Crowdsourcing for botanical data collection towards to automatic plant identification: A review

Abstract

Nowadays, a number of crowdsourcing systems are available, with community-driven forums contributing both visual datasets of flora and assisting members in determining species names of a given visual observation. However, crowdsourced problem has not clearly analyzed, particularly, in terms of providing data resources for establishing a powerful vision-based plant identification. In this paper, we carry out a comprehensive survey on various crowdsourcing systems for botanical data collecting. We first analyze six systems with respect of their focus, platforms, advantages as well as drawbacks. We then conduct questionnaire-based evaluations with a number of subjects having different expertise levels in botany. The evaluation results show that (1) the current systems have been accepted by a large number of users and (2) automatic plant identification based on images plays an important role in attracting the use of these systems. However, in order to make these systems be used in worldwide level, several issues still need to address. One of these issues is to improve the automatic plant identification. In order to understand the factors that affects identification performance, we have conducted several experiments with the state-of-the-art method based on deep learning techniques on different datasets. Results from these experiments show the crucial role of crowdsourcing system in collecting visual data for developing robust and effective plant identification.