Shi Wei-ya
School of Information Science and Technology, Henan University of Technology, Zhengzhou, 450001, China
Jiao Keke
School of Information Science and Technology, Henan University of Technology, Zhengzhou, 450001, China
Liang Yitao
School of Information Science and Technology, Henan University of Technology, Zhengzhou, 450001, China
Wang Feng
School of Information Science and Technology, Henan University of Technology, Zhengzhou, 450001, China
Zhang Dexian
School of Information Science and Technology, Henan University of Technology, Zhengzhou, 450001, China
ABSTRACT
Based on biophoton analytical technology, the ultraweak photons emitted from the normal and insects-contaminated wheat are measured separately. Nine parameters of wheat self-illuminating characteristic are used as wheat feature vector. The study proposed using the PSO-BP algorithm (Particle Swarm optimization-BP neural network) as the classification algorithm. The algorithm is trained to distinguish the normal and insects-contaminated wheat. The experimental results show that the recognition precision can reach to 90%. The model can provide a new thought for the detecting the wheat pests.
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How to cite this article
Shi Wei-ya, Jiao Keke, Liang Yitao, Wang Feng and Zhang Dexian, 2013. Detection of Insects-contaminated Wheat Based on PSO-BP Network. Information Technology Journal, 12: 3596-3599.
DOI: 10.3923/itj.2013.3596.3599
URL: https://scialert.net/abstract/?doi=itj.2013.3596.3599
DOI: 10.3923/itj.2013.3596.3599
URL: https://scialert.net/abstract/?doi=itj.2013.3596.3599
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