Skip to Main content Skip to Navigation
New interface
Conference papers

Automated Plant Species Identification: Challenges and Opportunities

Abstract : The number of species of macro organisms on the planet is estimated at about 10 million. This staggering diversity and the need to better understand it led inevitably to the development of classification schemes called biological taxonomies. Unfortunately, in addition to this enormous diversity, the traditional identification and classification workflows are both slow and error-prone; classification expertise is in the hands of a small number of expert taxonomists; and to make things worse, the number of taxonomists has steadily declined in recent years. Automated identification of organisms has therefore become not just a long time desire but a need to better understand, use, and save biodiversity. This paper presents a survey of recent efforts to use computer vision and machine learning techniques to identify organisms. It focuses on the use of leaf images to identify plant species. In addition, it presents the main technical and scientific challenges as well as the opportunities for herbaria and cybertaxonomists to take a quantum leap towards identifying biodiversity efficiently and empowering the general public by putting in their hands automated identification tools.
Document type :
Conference papers
Complete list of metadata

Cited literature [30 references]  Display  Hide  Download
Contributor : Hal Ifip Connect in order to contact the contributor
Submitted on : Monday, January 9, 2017 - 10:24:16 AM
Last modification on : Monday, January 9, 2017 - 1:05:36 PM
Long-term archiving on: : Monday, April 10, 2017 - 1:12:50 PM


Files produced by the author(s)


Distributed under a Creative Commons Attribution 4.0 International License



Erick Mata-Montero, Jose Carranza-Rojas. Automated Plant Species Identification: Challenges and Opportunities. 6th IFIP World Information Technology Forum (WITFOR), Sep 2016, San José, Costa Rica. pp.26-36, ⟨10.1007/978-3-319-44447-5_3⟩. ⟨hal-01429753⟩



Record views


Files downloads