Plant Identification: Experts vs. Machines in the Era of Deep Learning | Plant Identification: Experts vs. Machines in the Era of Deep Learning: Deep learning techniques challenge flora experts
2018
Bonnet, Pierre | Goëau, Hervé | Hang, Siang, Thye | Lasseck, Mario | Sulc, Milaň | Malécot, Valéry | Jauzein, Philippe | Melet, Jean-Claude | You, Christian | Joly, Alexis | Botanique et Modélisation de l'Architecture des Plantes et des Végétations (UMR AMAP) ; Centre de Coopération Internationale en Recherche Agronomique pour le Développement (Cirad)-Institut National de la Recherche Agronomique (INRA)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [Occitanie]) | Scientific Data Management (ZENITH) ; Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier (LIRMM) ; Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Centre Inria d'Université Côte d'Azur ; Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria) | Toyohashi University of Technology (TUT) | Museum für Naturkunde [Berlin] | Czech Technical University in Prague (CTU) | Génétique et Horticulture (GenHort) ; Institut National de la Recherche Agronomique (INRA)-AGROCAMPUS OUEST | AgroParisTech | Société Botanique du Centre-Ouest (SBCO)
International audience
显示更多 [+] 显示较少 [-]英语. Automated identification of plants and animals have improved considerably in the last few years, in particular thanks to the recent advances in deep learning. The next big question is how far such automated systems are from the human expertise. Indeed, even the best experts are sometimes confused and/or disagree between each others when validating visual or audio observations of living organism. A picture or a sound actually contains only a partial information that is usually not sufficient to determine the right species with certainty. Quantifying this uncertainty and comparing it to the performance of automated systems is of high interest for both computer scientists and expert naturalists. This chapter reports an experimental study following this idea in the plant domain. In total, nine deep-learning systems implemented by three different research teams were evaluated with regard to nine expert botanists of the French flora. Therefore, we created a small set of plant observations that were identified in the field and revised by experts in order to have a near-perfect golden standard. The main outcome of this work is that the performance of state-of-the-art deep learning models is now close to the most advanced human expertise. This shows that automated plant identification systems are now mature enough for several routine tasks, and can offer very promising tools for autonomous ecological surveillance systems.
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