Detection Level of Apple Based on BP Neural Network

DOI

10.2991/icismme-15.2015.345How to use a DOI?

Keywords

characteristic parameter; BP neural network; apple; detect level.

Abstract

In view of the draw backs of apple grade identification in China, which still relies on photoelectric sorting and manual separation, this paper presents a processing method on the basis of the technology of computer vision and digital image.Utilizing image processing technology, the researcher calculated the length of the long-short-axis, marked the location of it and calculated the 4 parameters, color, mean square,shape,size, as the key characteristics of the BP input of network to build a network and identify the level of apple through analysis of the external characteristics of apple. The optimum structure parameters of the BP neural network which had 9 hidden layer neurons were determined by RP training algorithm Results showed that average accuracy for fruit classification can reach 92.5% by using this model and the executing time of microcomputer for grading of one apple is 9.3 ms This method has the characteristics of high accuracy and good real-time performance

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Xiaoling Li
AU  - Jimin Yuan
PY  - 2015/07
DA  - 2015/07
TI  - Detection Level of Apple Based on BP Neural Network
BT  - Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy
PB  - Atlantis Press
SP  - 1667
EP  - 1671
SN  - 1951-6851
UR  - https://doi.org/10.2991/icismme-15.2015.345
DO  - 10.2991/icismme-15.2015.345
ID  - Li2015/07
ER  -