This paper presents several improvements of [email protected], an image sharing and retrieval application for identifying plants [6]: (i) ported to most android platforms (ii) three times more data (iii) exploiting metadata as well as visual content in the identification process (iv) a new multi-plant-organ, multi-image and multi-feature merging strategy with separate indexes for each visual feature (v) integrating cross-languages functions. This paper also presents the new results achieved by our system in the ImageCLEF 2013 plant identification task and in real-world user trials.

This paper presents an Android application for plant identification. The system relies on the observation of leaf images. Unlike other mobile plant identification applications, the user may choose the leaf characters that will guide the identification process. For this purpose, two kinds of descriptors are proposed to the user: a shape descriptor based on a multiscale triangular representation of the leaf margin and a descriptor of the salient points of the leaf. The application achieves good identification accuracy and provides Android users a useful system for plant identification.

[email protected] is an image sharing and retrieval application for the identification of plants, available on iPhone and iPad devices. Contrary to previous content-based identification applications it can work with several parts of the plant including flowers, leaves, fruits and bark. It also allows integrating user's observations in the database thanks to a collaborative workflow involving the members of a social network specialized on plants. Data collected so far makes it one of the largest mobile plant identification tool.

This demo presents a crowdsourcing web application dedicated to the access of botanical knowledge through automated identification of plant species by visual content. Inspired by citizen sciences, our aim is to speed up the collection and integration of raw botanical observation data, while providing to potential users an easy and efficient access to this botanical knowledge. The result presented during the demo is an enjoying application where anyone can play to shoot fresh cut leaves and observe the relevance of species suggested in spite of various visual difficult queries.