Convolutional neural network for simultaneous prediction of several soil properties using visible/near-infrared, mid-infrared, and their combined spectra

• Published in: Geoderma Vol. 352; pp. 251 - 267
• Main Authors: Ng, Wartini, Minasny, Budiman, Montazerolghaem, Maryam, Padarian, Jose, Ferguson, Richard, Bailey, Scarlett, McBratney, Alex B.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 15.10.2019
• Subjects: cation exchange capacity; chemometrics; clay; data collection; Deep learning; least squares; Mid-infrared spectroscopy; model validation; Multi-task learning; Near-infrared spectroscopy; prediction; sand; soil surveys; spectral analysis; spectrometers; spectroscopy; wavelengths; Deep learning; Multi-task learning; Mid-infrared spectroscopy; Near-infrared spectroscopy

No single instrument can characterize all soil properties because soil is a complex material. With the advancement of technology, laboratories have become equipped with various spectrometers. By fusing output from different spectrometers, better prediction outcomes are expected than using any single...