Using deep learning to predict soil properties from regional spectral data

By José Padarian, Budiman Minasny, Alex B. McBratney. Geoderma Regional, 2019

387 citations recorded by the source corpus.

Abstract Diffuse reflectance infrared spectroscopy allows the rapid acquisition of soil information in the field or the laboratory. The vis-NIR spectroscopy research enthusiasm around the world has created regional to global soil spectral libraries. While machine learning methods have been utilised in processing spectral data, such large regional datasets are better dealt with big data analytics. Deep learning is an exciting discipline that has already transformed the way data are analysed in many fields and could also change the way we model soil spectral data. This study developed and evaluated convolutional neural networks (CNNs), a type of deep learning algorithm, as a new way to predict soil properties from raw soil spectra. We demonstrated the effectiveness of CNNs on the LUCAS soil database, which consists of around 20,000 topsoil observations with physicochemical and biological properties from Europe. To fully utilise the capacity of the CNN model, we represented the soil spectral data as a 2-dimensional spectrogram, showing the reflectance as a function of wavelength and frequency. We showed the capacity of a CNN to be trained in a multi-task setting to simultaneously predict six soil properties in one model (OC, CEC, clay, sand, pH, total N). Compared with conventional methods such as PLS regression and Cubist regression tree, the CNN performed significantly better, especially the multi-tasking model. In the case of soil organic carbon prediction, the multi-task CNN decreased the error by 87% compared to PLS and 62% compared with Cubist. This approach proved to be effective when trained on a relatively large dataset. The high accuracy of CNN makes it an ideal tool for modelling soil spectral data.

Subfields: Analytical Chemistry, Artificial Intelligence, Environmental Engineering

View Paper: https://www.semanticscholar.org/paper/b1be04c000cf50d8eeb2aac8d26c1f93961c7988

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

In Artificial Intelligence

In Environmental Engineering

Citations in the corpus

Machine learning and soil sciences: a review aided by machine learning tools (2019, methodology): “This method has been recently applied in some DSM studies (Vaysse and Lagacherie, 2017; Sirsat et al., 2018; Cao et al., 2019).”