Abstract
An artificial neural network (ANN) was used to determine the moisture content of hard, red winter wheat. The ANN was trained to recognize moisture content in the range from 10.6% to 19.2% (wet basis) from transmission coefficient measurements on samples of wheat. The measurements were made at 8 microwave frequencies (10 GHz to 18 GHz) on wheat samples of varying bulk densities (0.72 g/cm/sup 3/ to 0.88 g/cm/sup 3/) at 24/spl deg/C. The trained network predicted moisture content (%) with a mean absolute error of 0.135 (compared with oven-dried measurements).
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