@article{fdi:010066262,
  title = {{P}lant identification : man vs. machine {L}ife{CLEF} 2014 plant identification challenge},
  author = {{B}onnet, {P}. and {J}oly, {A}. and {G}oeau, {H}. and {C}hamp, {J}. and {V}ignau, {C}. and {M}olino, {J}ean-{F}ran{\c{c}}ois and {B}arth{\'e}lemy, {D}. and {B}oujemaa, {N}.},
  editor = {},
  language = {{ENG}},
  abstract = {{T}his 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 {L}ife{CLEF} 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. {I}n total, 16 human runs were collected and evaluated comparatively to the 27 machine-based runs of {L}ife{CLEF} challenge. {O}ne 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. {O}n the other side, the best machine runs are competing with experienced botanists and clearly outperform beginners and inexperienced test subjects. {T}his 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.},
  keywords = {{V}isual plant identification ; {H}uman evaluation ; {D}igital data ; {I}mage analysis ; {EUROPE}},
  booktitle = {},
  journal = {{M}ultimedia {T}ools and {A}pplications},
  volume = {75},
  numero = {3},
  pages = {1647--1665},
  ISSN = {1380-7501},
  year = {2016},
  DOI = {10.1007/s11042-015-2607-4},
  URL = {https://www.documentation.ird.fr/hor/fdi:010066262},
}