Neural Grain Bin Inversion for Calibration-Free Moisture Estimation

The patent uses synthetic data that copies the characteristics of experimental calibration data without requiring physical access to calibration targets. The synthetic training set replicates the electromagnetic responses and grain parameters that would be obtained from real measurements, allowing the neural network to learn calibration relationships virtually. This eliminates the need for physical calibration objects while maintaining measurement accuracy.

The patent replaces the mechanical/physical calibration process with a computational approach. Instead of physically introducing calibration targets into the grain bin and collecting experimental data, the system uses synthetic data generation and neural network training to establish the calibration model. This substitution of physical calibration with computational modeling resolves the accessibility problem.

The patent performs preliminary action by pre-training the neural network offline using synthetic data that covers the full range of possible grain conditions. This pre-computation phase creates a ready-to-use model that can rapidly process real measurements without requiring expensive iterative calculations at the time of measurement. The heavy computational work is done beforehand, enabling fast real-time operation.

The patent replaces expensive, time-consuming iterative inversion calculations with a lightweight neural network inference process. The neural network, once trained, provides rapid predictions with minimal computational resources, effectively substituting the heavy computational machinery of traditional inversion methods with a efficient approximation that delivers similar accuracy much faster.