Study on Prediction Model of Grain Yield Based on Principal Component Analysis and BP Neural Network

Abstract:

Taking the grain yield data from 1980 to 2012 of Jilin Province for example, this paper analyzes the main factors that influences the grain yield based on the principle component analysis method. According to these main factors, the input samples of BP neutral network are definite. Thereby, the BP neutral networks could be trained to predict. The results show that the fertilizer consumption, large cattle head number, end grain sowing area, effective irrigation area and rural per capita living space are the main effect factor on grain yield. The BP neural network was built by using it as the input samples. The number of input nodes of the network is determined. Then build the prediction model of grain production in Jilin province. The simulation results show that, the average error of prediction results of BP neural network model based on principal component analysis is 4.48%.

• Periodical: Applied Mechanics and Materials (Volumes 713-715)
• Pages: 1939-1942
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.713-715.1939
• Online since: January 2015
• Authors: Xing Mei Xu*, Li Ying Cao, Jing Zhou
• Keywords: BP Neural Network, Contribution Rate, Grain Yield, Principal Component Analysis

References

[1] Hansen J W, Potgieter A, Tippett M K. Using a general circulation model to forecast regional wheat yields in northeast Australia[J]. Agricultural and Forecast Meteorology, 2004, 127: 77-92.

DOI: 10.1016/j.agrformet.2004.07.005

[2] Vogel F A, Bange G A, etal. Miscellaneous Publication[M]. Washington DC, 1999: 36.

[3] SUN Dongsheng, LI Shiying. Analysis of time series model for forecast China's grain output[J]. Journal of agricultural technology economic, 2010(3): 97-106.

[4] LV Jing. The prediction and analysis of grain yield based on Elman-NN model[J]. Journal of Northwest University ( Natural Science Edition), 2014, 44(3): 512-516. (in Chinese).

[5] ZHOU Jieming, PENG Wenfu. The Grey Correlation Analysis of Factors Affecting Grain Production and the Forecast for Grain Production in Sichuan Province[J]. Journal of Sichuan Normal University(Natural Science), 2005, 28(3): 350-353. (in Chinese).

[6] ZHANG Chengcai, CHEN Shaodan. Application of BP Neural Network in Predicting Grain Yield of Henan Province[J]. Hubei Agricultural Sciences, 2014, 53(8): 1969-1971. (in Chinese).

[7] YANG Yushi，DONG Lianjie，GUAN Lin. The Prediction of Grain Production Based on Rough Set and BP Neural Network[J]. Agricultural Mechanization Research, 2014, 11: 34-37. (in Chinese).

[8] CHEN Yongfu, WU Beibei, WANG Jingjing. Short run forecasting on China's cereals Production: A simulation by multivariate empirical Probability distributions model[J]. Systems Engineering--Theory&Practice, 2012: 32(11): 2363-2371. (in Chinese).

[9] JIANG Huifei, LIAO Shuhua, DING Yi, etc. Grain crop yield prediction based on markov model and probability distribution character of stochastic series[J]. Chinese agricultural meteorological, 2006, 27(4): 269-272. (in Chinese).

[10] SU Bo, LIU Lu, YANG Fangting. Comparison and research of grain production forecasting with methods of GM(1, N) gray system and BPNN[J]. Journal of China Agricultural University, 2006, 11(4): 99-104. (in Chinese).

[11] ZHU Xu, LU Jingzhou, XU Na, etc. Structural Damage Identification Based on Time Series and Principal Component Analysis[J]. Journal of Yantai University ( Natural Science and Engineering Edition) , 2013, 26(3): 207-211. (in Chinese).

[12] GAO Jun. Artificial neural network theory and simulation [M]. BEI Jing: Machinery Industry Press, 2007. (in Chinese).

[13] LIU Ke. The forecast application based on principal component analysis and BP neural network in the city completed area[J]. Progress in Geography, 2007, 26(6): 129-137. (in Chinese).

[14] XIE Zhonghua. Statistical analysis and application of MATLAB: 40 Case analysis[M]. BEI jing: Beihang University press, 2010. (in Chinese).