Variable rate prescription generation using heterogenous prescription sources with learned weighting factors

摘要

A method for prescribing a field operation by generating an optimized prescription with a weighted prescription subprocess, executing the field operation prescribed, and then updating the weighted prescription subprocess using a learning subprocess. The weighted prescription subprocess calculates and sums weighted output from two or more site-specific models to generate the optimized prescription. The learning subprocess determines new model weights as a function of relative model error calculated by comparing model output against actual and desired results of the executed field operation.