Évaluation comparative des algorithmes d'apprentissage automatique pour la classification des types de sols à partir de caractéristiques physico-chimiques : application de Random Forest, XGBoost, SVM et KNN
This study explores the application of machine learning algorithms for soil type classification, a crucial task for agriculture and sustainable land management, particularly in areas facing environmental challenges such as soil degradation. The research was conducted in the Djilor commune, located i...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | fra |
| Published: |
Éditions en environnement VertigO
2025-04-01
|
| Series: | VertigO |
| Subjects: | |
| Online Access: | https://journals.openedition.org/vertigo/48619 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849302946872819712 |
|---|---|
| author | Mamadou Ndiaye René Boissy Mbagnick Faye N’kpomé Styvince Romaric Kouao |
| author_facet | Mamadou Ndiaye René Boissy Mbagnick Faye N’kpomé Styvince Romaric Kouao |
| author_sort | Mamadou Ndiaye |
| collection | DOAJ |
| description | This study explores the application of machine learning algorithms for soil type classification, a crucial task for agriculture and sustainable land management, particularly in areas facing environmental challenges such as soil degradation. The research was conducted in the Djilor commune, located in the Sine Saloum region of Senegal, an area affected by salinization and agricultural land loss. Using physico-chemical characteristics such as texture (percentages of sand, silt, and clay), pH, organic matter, cation exchange capacity (CEC), bulk density, and water retention, this study evaluates the performance of four algorithms: Random Forest, XGBoost, SVM, and KNN. A set of 1000 random samples was used for training and testing, with cross-validation and confusion matrices to assess performance. The results show that SVM achieves the best performance with an overall accuracy of 98.85%, followed by Random Forest (97.13%) and KNN (95.40%), while XGBoost shows an accuracy of 93.68%. These results highlight the ability of the models to capture the complex relationships between the physico-chemical characteristics, although adjustments are needed to improve their ability to handle minority classes. The analysis of feature importance reveals that soil texture, particularly the percentages of sand and silt, plays a key role in soil type classification. These results highlight the relevance of machine learning as a tool for sustainable management of agricultural land and natural resources. However, the study also highlights limitations, such as the need for validation with field data and exploration of more complex models to improve the robustness and generalisability of the conclusions. |
| format | Article |
| id | doaj-art-e6ce36509df84820bce686840ffff7b7 |
| institution | Kabale University |
| issn | 1492-8442 |
| language | fra |
| publishDate | 2025-04-01 |
| publisher | Éditions en environnement VertigO |
| record_format | Article |
| series | VertigO |
| spelling | doaj-art-e6ce36509df84820bce686840ffff7b72025-08-20T04:02:05ZfraÉditions en environnement VertigOVertigO1492-84422025-04-0110.4000/13raqÉvaluation comparative des algorithmes d'apprentissage automatique pour la classification des types de sols à partir de caractéristiques physico-chimiques : application de Random Forest, XGBoost, SVM et KNNMamadou NdiayeRené BoissyMbagnick FayeN’kpomé Styvince Romaric KouaoThis study explores the application of machine learning algorithms for soil type classification, a crucial task for agriculture and sustainable land management, particularly in areas facing environmental challenges such as soil degradation. The research was conducted in the Djilor commune, located in the Sine Saloum region of Senegal, an area affected by salinization and agricultural land loss. Using physico-chemical characteristics such as texture (percentages of sand, silt, and clay), pH, organic matter, cation exchange capacity (CEC), bulk density, and water retention, this study evaluates the performance of four algorithms: Random Forest, XGBoost, SVM, and KNN. A set of 1000 random samples was used for training and testing, with cross-validation and confusion matrices to assess performance. The results show that SVM achieves the best performance with an overall accuracy of 98.85%, followed by Random Forest (97.13%) and KNN (95.40%), while XGBoost shows an accuracy of 93.68%. These results highlight the ability of the models to capture the complex relationships between the physico-chemical characteristics, although adjustments are needed to improve their ability to handle minority classes. The analysis of feature importance reveals that soil texture, particularly the percentages of sand and silt, plays a key role in soil type classification. These results highlight the relevance of machine learning as a tool for sustainable management of agricultural land and natural resources. However, the study also highlights limitations, such as the need for validation with field data and exploration of more complex models to improve the robustness and generalisability of the conclusions.https://journals.openedition.org/vertigo/48619Senegalmachine learningRandom ForestXGBoostsupport vector machineK-Nearest Neighbors |
| spellingShingle | Mamadou Ndiaye René Boissy Mbagnick Faye N’kpomé Styvince Romaric Kouao Évaluation comparative des algorithmes d'apprentissage automatique pour la classification des types de sols à partir de caractéristiques physico-chimiques : application de Random Forest, XGBoost, SVM et KNN VertigO Senegal machine learning Random Forest XGBoost support vector machine K-Nearest Neighbors |
| title | Évaluation comparative des algorithmes d'apprentissage automatique pour la classification des types de sols à partir de caractéristiques physico-chimiques : application de Random Forest, XGBoost, SVM et KNN |
| title_full | Évaluation comparative des algorithmes d'apprentissage automatique pour la classification des types de sols à partir de caractéristiques physico-chimiques : application de Random Forest, XGBoost, SVM et KNN |
| title_fullStr | Évaluation comparative des algorithmes d'apprentissage automatique pour la classification des types de sols à partir de caractéristiques physico-chimiques : application de Random Forest, XGBoost, SVM et KNN |
| title_full_unstemmed | Évaluation comparative des algorithmes d'apprentissage automatique pour la classification des types de sols à partir de caractéristiques physico-chimiques : application de Random Forest, XGBoost, SVM et KNN |
| title_short | Évaluation comparative des algorithmes d'apprentissage automatique pour la classification des types de sols à partir de caractéristiques physico-chimiques : application de Random Forest, XGBoost, SVM et KNN |
| title_sort | evaluation comparative des algorithmes d apprentissage automatique pour la classification des types de sols a partir de caracteristiques physico chimiques application de random forest xgboost svm et knn |
| topic | Senegal machine learning Random Forest XGBoost support vector machine K-Nearest Neighbors |
| url | https://journals.openedition.org/vertigo/48619 |
| work_keys_str_mv | AT mamadoundiaye evaluationcomparativedesalgorithmesdapprentissageautomatiquepourlaclassificationdestypesdesolsapartirdecaracteristiquesphysicochimiquesapplicationderandomforestxgboostsvmetknn AT reneboissy evaluationcomparativedesalgorithmesdapprentissageautomatiquepourlaclassificationdestypesdesolsapartirdecaracteristiquesphysicochimiquesapplicationderandomforestxgboostsvmetknn AT mbagnickfaye evaluationcomparativedesalgorithmesdapprentissageautomatiquepourlaclassificationdestypesdesolsapartirdecaracteristiquesphysicochimiquesapplicationderandomforestxgboostsvmetknn AT nkpomestyvinceromarickouao evaluationcomparativedesalgorithmesdapprentissageautomatiquepourlaclassificationdestypesdesolsapartirdecaracteristiquesphysicochimiquesapplicationderandomforestxgboostsvmetknn |