Artificial intelligence-based prediction of key textural properties from LUCAS and ICRAF spectral libraries


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Output type: Journal article

UM6P affiliated Publication?: Yes

Author list: Gouda M.Z., Nagihi E.M., Khiari L., Gallichand J., Ismail M.

Publisher: MDPI

Publication year: 2021

Journal: Agronomy (2073-4395)

Volume number: 11

Issue number: 8

ISSN: 2073-4395

eISSN: 2073-4395

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85112173597&doi=10.3390%2fagronomy11081550&partnerID=40&md5=d9c5b599864ede434a18be0d2e4f38a9

Languages: English (EN-GB)


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Abstract

Soil texture is a key soil property influencing many agronomic practices including fertilization and liming. Therefore, an accurate estimation of soil texture is essential for adopting sustainable soil management practices. In this study, we used different machine learning algorithms trained on vis–NIR spectra from existing soil spectral libraries (ICRAF and LUCAS) to predict soil textural fractions (sand–silt–clay %). In addition, we predicted the soil textural groups (G1: Fine, G2: Medium, and G3: Coarse) using routine chemical characteristics as auxiliary. With the ICRAF dataset, multilayer perceptron resulted in good predictions for sand and clay (R2 = 0.78 and 0.85, respectively) and categorical boosting outperformed the other algorithms (random forest, extreme gradient boosting, linear regression) for silt prediction (R2 = 0.81). For the LUCAS dataset, categorical boosting consistently showed a high performance for sand, silt, and clay predictions (R2 = 0.79, 0.76, and 0.85, respectively). Furthermore, the soil texture groups (G1, G2, and G3) were classified using the light gradient boosted machine algorithm with a high accuracy (83% and 84% for ICRAF and LUCAS, respectively). These results, using spectral data, are very promising for rapid diagnosis of soil texture and group in order to adjust agricultural practices. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.


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Last updated on 2021-24-11 at 23:19