A moisture content prediction model for deep bed peanut drying using support vector regression

In order to make the moisture content monitoring more convenient and rapid during peanut drying process, the drying characteristics of peanut were investigated and a real‐time SVR moisture content monitoring model was established in this paper. The results showed that hot air temperature, initial moisture content, airflow rate and the layer height were the key factors on peanut drying, and the peanut variety showed little effect on drying. The SVR model exhibited a good performance with R²: 0.91, RMSE: 4.38, and bias: −7.5e‐3. Compared with the results of linear regression models and multilayer perceptron model, SVR model showed a better performance. In addition, the SVR model was validated by the drying data of other three varieties of peanuts. And the relative errors between the predicted values by SVR model and the measured values were within 20%, which suggested that SVR was a promising modeling algorithm for peanut drying. PRACTICAL APPLICATION: Peanut drying is essential for peanut production due to its high moisture content at harvest (about 30–50% on the wet basis), which makes it susceptible to mildew, or even produces aflatoxins. Peanut has a special physiological structure. In the drying process, the peanut kernels are gradually shrunk, which makes the air layer volume between the shell and the kernel gradually increases, thus hindering mass and heat transfer and leading to its different drying characteristics from other grain and oil crops. Furthermore, it is necessary to monitor the moisture content changes of peanut during deep bed drying to ensure the drying uniformity and prevent energy waste. Therefore, the drying characteristics of peanuts were studied and a moisture content prediction model for deep bed drying was established to assist the actual drying process.