Plant Identification: Experts vs. Machines in the Era of Deep Learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F18%3A00322132" target="_blank" >RIV/68407700:21230/18:00322132 - isvavai.cz</a>

Result on the web

<a href="https://link.springer.com/chapter/10.1007/978-3-319-76445-0_8" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-319-76445-0_8</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-76445-0_8" target="_blank" >10.1007/978-3-319-76445-0_8</a>

Result language

angličtina

Original language name

Plant Identification: Experts vs. Machines in the Era of Deep Learning

Original language description

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.

Czech name

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Czech description

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Type

C - Chapter in a specialist book

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2018

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Book/collection name

Multimedia Tools and Applications for Environmental & Biodiversity Informatics

ISBN

978-3-319-76444-3

Number of pages of the result

19

Pages from-to

131-149

Number of pages of the book

221

Publisher name

Springer, Cham

Place of publication

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UT code for WoS chapter

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