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Computational Intelligence and Neuroscience
Volume 2017, Article ID 7361042, 6 pages
https://doi.org/10.1155/2017/7361042
Research Article

Deep Learning for Plant Identification in Natural Environment

School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China

Correspondence should be addressed to Haiyan Zhang; nc.ude.ufjb@lmzyhz

Received 2 March 2017; Accepted 18 April 2017; Published 22 May 2017

Academic Editor: Sergio Solinas

Copyright © 2017 Yu Sun et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Plant image identification has become an interdisciplinary focus in both botanical taxonomy and computer vision. The first plant image dataset collected by mobile phone in natural scene is presented, which contains 10,000 images of 100 ornamental plant species in Beijing Forestry University campus. A 26-layer deep learning model consisting of 8 residual building blocks is designed for large-scale plant classification in natural environment. The proposed model achieves a recognition rate of 91.78% on the BJFU100 dataset, demonstrating that deep learning is a promising technology for smart forestry.