TY - JOUR
T1 - ApLeaf
T2 - An efficient android-based plant leaf identification system
AU - Zhao, Zhong Qiu
AU - Ma, Lin Hai
AU - CHEUNG, Yiu Ming
AU - Wu, Xindong
AU - Tang, Yuanyan
AU - Chen, Chun Lung Philip
N1 - Funding Information:
This research was supported by the National Natural Science Foundation of China (Nos. 61375047 , 61005007 , 61273297 , 61272366 and 61175121 ), the 973 Program of China (No. 2013CB329604 ), the 863 Program of China (No. 2012AA011005 ), the Hong Kong Scholars Program (No. XJ2012012 ), the Program for Changjiang Scholars and Innovative Research Team in University (PCSIRT) of the Ministry of Education, China (No. IRT13059 ), the grant of the National Science Foundation of Fujian Province (No. 2013J06014 ), China Postdoctoral Science Foundation (No. 2013M540510 ), the Faculty Research Grant of Hong Kong Baptist University under Projects FRG2/12-13/082 , and the Project-sponsored by SRF for ROCS, SEM .
PY - 2015/3/3
Y1 - 2015/3/3
N2 - 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.
AB - 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.
KW - Android application
KW - Feature fusion
KW - Image retrieval
KW - Plant identification
UR - http://www.scopus.com/inward/record.url?scp=84918522479&partnerID=8YFLogxK
U2 - 10.1016/j.neucom.2014.02.077
DO - 10.1016/j.neucom.2014.02.077
M3 - Journal article
AN - SCOPUS:84918522479
SN - 0925-2312
VL - 151
SP - 1112
EP - 1119
JO - Neurocomputing
JF - Neurocomputing
IS - P3
ER -