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Erschienen in:
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2017 | OriginalPaper | Buchkapitel

Leaf Recognition for Plant Classification Based on Wavelet Entropy and Back Propagation Neural Network

verfasst von : Meng-Meng Yang, Preetha Phillips, Shuihua Wang, Yudong Zhang

Erschienen in: Intelligent Robotics and Applications

Verlag: Springer International Publishing

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Abstract

In this paper, we proposed a method for plant classification, which aims to recognize the type of leaves from a set of image instances captured from same viewpoints. Firstly, for feature extraction, this paper adopted the 2-level wavelet transform and obtained in total 7 features. Secondly, the leaves were automatically recognized and classified by Back-Propagation neural network (BPNN). Meanwhile, we employed K-fold cross-validation to test the correctness of the algorithm. The accuracy of our method achieves 90.0%. Further, by comparing with other methods, our method arrives at the highest accuracy.

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Vorheriges Kapitel Nonlinear Control of Omnidirectional Mobile Platforms
Nächstes Kapitel A Registration Method for 3D Point Clouds with Convolutional Neural Network
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Metadaten
Titel
Leaf Recognition for Plant Classification Based on Wavelet Entropy and Back Propagation Neural Network
verfasst von
Meng-Meng Yang
Preetha Phillips
Shuihua Wang
Yudong Zhang
Copyright-Jahr
2017
Buch
Intelligent Robotics and Applications

Print ISBN: 978-3-319-65297-9

Electronic ISBN: 978-3-319-65298-6

Copyright-Jahr: 2017

DOI
https://doi.org/10.1007/978-3-319-65298-6_34

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