Vol. 7, Issue 12, Part B (2025)

Artificial Intelligence (AI) has emerged as a transformative force in modern agriculture, offering unprecedented opportunities to enhance soil organic matter (SOM) dynamics, nutrient cycling efficiency, and water productivity. Soil health-led intensification the strategy of boosting agricultural productivity while restoring and sustaining ecological functions requires timely, precise, and spatially explicit information on soil processes. Traditional soil monitoring methods are often labor-intensive, infrequent, and unable to capture the complexity of interactions among soil organic matter, nutrients, microbes, and water. This comprehensive review synthesizes advancements in AI-driven approaches that support the assessment, prediction, and management of SOM decomposition, nutrient mineralization-immobilization patterns, and soil-water interactions.

Machine learning (ML) and deep learning (DL) enable continuous interpretation of multisource datasets, including remote sensing imagery, proximal soil sensors, and Internet-of-Things (IoT) networks. These models offer enhanced accuracy in predicting soil carbon turnover, nitrogen and phosphorus cycling, and greenhouse gas fluxes under diverse management scenarios. AI-enabled digital soil mapping has significantly improved the spatial resolution of soil organic carbon (SOC) inventories, facilitating the identification of carbon-depleted zones and guiding site-specific soil restoration strategies. Moreover, AI-based decision-support systems integrate crop growth models with SOM and moisture dynamics to optimize residue management, cover cropping, and organic amendments for improving nutrient use efficiency and soil biological activity.

Water productivity, AI techniques such as convolutional neural networks (CNNs), random forests, and recurrent neural networks (RNNs) have enhanced real-time soil moisture estimation, evapotranspiration forecasting, and irrigation scheduling. By linking AI-processed hydrological data with SOM-mediated water retention properties, farmers can adopt precision irrigation practices that reduce water losses while maintaining crop yields. The integration of AI with conservation agriculture, precision fertilization, and climate-smart management further supports the synergistic improvement of SOM stabilization and water-use efficiency. AI-driven soil health innovations represent a pivotal pathway toward sustainable intensification. By enabling precise management of SOM, nutrient cycling, and water resources, AI offers the potential to enhance productivity, resilience, and environmental sustainability across diverse agro-ecosystems.