title: Journal of Mammalogy doi: 10.1644/07-MAMM-A-011.1 – volume: 334 start-page: 378 issue: 5–6 year: 2011 ident: 10.7717/peerj-cs.1502/ref-20 article-title: An ethical issue in biodiversity science: the monitoring of penguins with flipper bands publication-title: Comptes Rendus Biologies doi: 10.1016/j.crvi.2011.04.004 – year: 2021 ident: 10.7717/peerj-cs.1502/ref-35 article-title: Tamil Nadu Tourism Development Centre, Guindy National Park – year: 2021 ident: 10.7717/peerj-cs.1502/ref-13 article-title: LabelImg—a graphical image annotation tool – volume: 21 start-page: 5987 issue: 18 year: 2021 ident: 10.7717/peerj-cs.1502/ref-31 article-title: Design, analysis, and testing of a hybrid VTOL tilt-rotor UAV for increased endurance publication-title: Sensors doi: 10.3390/s21185987 – volume: 22 start-page: 497 issue: 2 year: 2022 ident: 10.7717/peerj-cs.1502/ref-40 article-title: Confronting deep-learning and biodiversity challenges for automatic video-monitoring of marine ecosystems publication-title: Sensors doi: 10.3390/s22020497 – volume: 6 start-page: e26818v1 year: 2018 ident: 10.7717/peerj-cs.1502/ref-41 article-title: A deep learning algorithm for accurate and fast identification of coral reef fishes in underwater videos publication-title: PeerJ Preprints – start-page: 401 year: 2022 ident: 10.7717/peerj-cs.1502/ref-32 article-title: Deep learning based real-time biodiversity analysis using aerial vehicles – year: 2023 ident: 10.7717/peerj-cs.1502/ref-14 – volume: 26 start-page: 307 year: 1998 ident: 10.7717/peerj-cs.1502/ref-17 article-title: Detection of bats by mist-nets and ultrasonic sensors publication-title: Wildlife Society Bulletin – year: 2018 ident: 10.7717/peerj-cs.1502/ref-9 article-title: Overview of BirdCLEF 2018: monospecies vs. soundscape bird identification – volume: 14 start-page: e0214168 issue: 10 year: 2019 ident: 10.7717/peerj-cs.1502/ref-21 article-title: Robust sound event detection in bioacoustic sensor networks publication-title: PLOS ONE doi: 10.1371/journal.pone.0214168 – volume: 207 start-page: 107707 year: 2023 ident: 10.7717/peerj-cs.1502/ref-1 article-title: Animal behavior classification via deep learning on embedded systems publication-title: Computers and Electronics in Agriculture doi: 10.1016/j.compag.2023.107707 – volume: 13 start-page: 1 issue: 1 year: 2022 ident: 10.7717/peerj-cs.1502/ref-37 article-title: Perspectives in machine learning for wildlife conservation publication-title: Nature Communications doi: 10.1038/s41467-022-27980-y – volume: 22 start-page: 4230 issue: 7 year: 2020 ident: 10.7717/peerj-cs.1502/ref-11 article-title: Vehicle detection and tracking in adverse weather using a deep learning framework publication-title: IEEE Transactions on Intelligent Transportation Systems doi: 10.1109/TITS.2020.3014013 – year: 2017 ident: 10.7717/peerj-cs.1502/ref-12 article-title: Mobilenets: efficient convolutional neural networks for mobile vision applications – volume: 4 start-page: e1831 year: 2016 ident: 10.7717/peerj-cs.1502/ref-24 article-title: Evaluation of unmanned aerial vehicle shape, flight path and camera type for waterfowl surveys: disturbance effects and species recognition publication-title: PeerJ doi: 10.7717/peerj.1831 – year: 2021 ident: 10.7717/peerj-cs.1502/ref-6 article-title: QGround control for Pixhawk4 – volume: 2 start-page: 1 issue: 1 year: 2015 ident: 10.7717/peerj-cs.1502/ref-34 article-title: Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna publication-title: Scientific Data doi: 10.1038/sdata.2015.26 – volume: 140 start-page: 109761 year: 2020 ident: 10.7717/peerj-cs.1502/ref-38 article-title: COVIDiagnosis-net: deep bayes-squeezenet based diagnosis of the coronavirus disease 2019 (COVID-19) from X-ray images publication-title: Medical Hypotheses doi: 10.1016/j.mehy.2020.109761 – start-page: 3 year: 2019 ident: 10.7717/peerj-cs.1502/ref-7 article-title: Deep learning locally trained wildlife sensing in real acoustic wetland environment – volume: 8 start-page: 9102 year: 2020 ident: 10.7717/peerj-cs.1502/ref-23 article-title: Real-time apple detection system using embedded systems with hardware accelerators: an edge AI application publication-title: IEEE Access doi: 10.1109/ACCESS.2020.2964608 – start-page: 315 year: 2017 ident: 10.7717/peerj-cs.1502/ref-36 article-title: An object detection system based on YOLO in traffic scene – volume: 12 start-page: 182 issue: 1 year: 2020 ident: 10.7717/peerj-cs.1502/ref-25 article-title: Real-time detection of ground objects based on unmanned aerial vehicle remote sensing with deep learning: application in excavator detection for pipeline safety publication-title: Remote Sensing doi: 10.3390/rs12010182 – volume: 71 start-page: 1038 issue: 10 year: 2021 ident: 10.7717/peerj-cs.1502/ref-19 article-title: A comprehensive overview of technologies for species and habitat monitoring and conservation publication-title: BioScience doi: 10.1093/biosci/biab073 – volume: 25 start-page: 1097 year: 2012 ident: 10.7717/peerj-cs.1502/ref-16 article-title: Imagenet classification with deep convolutional neural networks publication-title: Advances in Neural Information Processing Systems |
| SSID | ssj0001511119 |
| Score | 2.3114471 |
| Snippet | Ecological biodiversity is declining at an unprecedented rate. To combat such irreversible changes in natural ecosystems, biodiversity conservation initiatives... |
| SourceID | doaj pubmedcentral proquest gale crossref |
| SourceType | Open Website Open Access Repository Aggregation Database Enrichment Source Index Database |
| StartPage | e1502 |
| SubjectTerms | Algorithms Algorithms and Analysis of Algorithms Autonomous Systems Biological diversity Biological diversity conservation Computer Vision Data mining Deep learning Ecosystems Object detection Population biology Protection and preservation Real-Time and Embedded Systems Robotics UAV YOLO v3 |
| Title | Real-time biodiversity analysis using deep-learning algorithms on mobile robotic platforms |
| URI | https://www.proquest.com/docview/2864897837 https://pubmed.ncbi.nlm.nih.gov/PMC10495972 https://doaj.org/article/47735406663e46eb9120ea4ac4396ae5 |
| Volume | 9 |
| WOSCitedRecordID | wos001059148300001&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: PRVAON databaseName: DOAJ Directory of Open Access Journals customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: DOA dateStart: 20150101 isFulltext: true titleUrlDefault: https://www.doaj.org/ providerName: Directory of Open Access Journals – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: M~E dateStart: 20150101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre – providerCode: PRVPQU databaseName: Advanced Technologies & Aerospace Database customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: P5Z dateStart: 20150527 isFulltext: true titleUrlDefault: https://search.proquest.com/hightechjournals providerName: ProQuest – providerCode: PRVPQU databaseName: Computer Science Database customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: K7- dateStart: 20150527 isFulltext: true titleUrlDefault: http://search.proquest.com/compscijour providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: BENPR dateStart: 20150527 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2376-5992 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0001511119 issn: 2376-5992 databaseCode: PIMPY dateStart: 20150527 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV3Nb9MwFLdgcODCN6IwKoMQXAhLnDi2jxvqxIRWRQWksovlz66oS6qk5cjfznOSTg0IceHiQ_yUxM_v03r-PYRe28RxI7IkSqmHBMVQEwluWUSENXEeG-WJaptNsOmUz-ei2Gv1FWrCOnjgjnFHGWPhaAKi7NRludMiIbFTmTLgSXPlWvTSmIm9ZKq7HxxMgehANRmkLEdr5-rvkWneQwREBk6oxer_0yL_XiW553ZO76O7fbyIj7v_fIBuuPIhurfrxYB71XyELmYQ8UWhUzzWy8ruqi2w6kFHcChwX2Dr3DrqO0UssFotqnq5ubxqcFXiq0qDicB1pSv4GF6v1CZEtM1j9PV08uXDx6jvmxAZmpJNRBXoKXWWcpZyl3mamDT2hIFZznPnWUI1JUwzaoV1LNY-tdrGOrOx015xnT5BB2VVuqcI5z52XlDwdBAHOJpzkWdOayLgDVp7O0LvdoyUpgcVD70tVhKSi8B32fJdmkYGvo_Qm2vydYem8TfCk7Ar10QBBLt9AKIhe9GQ_xKNEXoV9lQGmIsy1NEs1LZp5NnnmTwGH5DAojIxQm97Il_BnxvVX0uA9QdkrAHl4YAS9NAMpl_uREeGqVC8Vrpq20jC84yHIzY2QnwgU4P1DWfK5WUL9g3psoCkjzz7Hxx5ju4QCNLCmTihh-hgU2_dC3Tb_Ngsm3qMbrI5H6NbJ5NpMRu3CgXjJxbBeP5zAmNBL2C-ODsvvv0C0E4sPw |
| linkProvider | Directory of Open Access Journals |
| 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=Real-time+biodiversity+analysis+using+deep-learning+algorithms+on+mobile+robotic+platforms&rft.jtitle=PeerJ.+Computer+science&rft.au=Panigrahi%2C+Siddhant&rft.au=Maski%2C+Prajwal&rft.au=Thondiyath%2C+Asokan&rft.date=2023-08-25&rft.issn=2376-5992&rft.eissn=2376-5992&rft.volume=9&rft.spage=e1502&rft_id=info:doi/10.7717%2Fpeerj-cs.1502&rft.externalDBID=n%2Fa&rft.externalDocID=10_7717_peerj_cs_1502 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2376-5992&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2376-5992&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2376-5992&client=summon |