Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)

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A Comparative Study of Soil Quality Prediction based on Machine Learning for Geospatial Analysis

Authors
Habiba Ben-Abderrahmane1, *, Slimane Oulad-Naoui2, Meriem Mokdad2, Abdelmalek Tableb-Ahmed2
1Laboratoire d’Informatique et des Mathematiques, University of Ammar Telidji, 37G Ghardaia Road, 03000, Laghouat, Algeria
2Laboratoire des Mathématiques et Sciences Appliquées, Université de Ghardaia, Scientific Zone, PO Box 455, 47000, Ghardaia, Algeria
*Corresponding author. Email: habiba.benabderrahmane@lagh-univ.dz
Corresponding Author
Habiba Ben-Abderrahmane
Available Online 5 August 2025.
DOI
10.2991/978-94-6463-805-9_23How to use a DOI?
Keywords
Soil Quality Prediction; Machine Learning; GeoSpatial Analysis; Comparative Study
Abstract

Soil quality prediction (SQP) is essential for many fields such as: agriculture, civil engineering and environmental applications. Traditional SQP assessment methods are often time-consuming and resource-intensive. This study explores the application of four machine learning models: RBFN, LightGBM, XGBoost, and DNN for automated SQP. Based on the SoilGrid dataset, we first construct a geospatial dataset with nine physical and chemical soil features and evaluate model performances to identify the most effective approach. The experiments indicate that XGBoost achieves the highest R2 score of 0.977, while maintaining a reasonable computational cost, making it the most suitable model for soil quality prediction on a scale. In addition, we introduce an efficient pipeline that integrates geospatial data preprocessing, PCA-based dimensionality reduction, and weighted soil quality index computation, followed by geospatial mapping. This approach offers a scalable, accurate, and efficient solution to improve decision making in sustainable land management, precision agriculture, and environmental monitoring.

Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
Series
Advances in Intelligent Systems Research
Publication Date
5 August 2025
ISBN
978-94-6463-805-9
ISSN
1951-6851
DOI
10.2991/978-94-6463-805-9_23How to use a DOI?
Copyright
© 2025 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Habiba Ben-Abderrahmane
AU  - Slimane Oulad-Naoui
AU  - Meriem Mokdad
AU  - Abdelmalek Tableb-Ahmed
PY  - 2025
DA  - 2025/08/05
TI  - A Comparative Study of Soil Quality Prediction based on Machine Learning for Geospatial Analysis
BT  - Proceedings of the First International Conference on Artificial Intelligence, Smart Technologies and Communications (AISTC 2025)
PB  - Atlantis Press
SP  - 202
EP  - 210
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-805-9_23
DO  - 10.2991/978-94-6463-805-9_23
ID  - Ben-Abderrahmane2025
ER  -