DocumentCode :
2395267
Title :
Study on Defect Extraction of Pears with Rich Spots and Neural Network Grading Method
Author :
Zhongzhi Han ; Jing Liu ; Yougang Zhao ; Yanzhao, Li
Author_Institution :
Coll. of Inf. Sci., Qingdao Agric. Univ., Qingdao, China
fYear :
2012
fDate :
19-20 May 2012
Firstpage :
1989
Lastpage :
1993
Abstract :
In order to study the feasibility of the computerized grading system of pears with rich spots, large amount of pears are put orderly in a light box, where images of different aspects of them are captured by cameras which are connected to the computer. Additionally, two methods are proposed to remove the spots on the surface of pears in the image to reduce their effect on defect detection. And advantages and disadvantages of the two methods are discussed. One method works by applying adaptive threshold and the other works by combining filtering with edge detection. In reference to the National Standard of China, grading based on shape and defect is studied respectively. ANN (Artificial Neural Network) model which colligates the information of shape, color and defect is established to grade pears comprehensively. The method of adaptive threshold has better effect when it is used in processing pears with rich spots, such as Laiyang pear, whereas its executive efficiency is slightly lower. Yet to the species the spots of which are not so conspicuous, such as Huangjin Pear and Fengshui Pear etc., the method which combines filtering with edge detection is supposed to be applied for better performance. The results of the grading by ANN Model based on shape, defect and comprehensive quality respectively reach the accuracy of 87.5%, 92.6% and 90.3%.The methods proposed to remove spots and the model constructed for grading and recognition have positive significance to grading pears with rich spots by appearance.
Keywords :
agricultural products; cameras; edge detection; feature extraction; filtering theory; image colour analysis; neural nets; National Standard of China; adaptive threshold; artificial neural network model; color information; computerized grading system; defect detection; defect information; defect-based grading; edge detection; neural network grading method; pears defect extraction; pears surface; rich spots defect extraction; shape information; shape-based ANN Model; shape-based grading; Computers; Feature extraction; Image color analysis; Image edge detection; Neural networks; Shape; Standards; Defect extraction; Dynamic threshold; Neural network; Pears; Quality grading;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Systems and Informatics (ICSAI), 2012 International Conference on
Conference_Location :
Yantai
Print_ISBN :
978-1-4673-0198-5
Type :
conf
DOI :
10.1109/ICSAI.2012.6223440
Filename :
6223440
Link To Document :
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