Bonnet P. auteur aut IRD Joly A. auteur aut IRD Goeau H. auteur aut IRD Champ J. auteur aut IRD Vignau C. auteur aut IRD Molino Jean-François auteur aut IRD Barthélemy D. auteur aut IRD Boujemaa N. auteur aut IRD text journalArticle eng text/pdf born digital access 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. specialized Visual plant identification Human evaluation Digital data Image analysis EUROPE 076 75 3 1647-1665 2016 1380-7501 https://www.documentation.ird.fr/hor/fdi:010066262 10.1007/s11042-015-2607-4 1380-7501 [F B010066262] https://www.documentation.ird.fr/hor/fdi:010066262 https://www.documentation.ird.fr/intranet/publi/2016/03/010066262.pdf Accès réservé (Intranet de l'IRD) IRD - Base Horizon / Pleins textes 2016-04-01 2017-08-23 fdi:010066262 fre