Real-time determination of moisture content of potato slices during drying using long short-term memory from image morphometric parameters

Abstract

This article uses digital image processing to investigate the change in morphometric attributes of potato slices of 1 mm thickness for different drying temperatures (45, 50, 55 and 60 °C). For this purpose, a compact hot air dryer has been developed, having the desired provision for embedding an image acquisition system. The long short-term memory (LSTM) technique has been employed to determine the product moisture content inside the hot air dryer purely on the basis of morphometric characteristics of individual slices. The best-performing network optimized after hyperparameter tuning has an architecture of 156 hidden neurons, 2 LSTM units, and a learning rate of 0.0160. The optimized network’s training coefficient of determination (R2) and root mean square error (RMSE) are 0.977 and 0.0461, respectively. In addition, the quantitative uncertainty analysis using the quantile score (Q) and the prediction-interval-normalized root-mean-square width (PINRW) was carried out to evaluate the robustness of the network. This approach is instrumental in designing a non-invasive quality control system during drying phenomena. It also helps in studying the complexity of the drying mechanism by visualizing the morphometric changes taking place in the product during drying.