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Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
Because sugarcane average unit yield was affected by multiple factors in its growth and its inherent law was lack of external correlation data mining, the precise of the prediction method was low. Recently, the adaptive of modern intelligent genetic neural network algorithm for multi-factor effect has been strong, and the prediction accuracy has been high, but with which in sugarcane average unit yield prediction the researches are few. In this paper, based on the characteristics of external factor variable, the input multiple factors of sugar varieties, weather, etc. are optimized by multiple regression model, and the weight and threshold and the network structure of neural network model are optimized by the genetic model of SGA/IGA, etc., which improves the adaptive fitness of genetic BP neural network model. Moreover, an example comparison of this algorithm and the gray linear system, S-BP, SGA-BP, IGA-BP on sugarcane average unit yield prediction is made. The results show that the integrated prediction accuracy and effectiveness of the improved genetic BP (IGA-BP) algorithm model on sugarcane average unit yield is optimal. This research provides a means of accurate prediction for sugarcane market price in significant fluctuations.