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Multi-organ plant identification

Authors:

Jean-François Molino,

Daniel Barthelemy,

Nozha BoujemaaAuthors Info & Claims

MAED '12: Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data

Pages 41 - 44

https://doi.org/10.1145/2390832.2390843

Published: 02 November 2012 Publication History

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Abstract

This paper presents a new interactive web application for the visual identification of plants based on collaborative pictures. Contrary to previous content-based identification methods and systems developed for plants that mainly relied on leaves, or in few other cases on flowers, it makes use of five different organs and plant's views including habit, flowers, fruits, leaves and bark. Thanks to an interactive and visual query widget, the tagging process of the different organs and views is as simple as drag-and-drop operations and does not require any expertise in botany. All training pictures used by the system were continuously collected during one year through a crowdsourcing application that was set up in the scope of a citizen sciences initiative. System-oriented and human-centered evaluations of the application show that the results are already satisfactory and therefore very promising in the long term to identify a richer flora.

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Cited By

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Greeff MCaspers MKalkman VWillemse LSunderland BBánki OHogeweg L(2022)Sharing taxonomic expertise between natural history collections using image recognitionResearch Ideas and Outcomes10.3897/rio.8.e791878Online publication date: 1-Mar-2022
https://doi.org/10.3897/rio.8.e79187
Heidary-Sharifabad AZarchi MEmadi SZarei G(2021) Efficient Deep Learning Models for Categorizing Chenopodiaceae in the Wild International Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142152015735:10(2152015)Online publication date: 14-May-2021
https://doi.org/10.1142/S0218001421520157
Sharma GAdhikari S(2020)Crowdsourcing for the Identification and Conservation of the Floral Diversity of Nepal: A Technological PerspectiveJournal of Applied Science, Engineering, Technology, and Education10.35877/454RI.asci12651:2(119-123)Online publication date: 8-Jun-2020
https://doi.org/10.35877/454RI.asci1265
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Index Terms

Multi-organ plant identification
1. Human-centered computing
  1. Human computer interaction (HCI)

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Published In

MAED '12: Proceedings of the 1st ACM international workshop on Multimedia analysis for ecological data

November 2012

68 pages

ISBN:9781450315883

DOI:10.1145/2390832

General Chairs:
Concetto Spampinato
University of Catania, Italy
,
Vasileios Mezaris
CERTH-ITI, Greece
,
Jacco van Ossenbruggen
CWI, The Netherlands

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Association for Computing Machinery

New York, NY, United States

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Published: 02 November 2012

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MM '12

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SIGMM

MM '12: ACM Multimedia Conference

November 2, 2012

Nara, Japan

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Overall Acceptance Rate 13 of 23 submissions, 57%

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Greeff MCaspers MKalkman VWillemse LSunderland BBánki OHogeweg L(2022)Sharing taxonomic expertise between natural history collections using image recognitionResearch Ideas and Outcomes10.3897/rio.8.e791878Online publication date: 1-Mar-2022
https://doi.org/10.3897/rio.8.e79187
Heidary-Sharifabad AZarchi MEmadi SZarei G(2021) Efficient Deep Learning Models for Categorizing Chenopodiaceae in the Wild International Journal of Pattern Recognition and Artificial Intelligence10.1142/S021800142152015735:10(2152015)Online publication date: 14-May-2021
https://doi.org/10.1142/S0218001421520157
Sharma GAdhikari S(2020)Crowdsourcing for the Identification and Conservation of the Floral Diversity of Nepal: A Technological PerspectiveJournal of Applied Science, Engineering, Technology, and Education10.35877/454RI.asci12651:2(119-123)Online publication date: 8-Jun-2020
https://doi.org/10.35877/454RI.asci1265
Xing DYang JJin JLuo X(2020)Potential of plant identification apps in urban forestry studies in China: comparison of recognition accuracy and user experience of five appsJournal of Forestry Research10.1007/s11676-020-01234-332:5(1889-1897)Online publication date: 15-Oct-2020
https://doi.org/10.1007/s11676-020-01234-3
Zhu HLiu QQi YHuang XJiang FZhang S(2018)Plant identification based on very deep convolutional neural networksMultimedia Tools and Applications10.5555/3288251.328829877:22(29779-29797)Online publication date: 1-Nov-2018
https://dl.acm.org/doi/10.5555/3288251.3288298
Bonnet PBotella CMunoz FMonestiez PChouet MGoëau HJoly A(2018)Automated Identification of Citizen Science Observations for Ecological StudiesBiodiversity Information Science and Standards10.3897/biss.2.254502(e25450)Online publication date: 17-May-2018
https://doi.org/10.3897/biss.2.25450
Zhang SZhang CHuang W(2018)Integrating leaf and flower by local discriminant CCA for plant species recognitionComputers and Electronics in Agriculture10.1016/j.compag.2018.10.018155(150-156)Online publication date: Dec-2018
https://doi.org/10.1016/j.compag.2018.10.018
Do TNguyen HNguyen TVu HTran TLe T(2017)Plant identification using score-based fusion of multi-organ images2017 9th International Conference on Knowledge and Systems Engineering (KSE)10.1109/KSE.2017.8119457(191-196)Online publication date: Oct-2017
https://doi.org/10.1109/KSE.2017.8119457
Zhu HHuang XZhang SYuen P(2017)Plant identification via multipath sparse codingMultimedia Tools and Applications10.1007/s11042-016-3538-476:3(4599-4615)Online publication date: 1-Feb-2017
https://dl.acm.org/doi/10.1007/s11042-016-3538-4
Joly AGoëau HChamp JDufour-Kowalski SMüller HBonnet PHanjalic ASnoek CWorring MBulterman DHuet BKelliher AKompatsiaris YLi J(2016)Crowdsourcing Biodiversity MonitoringProceedings of the 24th ACM international conference on Multimedia10.1145/2964284.2976762(958-967)Online publication date: 1-Oct-2016
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Pl@ntNet Mobile 2014: Android port and new features

Pl@ntNet mobile app

The imageCLEF plant identification task 2013

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