Recognition of inlet wet food in drying process through a deep learning approach

Summary

Deep learning was tested for its feasibility as CV tool for the analysis of inlet wet food into the drying process. In detail, convolutional neural networks (CNNs) were successfully applied for addressing the following tasks: (i) the semantic image segmentation of the inlet product; (ii) the inlet product classification for automatic selection of drying parameters. As a result, CNNs have been shown to be used for the development of smart dryers able to monitor and control the process.