Automated plant species identification-Trends and future directions

Abstract

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.

Fig 1. Fundamental steps of supervised machine learning for image-based species identification.

Fig 2. Botanists' (left) versus computer vision (right) description of flowers.

Fig 3

Visual variation of Lapsana communis's flower throughout the day from two perspectives (left) and visual variation of Centaurea pseudophrygia's flower throughout the season and flowering stage (right).