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Murviel-lès-Montpellier, France

Joly A.,French Institute for Research in Computer Science and Automation | Goeau H.,French Institute for Research in Computer Science and Automation | Bonnet P.,CIRAD - Agricultural Research for Development | Bakic V.,French Institute for Research in Computer Science and Automation | And 8 more authors.
Ecological Informatics | Year: 2014

Speeding up the collection and integration of raw botanical observation data is a crucial step towards a sustainable development of agriculture and the conservation of biodiversity. Initiated in the context of a citizen sciences project, the main contribution of this paper is an innovative collaborative workflow focused on image-based plant identification as a mean to enlist new contributors and facilitate access to botanical data. Since 2010, hundreds of thousands of geo-tagged and dated plant photographs were collected and revised by hundreds of novice, amateur and expert botanists of a specialized social network. An image-based identification tool - available as both a web and a mobile application - is synchronized with that growing data and allows any user to query or enrich the system with new observations. An important originality is that it works with up to five different organs contrarily to previous approaches that mainly relied on the leaf. This allows querying the system at any period of the year and with complementary images composing a plant observation. Extensive experiments of the visual search engine as well as system-oriented and user-oriented evaluations of the application show that it is already very helpful to determine a plant among hundreds or thousands of species. At the time of writing, the whole framework covers about half of the plant species living in France (2200 species), which already makes it the widest existing automated identification tool (with its imperfections). © 2013 Elsevier B.V. Source


Joly A.,French Institute for Research in Computer Science and Automation | Goeau H.,French Institute for Research in Computer Science and Automation | Vignau C.,Tela Botanica | Barthelemy D.,French National Institute for Agricultural Research | Boujemaa N.,Direction of Saclay Center
Multimedia Tools and Applications | Year: 2015

This paper reports a large-scale experiment aimed at evaluating how state-of-art computer vision systems perform in identifying plants compared to human expertise. A subset of the evaluation dataset used within LifeCLEF 2014 plant identification challenge was therefore shared with volunteers of diverse expertise, ranging from the leading experts of the targeted flora to inexperienced test subjects. In total, 16 human runs were collected and evaluated comparatively to the 27 machine-based runs of LifeCLEF challenge. One of the main outcomes of the experiment is that machines are still far from outperforming the best expert botanists at the image-based plant identification competition. On the other side, the best machine runs are competing with experienced botanists and clearly outperform beginners and inexperienced test subjects. This shows that the performances of automated plant identification systems are very promising and may open the door to a new generation of ecological surveillance systems. © 2015 Springer Science+Business Media New York Source


Bonnet P.,CIRAD - Agricultural Research for Development | Joly A.,LIRMM | Joly A.,French Institute for Research in Computer Science and Automation | Goeau H.,French Institute for Research in Computer Science and Automation | And 6 more authors.
Multimedia Tools and Applications | Year: 2016

This paper reports a large-scale experiment aimed at evaluating how state-of-art computer vision systems perform in identifying plants compared to human expertise. A subset of the evaluation dataset used within LifeCLEF 2014 plant identification challenge was therefore shared with volunteers of diverse expertise, ranging from the leading experts of the targeted flora to inexperienced test subjects. In total, 16 human runs were collected and evaluated comparatively to the 27 machine-based runs of LifeCLEF challenge. One of the main outcomes of the experiment is that machines are still far from outperforming the best expert botanists at the image-based plant identification competition. On the other side, the best machine runs are competing with experienced botanists and clearly outperform beginners and inexperienced test subjects. This shows that the performances of automated plant identification systems are very promising and may open the door to a new generation of ecological surveillance systems. © 2015, Springer Science+Business Media New York. Source


Joly A.,French Institute for Research in Computer Science and Automation | Goeau H.,French Institute for Research in Computer Science and Automation | Barbe J.,French National Institute for Agricultural Research | Selmi S.,French Institute for Research in Computer Science and Automation | And 4 more authors.
Multimedia Systems | Year: 2015

Pl@ntNet is an innovative participatory sensing platform relying on image-based plants identification as a mean to enlist non-expert contributors and facilitate the production of botanical observation data. One year after the public launch of the mobile application, we carry out a self-critical evaluation of the experience with regard to the requirements of a sustainable and effective ecological surveillance tool. We first demonstrate the attractiveness of the developed multimedia system (with more than 90K end-users) and the nice self-improving capacities of the whole collaborative workflow. We then point out the current limitations of the approach towards producing timely and accurate distribution maps of plants at a very large scale. We discuss in particular two main issues: the bias and the incompleteness of the produced data. We finally open new perspectives and describe upcoming realizations towards bridging these gaps. © 2015 Springer-Verlag Berlin Heidelberg Source

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