arXiv (All)
open access
X-LIBS: Interpretable Soil Classification Using Explainable AI and Laser-Induced Breakdown Spectroscopy
Machine learning (ML) has emerged as a powerful tool for soil analysis using Laser-Induced Breakdown Spectroscopy (LIBS). However, traditional black-box models often lack interpretability, limiting their effectiveness in decision-making processes. This study introduced explainable AI (XAI) technique
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Human-guided physics-constrained AI agents construct an auditable model of soil-plug evolution#1049102
- Interpretable Machine Learning Approaches for Forecasting and Predicting Air Pollution: A Systematic Review.#334760
- Interpretable Machine Learning for Predicting Splitting Strength of Asphalt Concrete: Insights from SHAP Analysis#173977
- Optimization Of Surface Soil Moisture Extraction Using Sentinel-1a Sar And Machine Learning In Northwest Shandong Plain, China#990412
- Multi‐Decadal Dynamics of Wetland Methane Emissions Revealed by Knowledge‐Guided Machine Learning#653986
Record · ID 1048948
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive