53. Can local soil samples improve the accuracy of mid-infrared (MIR) and X-ray fluorescence (XRF)-based spectral prediction models?-Reference-Cited by-同舟云学术

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53. Can local soil samples improve the accuracy of mid-infrared (MIR) and X-ray fluorescence (XRF)-based spectral prediction models?

Published:2021-06-25 Issue: Volume: Page:
ISSN:
Container-title:Precision agriculture ’21
language:
Short-container-title:

Author:

Vona V.¹,Sarjant S.²,Luleva M.²,Kulmány I.M.³,Vona M.⁴

Affiliation:

1. Faculty of Agricultural and Food Sciences, Széchenyi István University, Mosonmagyaróvár-Hungary, Lucsony u. 15-17, 9200 Mosonmagyaróvár, Hungary.

2. AgroCares, Nieuwe Kanaal 7C, 6709 PA Wageningen, the Netherlands.

3. Research Institute of Agricultural Economics, National Agricultural Research and Innovation Center, Budapest-Hungary, IX. Zsil utca 3-5, 1093 Budapest, Hungary.

4. Csernozjom Kft. Arany János u 10, 5065 Nagykörű, Hungary.

Publisher

Wageningen Academic Publishers

Link

https://www.wageningenacademic.com/doi/pdf/10.3920/978-90-8686-916-9_53

Reference50 articles.

1. Atkeson, C.G., Moore, A.W., and Schaal, S. (1997). Locally weighted learning. In Lazy learning. Dordrecht, the Netherlands: Springer.

2. Locally weighted learning

3. Using a global VNIR soil-spectral library for local soil characterization and landscape modeling in a 2nd-order Uganda watershed

4. Using a global VNIR soil-spectral library for local soil characterization and landscape modeling in a 2nd-order Uganda watershed

5. National calibration of soil organic carbon concentration using diffuse infrared reflectance spectroscopy

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