Irrigation Zone Delineation by Coupling Neural Networks with Spatial Statistics

Heaton, Matthew J.; Teuscher, David; Hansen, Neil C.

doi:10.51387/24-NEJSDS71

The New England Journal of Statistics in Data Science

Irrigation Zone Delineation by Coupling Neural Networks with Spatial Statistics

Matthew J. Heaton

David Teuscher Neil C. Hansen

https://doi.org/10.51387/24-NEJSDS71

Pub. online: 31 October 2024 Type: Spatial And Environmental Statistics

Open Access

Accepted
21 August 2024

Published
31 October 2024

Abstract

Variable rate irrigation (VRI) seeks to increase the efficiency of irrigation by spatially adjusting water output within an agricultural field. Central to the success of VRI technology is establishing homogeneous irrigation zones. In this research, we propose a fusion of statistical modeling and deep learning by using artificial neural networks to map irrigation zones from simple-to-measure predictors. We further couple our neural network model with spatial correlation to capture smooth variations in the irrigation zones. We demonstrate the effectiveness of our model to define irrigation zones for a farm of winter wheat crop in Rexburg, Idaho.

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Keywords

Ordered multinomial Precision agriculture Bayesian

Funding

This work was partially funded by the US-Israel Binational Agricultural Research and Development Fund grant IS-5218-19.

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