DEVELOPMENT OF A MODEL FOR SUPPORTING AGRICULTURAL DECISION-MAKING BASED ON AGRO-METEOROLOGICAL SENSOR DATA: A CASE STUDY OF THE IMETOSSTATION
Keywords:
precision agriculture; agro-meteorological sensors; IMETOS; FieldClimate API; irrigation scheduling; VPD; soil moisture indexAbstract
This paper proposes a practical data-driven model that converts hourly observations from an IMETOSstation and connected soil sensors into interpretable indicators for microclimate stress monitoring, irrigation advisory, pesticide spraying windows, and station power-health diagnostics. The case study uses an exported FieldClimate dataset containing hourly air temperature, dew point, solar radiation, vapor pressure deficit (VPD), relative humidity, precipitation, wind statistics, and soil measurements (5TE volumetric water content, dielectric permittivity, soil temperature; Watermark soil-water tension), complemented by daily reference evapotranspiration (ET0). Results demonstrate how raw sensor streams can be operationalized into simple, auditable rules and normalized indices such as soil moisture index (SMI), soil water deficit, heat-stress hours, and spray-suitable hours.
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