Recent Papers

Soil health is a critical factor in sustainable agriculture, food security, and ecosystem stability. Traditional laboratory-based soil analysis methods are accurate but remain costly, time-consuming, and spatially limited, creating the need for faster and scalable alternatives. In recent years, machine learning (ML) techniques integrated with remote sensing have emerged as promising solutions. This review examines three major approaches: the use of Unmanned Aerial Vehicles (UAVs) equipped with hyperspectral sensors for high-resolution nutrient mapping, ensemble ML models for robust soil organic carbon (SOC) prediction, and soil-science-informed ML approaches that embed domain knowledge into algorithms for improved interpretability. Studies such as 1 and 6 demonstrate the potential of UAV-based imaging for nutrient estimation, while 2 highlights the advantages of ensemble methods for SOC modeling across diverse landscapes. More recently, 3 emphasized soil-informed ML for balancing accuracy with scientific explainability in data-scarce regions. Through a comparative review of datasets, preprocessing techniques, and algorithms, this paper highlights the strengths, limitations, and trade-offs of these approaches. The study concludes that UAV hyperspectral imaging provides unmatched spatial detail, ensemble SOC prediction ensures robustness, and soil-informed ML enhances generalizability. A hybrid strategy integrating these methods is suggested as a practical direction for future soil health monitoring research.

Copyright © 2025 Dr. P. Bastin Thiyagaraj1. This is an open access article distributed under the Creative Commons Attribution License.