Machine learning assessments of soil drying for agricultural planning
New machine learning models for real-time estimation of soil drying are presented. Only remotely-sensed, public data sources are used. Field conditions were modeled with 9194% accuracy by two of three algorithms. All errors fell within calculated uncertainty bounds. The model enables decision-support services without on-site measurements.
machine learning soil drying field readiness nearest neighbors soil moisture decision support