Experimental Modelling of Sunflower Seed Moisture Content During Controlled Drying Using Machine Learning Methods

The aim of this research was to experimentally analyze the influence of drying method, temperature, and drying time on moisture content (MC), elemental composition (percentages of C, H, N, S, and O), and protein and fat content in sunflower seeds, as well as to apply and compare different existing machine learning regression models for moisture content prediction. The study was conducted on three sunflower hybrids (Sumiko, Pioneer, and Agromatic Lidea) using conduction, vacuum, and fluidized bed drying at temperatures from 50 to 80 °C and durations from 15 to 60 min. The results showed that temperature and time are the main controllable parameters of drying, while drying methods and hybrid also significantly influence the process. In moisture content modelling, artificial neural networks (ANN) achieved the best predictive performance (R2 = 0.97; RMSE = 0.46), while SVR models showed slightly weaker but still high accuracy. The results indicate that machine learning models can be useful tools for predicting moisture content based on drying parameters and may support improved monitoring and management of the sunflower seed drying process.