PJB-2024-355
Machine Learning Innovations in Soil Science: An In-Depth Examination of Diverse Applications
Vishal Goel
Abstract
In recent years, the utilization of machine learning (ML) techniques in soil science has seen significant growth due to the availability of extensive soil data and open-source algorithms. ML methods have become essential tools for analyzing soil-related information. This paper explores the diverse applications of ML in soil science to uncover trends and patterns in the research literature. The study aims to elucidate how ML is employed in soil science and identify areas for further investigation. Key findings reveal a substantial increase in ML usage, particularly in developed countries, across various applications including remote sensing, soil organic carbon prediction, water dynamics modelling, contamination assessment, and erosion analysis. Advanced ML techniques often outperform simpler methods, capturing the complex, non-linear relationships inherent in soil systems. However, precautions against overfitting and the necessity for interpretable models are emphasized to ensure reliability and understanding. Collaboration between disciplines, coupled with high-quality soil data and domain-specific feature engineering, is crucial for advancing ML applications in soil science and promoting sustainable land management practices.
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