Prediction for Greenhouse Melon Disease Based on Optimized Neural Network

Xiao Hua Sun; Xi Wang Wang; Fu Shun Wang; Yan Zhao

doi:10.4028/www.scientific.net/AMR.765-767.1109

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 765-767Prediction for Greenhouse Melon Disease Based on...

Prediction for Greenhouse Melon Disease Based on Optimized Neural Network

Abstract:

in the greenhouse, the the thick skin melon tends to be more easily infected by some diseases. The traditional forecasting model disease convergence speed is slow and limited by the minimum, easily. This study, based on the BP neural network but to optimize it and introduce the genetic algorithm, through the local searching nearby the global optimal solutions, overcomes the local minimum value and convergence speed defects of traditional neural network with genetic algorithms global search ability. The experimental datas simulative analysis by Matlab shows that the thick skin melon's disease predicting error has been reduced significantly after the introduction of genetic optimization algorithm and has obtained an ideal fitting result.

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Periodical:

Advanced Materials Research (Volumes 765-767)

Pages:

1109-1112

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.765-767.1109

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Online since:

September 2013

Authors:

Xiao Hua Sun, Xi Wang Wang, Fu Shun Wang, Yan Zhao

Keywords:

Disease Forecast, Genetic Algorithm (GA), Greenhouse, Neural Network (NN), Thick Skin Melon

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References

[1] Aegerter B.J., Nunez J.J. Environmental factors affecting rose downy mildew and development of a forecasting model for a nursery production system, American Psychopathological Society, vol. 6, 2010, pp.145-148.

DOI: 10.1094/pdis.2003.87.6.732

Google Scholar

[2] Luo L Q, Ning S. Improved model for scabby wheat rate estimation using Machine vision and neural networks, Annual International Meeting , 2009, ASAE Paper No. 993201.

Google Scholar

[3] Goldberg DE, Genetic algorithms in search, optimization, and machine learning Reading, MA: Addison-Wesley; (1989).

Google Scholar

[4] R. Nithya, B. Santhi. Comparative Study on Feature Extraction Method for Breast Cancer Classification, Journal of Theoretical and Applied Information Technology, Vol 33. No. 2, 2011, p.220 – 226.

Google Scholar

[5] Imas Sukaesih Sitanggang, Razali Yaakob, Norwati Mustapha, Ainuddin A n. Application of Classification Algorithms in Data Minging for Hotspots Occurrence Prediction in Riau Province Indonesia, Journal of Theoretical and Applied Information Technology, Vol. 43. No. 2, 2012, p.214.

Google Scholar

[6] Randy L. Haupt, Sue Ellen Haupt, Practical Genetic Algorithms, A. John WILEY &SONS, INC, Publication, (2004).

Google Scholar

[7] Lankin-Vega G. ，Worner S.P. An ensemble model for predicting Rhopalosiphum padi abundance, Entomologia Experimentalis Applicata, vol. 3, 2008, pp.308-315.

DOI: 10.1111/j.1570-7458.2008.00778.x

Google Scholar