ApLeaf: An efficient android-based plant leaf identification system

To automatically identify plant species is very useful for ecologists, amateur botanists, educators, and so on. The Leafsnap is the first successful mobile application system which tackles this problem. However, the Leafsnap is based on the IOS platform. And to the best of our knowledge, as the mobi...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2015-03, Vol.151, p.1112-1119
Main Authors: Zhao, Zhong-Qiu, Ma, Lin-Hai, Cheung, Yiu-ming, Wu, Xindong, Tang, Yuanyan, Chen, Chun Lung Philip
Format: Article
Language:English
Subjects:
Amateur
Android application
Applications programs
Feature fusion
Image retrieval
Leaves
Mobile communication systems
Pictures
Plant identification
Trees
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Summary:To automatically identify plant species is very useful for ecologists, amateur botanists, educators, and so on. The Leafsnap is the first successful mobile application system which tackles this problem. However, the Leafsnap is based on the IOS platform. And to the best of our knowledge, as the mobile operation system, the Android is more popular than the IOS. In this paper, an Android-based mobile application designed to automatically identify plant species according to the photographs of tree leaves is described. In this application, one leaf image can be either a digital image from one existing leaf image database or a picture collected by a camera. The picture should be a single leaf placed on a light and untextured background without other clutter. The identification process consists of three steps: leaf image segmentation, feature extraction, and species identification. The demo system is evaluated on the ImageCLEF2012 Plant Identification database which contains 126 tree species from the French Mediterranean area. The outputs of the system to users are the top several species which match the query leaf image the best, as well as the textual descriptions and additional images about plant leaves, flowers, etc. Our system works well with state-of-the-art identification performance.
ISSN:0925-2312
1872-8286
DOI:10.1016/j.neucom.2014.02.077