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2015 12th Conference on Computer and Robot Vision (CRV) (2015)
Halifax, NS, Canada
June 3, 2015 to June 5, 2015
ISBN: 978-1-4799-1986-4
pp: 8-15
Advances in diffuse reflectance infra-red spec-cryoscopy measurements have made it possible to estimate number of functional properties of soil inexpensively and accurately. Core to such techniques are machine learning methods that can map high-dimensional spectra to real-valued outputs. While previous works have considered predicting each property individually using simple regression methods, the correlation structure present in the output variables prompts us to consider methods that can leverage this structure to make more accurate predictions. In this paper, we leverage advances in deep learning architectures, specifically convolution neural networks and conditional restricted Boltzmann machines for structured output prediction for soil property prediction. We evaluate our methods on two recent spectral datasets, where output soil properties are shown to have a measurable degree of correlation.
Soil properties, Artificial neural networks, Training, Convolution, Computer architecture

M. Veres, G. Lacey and G. W. Taylor, "Deep Learning Architectures for Soil Property Prediction," 2015 12th Conference on Computer and Robot Vision (CRV), Halifax, NS, Canada, 2015, pp. 8-15.
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