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Plant Leaves Recognition Combined PCA with AdaBoost.M1

Volume 15, Number 4, April 2019, pp. 1122-1130
DOI: 10.23940/ijpe.19.04.p7.11221130

Hui Chena, Haodong Zhub, and Xufeng Chaic

aEngineering Training Centre, Zhengzhou University of Light Industry, Zhengzhou,450002,China
bSchool of Computer and Communication Engineering, Zhengzhou University of Light Industry, Zhengzhou, 450002, China
cSida Foreign Language Primary School, Henan Experimental High School, Zhengzhou, 450000, China


(Submitted on November 8, 2018; Revised on December 10, 2018; Accepted on January 12, 2019)


In order to improve the overall performance of plant leaves recognition, this paper proposed a novel method combining PCA with AdaBoost.M1to recognize plant leaves. The proposed method firstly carries out the image preprocessing, which includes the image gray processing, the image binarization, and the edge extraction; extracts the 13 features of plant leaf with the characteristics of rotation invariance, proportion invariance, and translation invariance; subsequently employs PCA to reduce the dimensions of these feature parameters; and finally adopts the AdaBoost.M1 classifier to train and recognize the reduced-dimension plant leaf images. Simulation experiment results indicate that the proposed method is able to improve the overall performance effectively of plant leaves recognition.

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