Estimation of Neural Network Parameters for Wheat Yield Prediction

Precision agriculture (PA) and information technology (IT) are closely interwoven. The former usually refers to the application of nowadays’ technology to agriculture. Due to the use of sensors and GPS technology, in today’s agriculture many data are collected. Making use of those data via IT often leads to dramatic improvements in efficiency. For this purpose, the challenge is to change these raw data into useful information. This paper deals with suitable modeling techniques for those agricultural data where the objective is to uncover the existing patterns. In particular, the use of feed-forward backpropagation neural networks will be evaluated and suitable parameters will be estimated. In consequence, yield prediction is enabled based on cheaply available site data. Based on this prediction, economic or environmental optimization of, e.g., fertilization can be carried out.

[1]  J. V. Stafford,et al.  Economic benefits of neural network-generated site-specific decision rules for nitrogen fertilization. , 2007 .

[2]  Georg Weigert.  Data Mining und Wissensentdeckung im Precision Farming , 2006 .

[3]  Laurene V. Fausett,et al.  Fundamentals Of Neural Networks , 1993 .

[4]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .

[5]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[6]  Martin T. Hagan,et al.  Neural network design , 1995 .

[7]  Elizabeth Pattey,et al.  Corn yield prediction with artificial neural network trained using airborne remote sensing and topographic data , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[8]  Emmett W. Chappelle,et al.  "Red edge" optical properties of corn leaves from different nitrogen regimes , 2002, IEEE International Geoscience and Remote Sensing Symposium.

[9]  Board on Agriculture.  Precision Agriculture in the 21st Century: Geospatial and Information Technologies in Crop Management , 1998 .

[10]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[11]  Anupam Joshi,et al.  Application of neural networks: precision farming , 1998, 1998 IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98CH36227).

[12]  John R. Miller,et al.  Exploring the relationship between red edge parameters and crop variables for precision agriculture , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[13]  Kenneth A. Sudduth,et al.  Soil electrical conductivity and topography related to yield for three contrasting soil-crop systems , 2003 .

[14]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[15]  J Pawlak,et al.  Agricultural engineering for a better world , 2006 .