Integrated image processing and machine learning framework for precise quantification and prediction of soil erosion

Soil erosion, primarily driven by water and wind, poses a significant environmental challenge globally, leading to land degradation and geo-hazards. Despite various empirical methods, image analysis, and machine learning techniques employed to address this issue, effective mitigation tools remain lacking. This study presents an innovative framework integrating image processing (IP) and machine learning (ML) to enhance the understanding, quantification, and prediction of soil erosion processes. Laboratory flume experiments were conducted to capture erosion images, which were pre-processed using techniques such as Contrast Limited Adaptive Histogram Equalization (CLAHE) to improve image quality. Supervised ML models, including Logistic Regression (LR), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF), were applied to classify eroded and non-eroded soil areas. The model's performance was rigorously evaluated using metrics such as precision, recall, and F1-score. The results demonstrated that KNN and RF outperformed other models in predicting soil erosion, with KNN exhibiting the least variation (2.39%) compared to the reference erosion profile. This study underscores the potential of an IP and ML ensemble framework for precise soil erosion quantification and prediction, offering practical applications for erosion mitigation. The open-source code and dataset are available at https://github.com/mlgeotech/erosion.git.