Abstract. As we move toward Agriculture 4.0, there is increasing attention and pressure on the productivity of food production and processing. Optimizing efficiency in critical food processes such as corn drying is essential for long-term storage and economic viability.

Using machine learning, neural networks, and LSTM modeling, a predictive model was implemented for historical data containing drying parameters and weather conditions. Because the 3826 samples were not originally intended as a predictive-model dataset, various imputation techniques were used to ensure integrity.

The model was evaluated using four objective metrics and achieved an RMSE of 0.645, an MSE of 0.416, an MAE of 0.352, and a MAPE of 2.555. The results indicate that the method can support outlet-moisture prediction and process optimization while reducing energy consumption, improving product quality, and increasing the economic profitability of food processing.