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.
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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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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
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DOI: https://doi.org/10.1007/978-3-319-73447-7_45
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