A Simulation and Prediction Method of the Height of Wheat Based on BP Neural Networks and the Ant Colony Algorithm

Hong Yi Li; Xi Xin Wu; Tong Wang; Di Zhao

doi:10.4028/www.scientific.net/AMR.659.113

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 659A Simulation and Prediction Method of the Height...

A Simulation and Prediction Method of the Height of Wheat Based on BP Neural Networks and the Ant Colony Algorithm

Abstract:

This paper focuses on the simulation and prediction problem of height of the crops, particularly the wheat, which plays a significantly important role in its yield, in different growing stages. Our model bases on the BP neural network and the ant colony algorithm. Both of these two algorithms has their own advantages and disadvantages. However, through observations, we find that their advantages and disadvantages seem to be complementary, by which we propose the combination algorithm. This combination algorithm can conquer the local optimum problem of the BP Neural Network, and could overcome the shortcomings of the weak local optimum searching capability of the ant colony algorithm. The experiments show that the our proposed algorithm can hopefully yield good simulation and prediction results of the height of wheat in different growing stages.

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Periodical:

Advanced Materials Research (Volume 659)

Pages:

113-117

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.659.113

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Online since:

January 2013

Authors:

Hong Yi Li, Xi Xin Wu, Tong Wang, Di Zhao

Keywords:

Ant Colony Algorithm (ACA), BP Neural Network (BPNN), Height of Wheat, Prediction, Scientific Management of Planning and Fertilization, Simulation

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