An imperative for soil spectroscopic modelling is to think global but fit local with transfer learning

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An imperative for soil spectroscopic modelling is to think global but fit local with transfer learning

Soil spectroscopy with machine learning (ML) can estimate soil properties. Extensive soil spectral libraries (SSLs) have been developed for this purpose. However, general models built with those SSLs do not generalize well on new ‘unseen’ local data. The main reason is the different characteristics...

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Bibliographic Details
Main Authors:	Viscarra Rossel, Raphael, Shen, Zefang, Ramirez Lopez, L., Behrens, T., Shi, Z., Wetterlind, J., Sudduth, K.A., Stenberg, B., Guerrero, C., Gholizadeh, A., Ben-Dor, E., St Luce, M., Orellano, C.
Format:	Journal Article
Published:	2024
Online Access:	http://purl.org/au-research/grants/arc/DP210100420 http://hdl.handle.net/20.500.11937/96051

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