Automatic detection of alien plant species in action camera images using the chopped picture method and the potential of citizen science
Monitoring and detection of invasive alien plant species are necessary for effective management and control measures. Although efforts have been made to detect alien trees using satellite images, the detection of alien herbaceous species has been difficult. In this study, we examined the possibility...
Saved in:
| Published in | Breeding Science Vol. 72; no. 1; pp. 96 - 106 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Tokyo
Japanese Society of Breeding
01.01.2022
Japan Science and Technology Agency |
| Subjects | |
| Online Access | Get full text |
Cover
Loading…
| Abstract | Monitoring and detection of invasive alien plant species are necessary for effective management and control measures. Although efforts have been made to detect alien trees using satellite images, the detection of alien herbaceous species has been difficult. In this study, we examined the possibility of detecting non-native plants using deep learning on images captured by two action cameras. We created a model for each camera using the chopped picture method. The models were able to detect the alien plant Solidago altissima (tall goldenrod) and obtained an average accuracy of 89%. This study proved that it is possible to automatically detect exotic plants using inexpensive action cameras through deep learning. This advancement suggests that, in the future, citizen science may be useful for conducting distribution surveys of alien plants in a wide area at a low cost. |
|---|---|
| AbstractList | Monitoring and detection of invasive alien plant species are necessary for effective management and control measures. Although efforts have been made to detect alien trees using satellite images, the detection of alien herbaceous species has been difficult. In this study, we examined the possibility of detecting non-native plants using deep learning on images captured by two action cameras. We created a model for each camera using the chopped picture method. The models were able to detect the alien plant Solidago altissima (tall goldenrod) and obtained an average accuracy of 89%. This study proved that it is possible to automatically detect exotic plants using inexpensive action cameras through deep learning. This advancement suggests that, in the future, citizen science may be useful for conducting distribution surveys of alien plants in a wide area at a low cost.Monitoring and detection of invasive alien plant species are necessary for effective management and control measures. Although efforts have been made to detect alien trees using satellite images, the detection of alien herbaceous species has been difficult. In this study, we examined the possibility of detecting non-native plants using deep learning on images captured by two action cameras. We created a model for each camera using the chopped picture method. The models were able to detect the alien plant Solidago altissima (tall goldenrod) and obtained an average accuracy of 89%. This study proved that it is possible to automatically detect exotic plants using inexpensive action cameras through deep learning. This advancement suggests that, in the future, citizen science may be useful for conducting distribution surveys of alien plants in a wide area at a low cost. Monitoring and detection of invasive alien plant species are necessary for effective management and control measures. Although efforts have been made to detect alien trees using satellite images, the detection of alien herbaceous species has been difficult. In this study, we examined the possibility of detecting non-native plants using deep learning on images captured by two action cameras. We created a model for each camera using the chopped picture method. The models were able to detect the alien plant Solidago altissima (tall goldenrod) and obtained an average accuracy of 89%. This study proved that it is possible to automatically detect exotic plants using inexpensive action cameras through deep learning. This advancement suggests that, in the future, citizen science may be useful for conducting distribution surveys of alien plants in a wide area at a low cost. Monitoring and detection of invasive alien plant species are necessary for effective management and control measures. Although efforts have been made to detect alien trees using satellite images, the detection of alien herbaceous species has been difficult. In this study, we examined the possibility of detecting non-native plants using deep learning on images captured by two action cameras. We created a model for each camera using the chopped picture method. The models were able to detect the alien plant Solidago altissima (tall goldenrod) and obtained an average accuracy of 89%. This study proved that it is possible to automatically detect exotic plants using inexpensive action cameras through deep learning. This advancement suggests that, in the future, citizen science may be useful for conducting distribution surveys of alien plants in a wide area at a low cost. |
| ArticleNumber | 21062 |
| Author | Ise, Takeshi Sasaki, Yu Takaya, Kosuke |
| Author_xml | – sequence: 1 fullname: Takaya, Kosuke organization: Graduate School of Agriculture, Kyoto University – sequence: 2 fullname: Sasaki, Yu organization: Center for the Promotion of Interdisciplinary Education and Research, Kyoto University – sequence: 3 fullname: Ise, Takeshi organization: Field Science Education and Research Center, Kyoto University |
| BookMark | eNp1kU2LFDEQhoOsuB968g8EvAhLr_nunoswLK4KC170HNLp6ukM3UmbpIX1F_izzfQMAy54SYXUU29V6r1GFz54QOgtJXeU1eTDPrVtumOUKPYCXVEu6orXXF6sd1HVipJLdJ3SnhAmiZCv0CVXJTYbcYX-bJccJpOdxR1ksNkFj0OPzejA43k0PuM0g3WQsPPYHAFrJogGu8nsyvuSnN_hPAC2Q5hn6PDsbF4i4AnyEDpsfLem55DBZ2fGQwfrsvtdeqSi7S28Ri97MyZ4c4o36MfDp-_3X6rHb5-_3m8fK6sakispDDHA2g0IyXshqSSs4VapnnNFuWVt21ImWSPqtmE1iLKi3pC2V0ra3vT8Bn086s5LO0Fny0DRjHqO5TPxSQfj9L8Z7wa9C790s2nqRogi8P4kEMPPBVLWk0sWxrIqCEvSxZINoRtSq4K-e4buwxJ9-Z5mSigqJResULdHysaQUoT-PAwl-uCwXh3Wq8OFps_oskhzcKVM68b_1GyPNfuUi2NnfROL7yOc2JppejjWmnPODiZq8Pwv0SfFGw |
| CitedBy_id | crossref_primary_10_1002_ece3_10698 crossref_primary_10_1007_s10530_023_03150_z crossref_primary_10_1155_2024_8854675 crossref_primary_10_1016_j_sciaf_2024_e02462 crossref_primary_10_3390_agriculture14122201 crossref_primary_10_1080_15481603_2024_2327146 |
| Cites_doi | 10.1111/j.1366-9516.2006.00278.x 10.3390/rs10040641 10.1093/aobpla/plab050 10.1016/S0034-4257(03)00096-8 10.1007/s10530-016-1217-z 10.3389/fpls.2017.00887 10.3390/rs1030318 10.1007/s11284-006-0035-7 10.1073/pnas.1908253116 10.1016/j.tree.2010.12.007 10.1177/001316446002000104 10.1186/s12859-018-2474-x 10.2307/25065637 10.1093/aobpla/plaa052 10.1016/j.compag.2020.105519 10.1098/rspb.2003.2327 10.1016/j.ecolind.2019.106020 10.1007/s00442-004-1679-z 10.1007/s10530-005-6419-8 10.1111/2041-210X.13473 10.1111/2041-210X.13296 10.1016/j.jag.2017.12.008 10.1098/rspb.2002.2179 10.1186/s12898-020-00331-5 10.1109/CVPR.2016.90 10.4236/oje.2018.83011 10.1016/j.compag.2020.105796 10.1111/een.12394 10.1002/fee.1826 10.1016/j.marpolbul.2016.06.072 10.1890/110278 10.1007/s10530-015-0885-4 10.1111/cobi.13223 10.1146/annurev-ecolsys-102209-144636 10.1016/j.actao.2012.10.011 10.1186/s13007-019-0462-4 10.1111/jzo.12509 10.1016/j.gecco.2019.e00812 10.1016/j.tree.2009.03.017 10.1111/j.1365-2664.2006.01227.x 10.1111/j.1755-263X.2011.00196.x 10.1016/j.tplants.2018.07.004 |
| ContentType | Journal Article |
| Copyright | 2022 by JAPANESE SOCIETY OF BREEDING Copyright Japan Science and Technology Agency 2022 Copyright © 2022 by JAPANESE SOCIETY OF BREEDING. Copyright © 2022 by JAPANESE SOCIETY OF BREEDING 2022 |
| Copyright_xml | – notice: 2022 by JAPANESE SOCIETY OF BREEDING – notice: Copyright Japan Science and Technology Agency 2022 – notice: Copyright © 2022 by JAPANESE SOCIETY OF BREEDING. – notice: Copyright © 2022 by JAPANESE SOCIETY OF BREEDING 2022 |
| DBID | AAYXX CITATION 7QO 8FD FR3 P64 RC3 7X8 5PM |
| DOI | 10.1270/jsbbs.21062 |
| DatabaseName | CrossRef Biotechnology Research Abstracts Technology Research Database Engineering Research Database Biotechnology and BioEngineering Abstracts Genetics Abstracts MEDLINE - Academic PubMed Central (Full Participant titles) |
| DatabaseTitle | CrossRef Genetics Abstracts Engineering Research Database Biotechnology Research Abstracts Technology Research Database Biotechnology and BioEngineering Abstracts MEDLINE - Academic |
| DatabaseTitleList | MEDLINE - Academic Genetics Abstracts |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Agriculture |
| DocumentTitleAlternate | Automatic detection of alien plant species |
| EISSN | 1347-3735 |
| EndPage | 106 |
| ExternalDocumentID | PMC8987844 10_1270_jsbbs_21062 article_jsbbs_72_1_72_21062_article_char_en |
| GroupedDBID | 23N 2WC 5GY A8Z ABDBF ACIWK ACPRK ADBBV ADRAZ AENEX AFRAH ALMA_UNASSIGNED_HOLDINGS AOIJS B.T BAWUL CS3 DIK DU5 E3Z EBD EBS ECGQY EJD ESTFP ESX EYRJQ GX1 HYE JSF JSH KQ8 M48 M~E N5S OK1 QF4 QN7 RJT RNS RPM RYR RZJ TKC TR2 XSB AAYXX ACUHS CITATION OVT 7QO 8FD FR3 P64 RC3 7X8 5PM |
| ID | FETCH-LOGICAL-c680t-54a0ae2b9e453f45150283c66f33613c2bbb1252847b827e4127fa0bf665cfaf3 |
| IEDL.DBID | M48 |
| ISSN | 1344-7610 |
| IngestDate | Thu Aug 21 18:28:05 EDT 2025 Thu Jul 10 16:26:38 EDT 2025 Mon Jun 30 04:44:57 EDT 2025 Tue Jul 01 01:03:16 EDT 2025 Thu Apr 24 23:05:02 EDT 2025 Sun Jul 28 05:37:14 EDT 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 1 |
| Language | English |
| License | This is an open-access article distributed under the terms of the Creative Commons Attribution (BY) License (CC-BY 4.0: https://creativecommons.org/licenses/by/4.0/). |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c680t-54a0ae2b9e453f45150283c66f33613c2bbb1252847b827e4127fa0bf665cfaf3 |
| Notes | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 Communicated by Sachiko Isobe |
| OpenAccessLink | http://journals.scholarsportal.info/openUrl.xqy?doi=10.1270/jsbbs.21062 |
| PMID | 36045894 |
| PQID | 2646155342 |
| PQPubID | 1966357 |
| PageCount | 11 |
| ParticipantIDs | pubmedcentral_primary_oai_pubmedcentral_nih_gov_8987844 proquest_miscellaneous_2709019076 proquest_journals_2646155342 crossref_primary_10_1270_jsbbs_21062 crossref_citationtrail_10_1270_jsbbs_21062 jstage_primary_article_jsbbs_72_1_72_21062_article_char_en |
| ProviderPackageCode | CITATION AAYXX |
| PublicationCentury | 2000 |
| PublicationDate | 2022-01-01 |
| PublicationDateYYYYMMDD | 2022-01-01 |
| PublicationDate_xml | – month: 01 year: 2022 text: 2022-01-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationPlace | Tokyo |
| PublicationPlace_xml | – name: Tokyo |
| PublicationTitle | Breeding Science |
| PublicationTitleAlternate | Breeding Science |
| PublicationYear | 2022 |
| Publisher | Japanese Society of Breeding Japan Science and Technology Agency |
| Publisher_xml | – name: Japanese Society of Breeding – name: Japan Science and Technology Agency |
| References | Kim, K.C. and L.B. Byrne (2006) Biodiversity loss and the taxonomic bottleneck: Emerging biodiversity science. Ecol Res 21: 794–810. Leung, B., D.M. Lodge, D. Finnoff, J.F. Shogren, M.A. Lewis and G. Lamberti (2002) An ounce of prevention or a pound of cure: Bioeconomic risk analysis of invasive species. Proc Biol Sci 269: 2407–2413. Levine, J.M., M. Vila, C.M.D. Antonio, J.S. Dukes, K. Grigulis and S. Lavorel (2003) Mechanisms underlying the impacts of exotic plant invasions. Proc Biol Sci 270: 775–781. Rai, P.K. and J.S. Singh (2020) Invasive alien plant species: Their impact on environment, ecosystem services and human health. Ecol Indic 111: 106020. Dickinson, J.L., B. Zuckerberg and D.N. Bonter (2010) Citizen science as an ecological research tool: Challenges and benefits. Annu Rev Ecol Evol Syst 41: 149–172. James, K. and K. Bradshaw (2020) Detecting plant species in the field with deep learning and drone technology. Methods Ecol Evol 11: 1509–1519. Claassens, L. and A.N. Hodgson (2018) Gaining insights into in situ behaviour of an endangered seahorse using action cameras. J Zool 304: 98–108. Prach, K. and L.R. Walker (2011) Four opportunities for studies of ecological succession. Trends Ecol Evol 26: 119–123. Seeland, M., M. Rzanny, D. Boho, J. Wäldchen and P. Mäder (2019) Image-based classification of plant genus and family for trained and untrained plant species. BMC Bioinformatics 20: 1–13. Watanabe, S., K. Sumi and T. Ise (2020) Identifying the vegetation type in Google Earth images using a convolutional neural network: A case study for Japanese bamboo forests. BMC Ecol 20: 1–14. Silvertown, J. (2009) A new dawn for citizen science. Trends Ecol Evol 24: 467–471. Schuttler, S.G., A.E. Sorensen, R.C. Jordan, C. Cooper and A. Shwartz (2018) Bridging the nature gap: can citizen science reverse the extinction of experience? Front Ecol Environ 16: 405–411. Crall, A.W., C.S. Jarnevich, N.E. Young, B.J. Panke, M. Renz and T.J. Stohlgren (2015) Citizen science contributes to our knowledge of invasive plant species distributions. Biol Invasions 17: 2415–2427. Qian, W., Y. Huang, Q. Liu, W. Fan, Z. Sun, H. Dong, F. Wan and X. Qiao (2020) UAV and a deep convolutional neural network for monitoring invasive alien plants in the wild. Comput Electron Agric 174: 105519. Agarwal, G., P. Belhumeur, S. Feiner, D. Jacobs, W.J. Kress, R. Ramamoorthi and S. White (2006) First steps toward an electronic field guide for plants. Taxon 55: 597–610. Hulme, P.E. (2006) Beyond control: Wider implications for the management of biological invasions. J Appl Ecol 43: 835–847. Pauchard, A. and K. Shea (2006) Integrating the study of non-native plant invasions across spatial scales. Biol Invasions 8: 399–413. Tan, M. and Q. Le (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. Proc Int Conf Mach Learn 97: 6105–6114. Brown, E.D. and B.K. Williams (2019) The potential for citizen science to produce reliable and useful information in ecology. Conserv Biol 33: 561–569. Miralles, L., E. Dopico, F. Devlo-Delva and E. Garcia-Vazquez (2016) Controlling populations of invasive pygmy mussel (Xenostrobus securis) through citizen science and environmental DNA. Mar Pollut Bull 110: 127–132. Jones, H.G. (2020) What plant is that? Tests of automated image recognition apps for plant identification on plants from the British flora. AoB Plants 12: plaa052. Allison, S.D. and P.M. Vitousek (2004) Rapid nutrient cycling in leaf litter from invasive plants in Hawai’i. Oecologia 141: 612–619. Miller-Rushing, A., R. Primack and R. Bonney (2012) The history of public participation in ecological research. Front Ecol Environ 10: 285–290. Ruckli, R., H.P. Rusterholz and B. Baur (2013) Invasion of Impatiens glandulifera affects terrestrial gastropods by altering microclimate. Acta Oecol (Montrouge) 47: 16–23. Kganyago, M., J. Odindi, C. Adjorlolo and P. Mhangara (2018) Evaluating the capability of Landsat 8 OLI and SPOT 6 for discriminating invasive alien species in the African Savanna landscape. Int J Appl Earth Obs Geoinf 67: 10–19. Cohen, J. (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20: 37–46. Singh, A.K., B. Ganapathysubramanian, S. Sarkar and A. Singh (2018) Deep learning for plant stress phenotyping: Trends and future perspectives. Trends Plant Sci 23: 883–898. Manfreda, S., M.F. McCabe, P.E. Miller, R. Lucas, V. Pajuelo Madrigal, G. Mallinis, E.B. Dor, D. Helman, L. Estes, G. Ciraolo et al. (2018) On the use of unmanned aerial systems for environmental monitoring. Remote Sens (Basel) 10: 641. Carter, G.A., K.L. Lucas, G.A. Blossom, C.L. Lassitter, D.M. Holiday, D.S Mooneyhan and D.R. Fastring, T.R. Holcombe and J.A. Griffith (2009) Remote sensing and mapping of tamarisk along the Colorado river, USA: A comparative use of summer-acquired Hyperion, Thematic Mapper and QuickBird data. Remote Sens (Basel) 1: 318–329. Crall, A.W., G.J. Newman, T.J. Stohlgren, K.A. Holfelder, J. Graham and D.M. Waller (2011) Assessing citizen science data quality: An invasive species case study. Conserv Lett 4: 433–442. Ise, T., M. Minagawa and M. Onishi (2018) Classifying 3 moss species by deep learning, using the “Chopped Picture” method. Open J Ecol 8: 166–173. Rzanny, M., P. Mäder, A. Deggelmann, M. Chen and J. Wäldchen (2019) Flowers, leaves or both? How to obtain suitable images for automated plant identification. Plant Methods 15: 1–11. Fusco, E.J., J.T. Finn, J.K. Balch, R.C. Nagy and B.A. Bradley (2019) Invasive grasses increase fire occurrence and frequency across US ecoregions. Proc Natl Acad Sci USA 116: 23594–23599. Palacios, F., G. Bueno, J. Salido, M.P. Diago, I. Hernández and J. Tardaguila (2020) Automated grapevine flower detection and quantification method based on computer vision and deep learning from on-the-go imaging using a mobile sensing platform under field conditions. Comput Electron Agric 178: 105796. Maistrello, L., P. Dioli, M. Bariselli, G.L. Mazzoli and I. Giacalone-Forini (2016) Citizen science and early detection of invasive species: Phenology of first occurrences of Halyomorpha halys in Southern Europe. Biol Invasions 18: 3109–3116. Underwood, E., S. Ustin and D. DiPietro (2003) Mapping non-native plants using hyperspectral imagery. Remote Sens Environ 86: 150–161. Johnson, B.A., A.D. Mader, R. Dasgupta and P. Kumar (2020) Citizen science and invasive alien species: An analysis of citizen science initiatives using information and communications technology (ICT) to collect invasive alien species observations. Glob Ecol Conserv 21: e00812. Müllerová, J., J. Brůna, T. Bartaloš, P. Dvořák, M. Vítková and P. Pyšek (2017) Timing is important: Unmanned aircraft vs. satellite imagery in plant invasion monitoring. Front Plant Sci 8: 887. He, K., X. Zhang, S. Ren and J. Sun (2016) Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778. Dash, J.P., M.S. Watt, T.S.H. Paul, J. Morgenroth and R. Hartley (2019) Taking a closer look at invasive alien plant research: A review of the current state, opportunities, and future directions for UAVs. Methods Ecol Evol 10: 2020–2033. Gilpin, A.M., A.J. Denham and D.J. Ayre (2017) The use of digital video recorders in pollination biology. Ecol Entomol 42: 383–388. Jarnevich, C.S., T.J. Stohlgren, D. Barnett and J. Kartesz (2006) Filling in the gaps: Modelling native species richness and invasions using spatially incomplete data. Divers Distrib 12: 511–520. Pärtel, J., M. Pärtel and J. Wäldchen (2021) Plant image identification application demonstrates high accuracy in Northern Europe. AoB Plants 13: plab050. 22 23 24 25 26 27 28 29 M. Seeland (38) 2019; 20 M. Rzanny (36) 2019; 15 31 10 32 11 33 12 34 13 35 14 15 37 16 17 39 18 H.G. Jones (19) 2020; 12 J. Pärtel (30) 2021; 13 1 2 3 4 5 6 7 8 9 40 41 20 42 21 43 |
| References_xml | – reference: Brown, E.D. and B.K. Williams (2019) The potential for citizen science to produce reliable and useful information in ecology. Conserv Biol 33: 561–569. – reference: Ruckli, R., H.P. Rusterholz and B. Baur (2013) Invasion of Impatiens glandulifera affects terrestrial gastropods by altering microclimate. Acta Oecol (Montrouge) 47: 16–23. – reference: Singh, A.K., B. Ganapathysubramanian, S. Sarkar and A. Singh (2018) Deep learning for plant stress phenotyping: Trends and future perspectives. Trends Plant Sci 23: 883–898. – reference: Tan, M. and Q. Le (2019) Efficientnet: Rethinking model scaling for convolutional neural networks. Proc Int Conf Mach Learn 97: 6105–6114. – reference: Prach, K. and L.R. Walker (2011) Four opportunities for studies of ecological succession. Trends Ecol Evol 26: 119–123. – reference: Watanabe, S., K. Sumi and T. Ise (2020) Identifying the vegetation type in Google Earth images using a convolutional neural network: A case study for Japanese bamboo forests. BMC Ecol 20: 1–14. – reference: Crall, A.W., G.J. Newman, T.J. Stohlgren, K.A. Holfelder, J. Graham and D.M. Waller (2011) Assessing citizen science data quality: An invasive species case study. Conserv Lett 4: 433–442. – reference: Agarwal, G., P. Belhumeur, S. Feiner, D. Jacobs, W.J. Kress, R. Ramamoorthi and S. White (2006) First steps toward an electronic field guide for plants. Taxon 55: 597–610. – reference: Pärtel, J., M. Pärtel and J. Wäldchen (2021) Plant image identification application demonstrates high accuracy in Northern Europe. AoB Plants 13: plab050. – reference: Palacios, F., G. Bueno, J. Salido, M.P. Diago, I. Hernández and J. Tardaguila (2020) Automated grapevine flower detection and quantification method based on computer vision and deep learning from on-the-go imaging using a mobile sensing platform under field conditions. Comput Electron Agric 178: 105796. – reference: Carter, G.A., K.L. Lucas, G.A. Blossom, C.L. Lassitter, D.M. Holiday, D.S Mooneyhan and D.R. Fastring, T.R. Holcombe and J.A. Griffith (2009) Remote sensing and mapping of tamarisk along the Colorado river, USA: A comparative use of summer-acquired Hyperion, Thematic Mapper and QuickBird data. Remote Sens (Basel) 1: 318–329. – reference: Rai, P.K. and J.S. Singh (2020) Invasive alien plant species: Their impact on environment, ecosystem services and human health. Ecol Indic 111: 106020. – reference: Dash, J.P., M.S. Watt, T.S.H. Paul, J. Morgenroth and R. Hartley (2019) Taking a closer look at invasive alien plant research: A review of the current state, opportunities, and future directions for UAVs. Methods Ecol Evol 10: 2020–2033. – reference: Gilpin, A.M., A.J. Denham and D.J. Ayre (2017) The use of digital video recorders in pollination biology. Ecol Entomol 42: 383–388. – reference: Jarnevich, C.S., T.J. Stohlgren, D. Barnett and J. Kartesz (2006) Filling in the gaps: Modelling native species richness and invasions using spatially incomplete data. Divers Distrib 12: 511–520. – reference: Allison, S.D. and P.M. Vitousek (2004) Rapid nutrient cycling in leaf litter from invasive plants in Hawai’i. Oecologia 141: 612–619. – reference: Schuttler, S.G., A.E. Sorensen, R.C. Jordan, C. Cooper and A. Shwartz (2018) Bridging the nature gap: can citizen science reverse the extinction of experience? Front Ecol Environ 16: 405–411. – reference: James, K. and K. Bradshaw (2020) Detecting plant species in the field with deep learning and drone technology. Methods Ecol Evol 11: 1509–1519. – reference: Kganyago, M., J. Odindi, C. Adjorlolo and P. Mhangara (2018) Evaluating the capability of Landsat 8 OLI and SPOT 6 for discriminating invasive alien species in the African Savanna landscape. Int J Appl Earth Obs Geoinf 67: 10–19. – reference: Miller-Rushing, A., R. Primack and R. Bonney (2012) The history of public participation in ecological research. Front Ecol Environ 10: 285–290. – reference: Leung, B., D.M. Lodge, D. Finnoff, J.F. Shogren, M.A. Lewis and G. Lamberti (2002) An ounce of prevention or a pound of cure: Bioeconomic risk analysis of invasive species. Proc Biol Sci 269: 2407–2413. – reference: Ise, T., M. Minagawa and M. Onishi (2018) Classifying 3 moss species by deep learning, using the “Chopped Picture” method. Open J Ecol 8: 166–173. – reference: Jones, H.G. (2020) What plant is that? Tests of automated image recognition apps for plant identification on plants from the British flora. AoB Plants 12: plaa052. – reference: Pauchard, A. and K. Shea (2006) Integrating the study of non-native plant invasions across spatial scales. Biol Invasions 8: 399–413. – reference: Maistrello, L., P. Dioli, M. Bariselli, G.L. Mazzoli and I. Giacalone-Forini (2016) Citizen science and early detection of invasive species: Phenology of first occurrences of Halyomorpha halys in Southern Europe. Biol Invasions 18: 3109–3116. – reference: Miralles, L., E. Dopico, F. Devlo-Delva and E. Garcia-Vazquez (2016) Controlling populations of invasive pygmy mussel (Xenostrobus securis) through citizen science and environmental DNA. Mar Pollut Bull 110: 127–132. – reference: Qian, W., Y. Huang, Q. Liu, W. Fan, Z. Sun, H. Dong, F. Wan and X. Qiao (2020) UAV and a deep convolutional neural network for monitoring invasive alien plants in the wild. Comput Electron Agric 174: 105519. – reference: Fusco, E.J., J.T. Finn, J.K. Balch, R.C. Nagy and B.A. Bradley (2019) Invasive grasses increase fire occurrence and frequency across US ecoregions. Proc Natl Acad Sci USA 116: 23594–23599. – reference: Müllerová, J., J. Brůna, T. Bartaloš, P. Dvořák, M. Vítková and P. Pyšek (2017) Timing is important: Unmanned aircraft vs. satellite imagery in plant invasion monitoring. Front Plant Sci 8: 887. – reference: Johnson, B.A., A.D. Mader, R. Dasgupta and P. Kumar (2020) Citizen science and invasive alien species: An analysis of citizen science initiatives using information and communications technology (ICT) to collect invasive alien species observations. Glob Ecol Conserv 21: e00812. – reference: Kim, K.C. and L.B. Byrne (2006) Biodiversity loss and the taxonomic bottleneck: Emerging biodiversity science. Ecol Res 21: 794–810. – reference: Seeland, M., M. Rzanny, D. Boho, J. Wäldchen and P. Mäder (2019) Image-based classification of plant genus and family for trained and untrained plant species. BMC Bioinformatics 20: 1–13. – reference: Silvertown, J. (2009) A new dawn for citizen science. Trends Ecol Evol 24: 467–471. – reference: Dickinson, J.L., B. Zuckerberg and D.N. Bonter (2010) Citizen science as an ecological research tool: Challenges and benefits. Annu Rev Ecol Evol Syst 41: 149–172. – reference: Cohen, J. (1960) A coefficient of agreement for nominal scales. Educ Psychol Meas 20: 37–46. – reference: Manfreda, S., M.F. McCabe, P.E. Miller, R. Lucas, V. Pajuelo Madrigal, G. Mallinis, E.B. Dor, D. Helman, L. Estes, G. Ciraolo et al. (2018) On the use of unmanned aerial systems for environmental monitoring. Remote Sens (Basel) 10: 641. – reference: Rzanny, M., P. Mäder, A. Deggelmann, M. Chen and J. Wäldchen (2019) Flowers, leaves or both? How to obtain suitable images for automated plant identification. Plant Methods 15: 1–11. – reference: Claassens, L. and A.N. Hodgson (2018) Gaining insights into in situ behaviour of an endangered seahorse using action cameras. J Zool 304: 98–108. – reference: Crall, A.W., C.S. Jarnevich, N.E. Young, B.J. Panke, M. Renz and T.J. Stohlgren (2015) Citizen science contributes to our knowledge of invasive plant species distributions. Biol Invasions 17: 2415–2427. – reference: He, K., X. Zhang, S. Ren and J. Sun (2016) Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778. – reference: Underwood, E., S. Ustin and D. DiPietro (2003) Mapping non-native plants using hyperspectral imagery. Remote Sens Environ 86: 150–161. – reference: Hulme, P.E. (2006) Beyond control: Wider implications for the management of biological invasions. J Appl Ecol 43: 835–847. – reference: Levine, J.M., M. Vila, C.M.D. Antonio, J.S. Dukes, K. Grigulis and S. Lavorel (2003) Mechanisms underlying the impacts of exotic plant invasions. Proc Biol Sci 270: 775–781. – ident: 17 doi: 10.1111/j.1366-9516.2006.00278.x – ident: 25 doi: 10.3390/rs10040641 – volume: 13 start-page: plab050 issn: 2041-2851 year: 2021 ident: 30 publication-title: AoB Plants doi: 10.1093/aobpla/plab050 – ident: 42 doi: 10.1016/S0034-4257(03)00096-8 – ident: 24 doi: 10.1007/s10530-016-1217-z – ident: 28 doi: 10.3389/fpls.2017.00887 – ident: 4 doi: 10.3390/rs1030318 – ident: 21 doi: 10.1007/s11284-006-0035-7 – ident: 11 doi: 10.1073/pnas.1908253116 – ident: 32 doi: 10.1016/j.tree.2010.12.007 – ident: 6 doi: 10.1177/001316446002000104 – volume: 20 start-page: 1 issn: 1471-2105 year: 2019 ident: 38 publication-title: BMC Bioinformatics doi: 10.1186/s12859-018-2474-x – ident: 1 doi: 10.2307/25065637 – volume: 12 start-page: plaa052 issn: 2041-2851 year: 2020 ident: 19 publication-title: AoB Plants doi: 10.1093/aobpla/plaa052 – ident: 33 doi: 10.1016/j.compag.2020.105519 – ident: 23 doi: 10.1098/rspb.2003.2327 – ident: 34 doi: 10.1016/j.ecolind.2019.106020 – ident: 2 doi: 10.1007/s00442-004-1679-z – ident: 31 doi: 10.1007/s10530-005-6419-8 – ident: 16 doi: 10.1111/2041-210X.13473 – ident: 9 doi: 10.1111/2041-210X.13296 – ident: 20 doi: 10.1016/j.jag.2017.12.008 – ident: 22 doi: 10.1098/rspb.2002.2179 – ident: 43 doi: 10.1186/s12898-020-00331-5 – ident: 13 doi: 10.1109/CVPR.2016.90 – ident: 41 – ident: 15 doi: 10.4236/oje.2018.83011 – ident: 29 doi: 10.1016/j.compag.2020.105796 – ident: 12 doi: 10.1111/een.12394 – ident: 37 doi: 10.1002/fee.1826 – ident: 27 doi: 10.1016/j.marpolbul.2016.06.072 – ident: 26 doi: 10.1890/110278 – ident: 8 doi: 10.1007/s10530-015-0885-4 – ident: 3 doi: 10.1111/cobi.13223 – ident: 10 doi: 10.1146/annurev-ecolsys-102209-144636 – ident: 35 doi: 10.1016/j.actao.2012.10.011 – volume: 15 start-page: 1 issn: 1746-4811 year: 2019 ident: 36 publication-title: Plant Methods doi: 10.1186/s13007-019-0462-4 – ident: 5 doi: 10.1111/jzo.12509 – ident: 18 doi: 10.1016/j.gecco.2019.e00812 – ident: 39 doi: 10.1016/j.tree.2009.03.017 – ident: 14 doi: 10.1111/j.1365-2664.2006.01227.x – ident: 7 doi: 10.1111/j.1755-263X.2011.00196.x – ident: 40 doi: 10.1016/j.tplants.2018.07.004 |
| SSID | ssj0025045 |
| Score | 2.31404 |
| Snippet | Monitoring and detection of invasive alien plant species are necessary for effective management and control measures. Although efforts have been made to detect... |
| SourceID | pubmedcentral proquest crossref jstage |
| SourceType | Open Access Repository Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 96 |
| SubjectTerms | action camera alien plant Cameras chopped picture method citizen science computer vision Deep learning Flowers & plants Indigenous plants Introduced plants Invasive plants Invasive species Plant species Research Paper Satellite imagery Solidago altissima |
| Title | Automatic detection of alien plant species in action camera images using the chopped picture method and the potential of citizen science |
| URI | https://www.jstage.jst.go.jp/article/jsbbs/72/1/72_21062/_article/-char/en https://www.proquest.com/docview/2646155342 https://www.proquest.com/docview/2709019076 https://pubmed.ncbi.nlm.nih.gov/PMC8987844 |
| Volume | 72 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| ispartofPNX | Breeding Science, 2022, Vol.72(1), pp.96-106 |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3fi9QwEB7OU-R8EH_i6nlEuCehZ7dNk1YQWcTzEM4nF_atJN1kr8ea1t0uqH-Bf7YzabZYuSdf9iXTbZjpdL7pZL4BOM2EEugINDQj5xGXJo9UvCwiI2xFEZELT6R9-UVczPnnRbY4gP0wzqDA7Y2pHc2Tmm_WZz--_3yPDv_OcyPI-M31VuvtGeYu9C6-jSFJ0iiDSz6UE4imK_OZF-cR5u1xaNT75-IjuJsKKhwWfBSl7lwjUFuZEQYdn6D8KySdP4D7AUuyWW_8h3Bg3CO4N1ttAp-GeQy_Z7uu8aysbGk6f-zKscYyRN_GsXaNemXUbIn5Mqsd67scWKXoUxWrv-F-toyOxq8YAkWG2mpbs2Rt7QsPrB8_zZRb-uW26ejsEW4J71DVXf0L7xFC7BOYn3_8-uEiCsMXokrkcRdlXMXKJLowPEstR9hDSKQSwqYpQoAq0VojOKLopvNEGo7atCrWVoisssqmT-HQNc48A6aKKdpEWonZCxcmVxXCCpUi8LSZ1FZP4PVe2WUVmMlpQMa6pAwFjVR6I5XeSBM4HYTbnpDjZrG3vdUGoeCJQUgm5ZR-vPCwRp1u-LqYwPHe0uX-iSwROVINN-X436-GZXRGqrAoZ5odysi4oOZ8KSYgR0_IsA2i8x6vuPrK03rnRS5zzp__95Uv4Cih5gz_gegYDrvNzrxEyNTpE7j1aTE98U7xBy8KG_s |
| linkProvider | Scholars Portal |
| 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=Automatic+detection+of+alien+plant+species+in+action+camera+images+using+the+chopped+picture+method+and+the+potential+of+citizen+science&rft.jtitle=Breeding+science&rft.au=Takaya%2C+Kosuke&rft.au=Sasaki%2C+Yu&rft.au=Ise%2C+Takeshi&rft.date=2022-01-01&rft.pub=Japanese+Society+of+Breeding&rft.issn=1344-7610&rft.eissn=1347-3735&rft.volume=72&rft.issue=1&rft.spage=96&rft.epage=106&rft_id=info:doi/10.1270%2Fjsbbs.21062&rft_id=info%3Apmid%2F36045894&rft.externalDocID=PMC8987844 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1344-7610&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1344-7610&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1344-7610&client=summon |