Automated plant species identification—Trends and future directions

By Jana Wäldchen, Michael Rzanny, Marco Seeland, Patrick Mäder. PLoS Comput. Biol., 2018

296 citations recorded by the source corpus.

Current rates of species loss triggered numerous attempts to protect and conserve biodiversity. Species conservation, however, requires species identification skills, a competence obtained through intensive training and experience. Field researchers, land managers, educators, civil servants, and the interested public would greatly benefit from accessible, up-to-date tools automating the process of species identification. Currently, relevant technologies, such as digital cameras, mobile devices, and remote access to databases, are ubiquitously available, accompanied by significant advances in image processing and pattern recognition. The idea of automated species identification is approaching reality. We review the technical status quo on computer vision approaches for plant species identification, highlight the main research challenges to overcome in providing applicable tools, and conclude with a discussion of open and future research thrusts.

Subfields: Ecological Modeling, Molecular Biology, Plant Science

View Paper: https://www.semanticscholar.org/paper/a433b093d6ec72cbdb84bf3863f42e6621670fe4

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In Plant Science

1. Dionysis D. Bochtis
2. Aristotelis C. Tagarakis

Citations in the corpus

• Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “Last but not least, the development of efficient ML techniques by incorporating expert knowledge from different stakeholders should be fostered, particularly regarding computing science, agriculture, and the private sector, as a means of designing realistic solutions [19,22,24,33].”