Computer Vision-based Soil Health Analysis using Machine Learning

1. We design an attention-based cross-modal fusion strategy
that enables adaptive interaction between heterogeneous
data sources, improving the learning of complementary
spectral, spatial, and environmental representations.
2. We introduce a normalized and interpretable Soil Health Index (SHI) derived from dual-standard pH predictions, translating continuous outputs into actionable categorical soil
health classes for practical agricultural decision-making.
3. We incorporate computationally efficient training strategies,
including mixed-precision learning, stochastic weight averaging, and data augmentation, to enhance robustness while
maintaining scalability for large-area soil monitoring.
4. We conduct an extensive evaluation on the pan-European
LUCAS 2018 soil dataset, demonstrating strong generalization across diverse soil types, climatic regions, and land-use
categories.