DocumentCode :
2772540
Title :
Harum Manis Mango weevil infestation classification using backpropagation neural network
Author :
Yacob, Yasmin M. ; Shaiful, A.R.A.M. ; Husin, Z. ; Farook, Rohani S M ; Aziz, A. Rallis A
Author_Institution :
Sch. of Comput. & Commun. Eng., Univ. Malaysia Perlis, Kuala Perlis
fYear :
2008
fDate :
1-3 Dec. 2008
Firstpage :
1
Lastpage :
6
Abstract :
Postharvest non-destructive detection methods in fruit quality have been widely studied ever since. This include studies of maturity, bruises and detection of pests or weevil existence in fruits such as apple, banana, zucchini including mango. Regarding fruit grading, the non-destructive methods which can be used are image processing and dielectric properties. Either technique has its own benefits and drawbacks. As for image processing technique, the cost is high since suitable device to acquire the images are by using MRI or X-ray. Whereas for dielectric method, permittivity is difficult to record because the reading is very small and are prone to environment and temperature influence. This paper analyze about classification of Harum Manis mango infestation using dielectric sensor which was trained and tested using back-propagation neural network. In addition, reviews regarding neural network design is also discussed.
Keywords :
agricultural engineering; backpropagation; dielectric properties; image processing; neural nets; Harum Manis mango weevil infestation classification; MRI; X-ray; backpropagation neural network; dielectric properties; dielectric sensor; image processing technique; permittivity; postharvest nondestructive detection methods; Backpropagation; Costs; Dielectrics; Image processing; Magnetic resonance imaging; Neural networks; Permittivity; Temperature sensors; Testing; X-ray imaging; Neural Network; dielectric sensor; non-destructive detection; weevil;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Electronic Design, 2008. ICED 2008. International Conference on
Conference_Location :
Penang
Print_ISBN :
978-1-4244-2315-6
Electronic_ISBN :
978-1-4244-2315-6
Type :
conf
DOI :
10.1109/ICED.2008.4786780
Filename :
4786780
Link To Document :
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