Predicting Moisture Content of Cereal Grain and Oilseed by Using Machine Learning Algorithms: A Comparison of State-of-the-Art Methods

Highlights Estimating the moisture content (MC) of cereal grain and oilseed in real time is a challenge. We explored the accuracy of MC estimation with machine learning models when cereal grain and oilseed were dried with a microwave device. The frequency, attenuation, phase shift, and dielectric properties were utilized for model training. Out of five machine learning models, a custom DNN model performed the best, and the RF model was the next best-performing model. ABSTRACT. Estimating moisture content (MC) in cereal grains and oilseeds plays a major role in their quality control, ensuring safe storage and determining their market value. Conventional MC measurement methods, such as oven drying, are time-consuming and not suitable for continuous monitoring. This study focuses on evaluating various machine learning algorithms to estimate MC in cereal grains and oilseeds by analyzing their dielectric properties (dielectric constant, e', and dielectric loss factor, e"). Machine learning (ML) models, including a modified Deep Neural Network (DNN), Autoencoder, LeNet5, Support Vector Machine (SVM), and Random Forest (RF), were evaluated for MC determination. The dataset consisted of multiple cereal grain and oilseed types of oats, soybeans, sorghum, corn, barley, and wheat (hereafter referred to as “grains” for simplicity). The models were trained on three test sets: (I) only dielectric properties from all grains, (II) all measured physical properties from all grains, and (III) all measured physical properties from wheat. Results showed that the DNN outperformed other models on test sets I and III with an R 2 value of 0.9084 and 0.9995, respectively, whereas RF showed the best performance in test set II with an R 2 value of 0.9784. These results highlight the effectiveness of ML algorithms in estimating the MC level of grains accurately, providing an alternative to traditional methods and enabling real-time and non-destructive monitoring in agricultural processes. Keywords: Cereal grains, Dielectric properties, Machine learning, Microwave sensing, Moisture content, Oilseeds.