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

被引：4

作者：

Yang, Meng-Meng ^{[1
]}

Phillips, Preetha ^{[2
,3
]}

Wang, Shuihua ^{[1
,4
]}

Zhang, Yudong ^{[1
,5
]}

机构：

[1] Nanjing Normal Univ, Sch Comp Sci & Technol, Nanjing 210023, Jiangsu, Peoples R China

[2] Shepherd Univ, Sch Nat Sci & Math, Shepherdstown, WV 25443 USA

[3] West Virginia Sch Osteopath Med, 400 N Lee St, Lewisburg, WV 24901 USA

[4] CUNY City Coll, Dept Elect Engn, New York, NY 10031 USA

[5] Jiangsu Key Lab Adv Mfg Technol, Huaiyin 223003, Jiangsu, Peoples R China

来源：

INTELLIGENT ROBOTICS AND APPLICATIONS, ICIRA 2017, PT III | 2017年 / 10464卷

关键词：

Feature extraction; Classification; Back-Propagation; K-fold crossvalidation; Pattern recognition; DECISION TREE; TRANSFORM; EXTRACTION; MACHINE; IMAGES; MRI;

D O I：

10.1007/978-3-319-65298-6_34

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：367 / 376

页数：10

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