Machine Learning Algorithms for Nondestructive Sensing of Moisture Content in Grain and Seed

Machine Learning (ML) models were used to determine the percent moisture content (MC) for multiple grains and seeds after training on a large dataset obtained through several decades of research. The dataset consisted of attenuation, phase shift, dielectric properties, frequency, bulk density, and sample thickness collected for corn, barley, sorghum, soybeans, and wheat. In this paper, a new ML-based approach for calibrating microwave sensors for rapid and nondestructive determination of moisture content in multiple grains and seeds is proposed. For this purpose, a single model trained on multiple grains and seeds was developed and allowed moisture determination in individual grain or seed samples. Performance of this model is investigated and compared with models trained on an individual grain or seed by using different algorithms including artificial neural network (NN), Support Vector Regression (SVR), ElasticNet, among other algorithms. In addition, these models were tested on new data collected for corn, wheat, and soybeans at 24∘ C with MC ranging from 7.89% to 20.19% and frequencies between 5 GHz to 15 GHz. The lowest mean absolute error (MAE) of MC was obtained with frequencies between 8 and 12 GHz for most models. Training when using the dielectric properties, frequency, and grain type with a single SVR-based model had the lowest error at 9 GHz for soybeans and corn. The SVR-based model showed no drawbacks and a slight improvement predicting MC using a single model when training over all grains and seeds compared with training several models over each grain individually.