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 - Mamadou Ndiaye; René Boissy; Mbagnick Faye; N’kpomé Styvince Romaric Kouao (VertigO)
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.