The Diagnostic Void: A Systemic Failure of Commercial Precision Agriculture Platforms to Integrate Science-Based Soil Health Indicators

Soil degradation is a primary threat to agricultural sustainability, yet addressing it is hampered by high spatial variability of soil properties. While Precision Agriculture (PA) platforms promise tools for site-specific management, their capacity to diagnose the root causes of poor soil health, such as physical degradation, remains critically unassessed. This study confronts this issue by empirically testing the dominant „symptom-based” paradigm of commercial PA. We conducted a multi-phase assessment of 34 platforms, including an in-depth user-experience (UX) test of nine major platforms, using a unique, field-verified dataset of soil physical health indicators (aggregate stability, erodibility K-factor) from a representative hummocky moraine landscape in Poland. Our findings reveal a profound „diagnostic void” in the commercial PA ecosystem. While we confirmed a strong statistical correlation between soil degradation indicators (the „cause”) and vegetation indices like NDVI (the „symptom”), we discovered that none of the tested platforms offered built-in tools for soil structure or erosion analysis. This failure was compounded by a convergence of technical (e.g., lack of GeoTIFF support), economic (paywalls), and ecosystem-level barriers that systematically prevent the integration of user-generated scientific soil data. The current generation of PA platforms is fundamentally limited to treating symptoms rather than diagnosing causes, forcing users into a reactive and potentially unsustainable management paradigm. We argue that this diagnostic gap is not a mere technological oversight but a systemic failure driven by market priorities. Bridging this gap requires a fundamental reorientation of the ag-tech sector towards open, interoperable, and analytically robust platforms that empower science-based soil stewardship.