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An Artificial Neural Network Model for Crop Yield Responding to Soil Parameters

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)

Abstract

This paper presents an artificial neural network model for crop yield responding to soil parameters. The experimental data had been obtained via a precision agriculture experiment, which is carried out by PAC in a demo farm locating in Shunyi district, Beijing in 2000. The model has been established by training a back propagation neural network with 58 samples and tested with other 14 samples. The model consists of 6, 11 and 1 processing units in the input, hidden and output layers, and the step length is 0.05, the momentum coefficient is 0.5. The training was terminated after 20000 times and the convergence effect was very good. The training results are that the correlation coefficient is 0.916 and the average error value is 2.8×10-2. It shows that the model can precisely describe crop yield responding to soil parameters.

Keywords

Hide Layer Crop Yield Output Layer Artificial Neural Network Model Back Propagation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  1. 1.China Agricultural UniversityBeijingChina

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