Soil health is now being measured with increasing speed through sensors, remote sensing, artificial intelligence, and digital monitoring platforms. This is important progress. However, one critical issue remains: many soil health indicators are still interpreted using fixed benchmarks and temperate-region assumptions. These assumptions may not work well in tropical agricultural landscapes, where highly weathered soils, acidity, aluminium toxicity, rapid organic matter turnover, intense rainfall, and unstable biological activity strongly affect soil function.

This oral presentation argues that soil health should not be treated as a fixed score, but as a dynamic condition shaped by soil chemistry, soil biology, water movement, climate stress, crop demand, and management history. For tropical and Global South systems, soil health assessment must move from simple monitoring toward soil intelligence.

The presentation introduces the SHE™ Soil Intelligence Framework and the Soil Intelligence Pyramid as practical concepts for linking soil indicators with field decisions, regenerative agriculture, ESG reporting, and soil governance. It also presents a 5-Sensor Model for reading soil function through interacting physical, chemical, biological, hydrological, and management signals.

The session will highlight why universal soil health metrics can become misleading when they are applied without local calibration. Special attention will be given to tropical acid soils, biological resilience, and the need for adaptive thresholds rather than static benchmarks.

The presentation concludes by showing how AI-enabled soil intelligence can support more credible soil monitoring, geospatial MRV, regenerative agriculture verification, and policy-linked soil health systems. The goal is to contribute a tropical and Global South perspective to the development of more practical, inclusive, and scientifically defensible soil health indicators.