Development of Mathematical and Computational Models for Predicting Agricultural Soil–Water Management Properties (ASWMPs) to Optimize Intelligent Irrigation Systems and Enhance Crop Resilience

Soil–water management is fundamental to plant ecophysiology, directly affecting plant resilience under both anthropogenic and natural stresses. Understanding Agricultural Soil–Water Management Properties (ASWMPs) is therefore essential for optimizing water availability, enhancing harvest resilience, and enabling informed decision-making in intelligent irrigation systems, particularly in the face of climate variability and soil degradation. In this regard, the present research develops predictive models for ASWMPs based on the grain size distribution and dry bulk density of soils, integrating both traditional mathematical approaches and advanced computational techniques. By examining 900 soil samples from the NaneSoil database, spanning diverse crop species (Avena sativa L., Daucus carota L., Hordeum vulgare L., Medicago sativa L., Phaseolus vulgaris L., Sorghum vulgare Pers., Triticum aestivum L., and Zea mays L.), several predictive models are proposed for three key ASWMPs: soil-saturated hydraulic conductivity, field capacity, and permanent wilting point. Mathematical models demonstrate high accuracy (71.7–96.4%) and serve as practical agronomic tools but are limited in capturing complex soil–plant-water interactions. Meanwhile, a Deep Neural Network (DNN)-based model significantly enhances predictive performance (91.4–99.7% accuracy) by uncovering nonlinear relationships that govern soil moisture retention and plant water availability. These findings contribute to precision agriculture by providing robust tools for soil–water management, ultimately supporting plant resilience against environmental challenges such as drought, salinization, and soil compaction.