Artificial intelligence-based soil monitoring systems require rigorous metrological validation to ensure reliability in precision agriculture applications. This study presents a Virtual Measuring Instrument (VMI) framework that treats an LSTM neural network as a formal measurement function, validated according to the Guide to the Expression of Uncertainty in Measurement (GUM). The system integrates in-situ soil sensors with edge computing and LSTM-based predictive modeling to generate continuous soil parameter estimates. A comprehensive uncertainty budget combines Type A contributions from algorithmic variability across multiple training runs with Type B uncertainties from sensor specifications, propagated through the LSTM transformation via Monte Carlo simulation. Experimental validation was conducted over eight weeks at an agricultural field site with 5,376 measurements per sensor channel, collected at 15-minute intervals. Results demonstrate strong agreement between AI-generated estimates and reference laboratory measurements, with expanded uncertainty (k=2) remaining within acceptable limits for practical agricultural applications. The analysis confirms that sensor accuracy improvements yield greater uncertainty reduction than further AI model optimization. This work establishes that AI models can function as metrologically valid measurement instruments when supported by formal measurement models and GUM-compliant uncertainty frameworks. The proposed VMI methodology provides a reproducible, auditable approach for integrating AI-based monitoring systems into regulated precision agriculture and environmental monitoring solutions, enabling their reliable deployment with full traceability to reference standards.