Abstract
In this paper, we consider a fertilization forecast algorithm based on improved BP neural network. By analyzing traditional single fertilization forecast algorithm, we find that they are too simple, lack of network training and cannot take into account the impact of different nutrients. Then, we consider an improved BP neural network algorithm, which is based on the Lagrangian multiplier method to optimize the BP neural network and nutrient balance method by weighted combination algorithm. The simulation results show that the improved method can accurately guide the amount of fertilizer, only a small amount of learning data.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Zhu, M.: Study on the effect of precision agriculture technology in Chinese agricultural development. J. Anhui Agric. Sci. 36(25), 11126–11128 (2008)
Zhao, C.: Strategy thinking on precision agriculture of China. Agric. Netw. Inf. 4, 5–8 (2010)
Yang, Y., Ran, C., Liu, W.: Algorithm of fertilization knowledge model based on fuzzy-neural network. In: IEEE International Conference on Intelligent Computing and Intelligent Systems, pp. 40–43. IEEE (2010)
Wang, H.C., Song, F., Wen, F.W.: An improved BP algorithm and its application. Adv. Mater. Res. 765–767, 489–492 (2013)
HU, S., Dai, Y., Yao, M.Y., et al.: Application of soil nutrient balance method on corn fertilization recommended. Inner Mong. Agric. Sci. Technol. (2007)
Han, F., LI, L., Peng, Z., et al.: Indica hybrid rice recommended fertilization parameter based on nutrient balance method in guizhou. Guizhou Agric. Sci. (2014)
Ding, S., Su, C., Yu, J.: An optimizing BP neural network algorithm based on genetic algorithm. Kluwer Academic Publishers (2011)
Zhang, H.W., Peng, L., Yan, X.Q.: Study on optimal weighted combination method of air quality mid-long term prediction. J. Tianjin Inst. Text. Sci. Technol. (2005)
Ilanko, S., Monterrubio, L.E., Mochida, Y., et al.: Lagrangian multiplier method. In: The Rayleigh-Ritz Method for Structural Analysis, pp. 33–37. Wiley, Hoboken (2014)
Zhao, Z., Xin, H., Ren, Y., et al.: Application and comparison of BP neural network algorithm in MATLAB. In: International Conference on Measuring Technology and Mechatronics Automation, pp. 590–593. IEEE Computer Society (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Xue, T., Liu, Y. (2018). Fertilization Forecasting Algorithm Based on Improved BP Neural Network. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 227. Springer, Cham. https://doi.org/10.1007/978-3-319-73447-7_45
Download citation
DOI: https://doi.org/10.1007/978-3-319-73447-7_45
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-73446-0
Online ISBN: 978-3-319-73447-7
eBook Packages: Computer ScienceComputer Science (R0)