An explainable and auditable IoT–AI engineering framework for soil–environment regime discovery in arid agriculture

Sustainable crop planning in arid and semi-arid regions is constrained by soil degradation, water scarcity, and high environmental variability, while the adoption of data-driven decision methods remains limited by poor transparency and trust. This study investigates whether unsupervised soil–environ...

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Detalles Bibliográficos

Autores principales:	May Alashwal, Huma Jamshed, Mohammed Algarni, Urooj Waheed, Mohammed Zwawi, Ahmad Hussain
Formato:	Artigo
Lenguaje:	Inglês
Publicado:	Elsevier 2026-12-01
Colección:	Results in Engineering
Materias:	Explainable artificial intelligence (XAI) Unsupervised learning Soil–environment zoning Soil–environment regime discovery Arid Semi-arid agriculture
Acceso en línea:	http://www.sciencedirect.com/science/article/pii/S2590123026027301
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