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Crop yield forecasting using artificial neural networks : a comparison between spatial and temporal models

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journal contribution
posted on 06.12.2017, 00:00 by Wanwu GuoWanwu Guo, H Xue
Our recent study using historic data of wheat yield and associated plantation area, rainfall, and temperature has shown that incorporating statistics and artificial neural networks can produce highly satisfactory forecasting of wheat yield. However, no comparison has been made between the outcomes from the spatial neural network model and commonly used temporal neural network models in crop forecasting. This paper presents the latest research outcomes from using both the spatial and temporal neural network models in crop forecasting. Our simulation shows that the spatial NN model is able to predict the wheat yield with respect to a given plantation area with a high accuracy compared with the temporal NARNN and NARXNN models. However, the high accuracy of the spatial NN model in crop yield forecasting is limited to the forecasting of crop yield only within normal ranges. Users must be cautious when using either NARNN or NARXNN for crop yield forecasting due to their inconsistency between the results of training and forecasting.

History

Issue

2014

Start Page

1

End Page

7

Number of Pages

7

eISSN

1563-5147

ISSN

1024-123X

Location

United States

Publisher

Hindawi Publishing Corporation

Language

en-aus

Peer Reviewed

Yes

Open Access

Yes

External Author Affiliations

Inner Mongolia Agricultural University; School of Engineering and Technology (2013- ); TBA Research Institute;

Era Eligible

Yes

Journal

Mathematical problems in engineering.