DEVELOPING MICROWAVE IMAGING SYSTEM WITH MACHINE LEARNING FOR DETECTING FOREIGN OBJECTS IN PACKAGED FOOD

To fill the research gap and advance food production automation, our goal is to develop non-invasive, non-ionizing, hygienic, contactless, and intelligent in-line food inspection systems empowered by MIMO (Multiple Input Multiple Output) microwave imaging coupled with machine learning algorithm. Specifically, in this proposed project, we propose to design such systems for the detection of foreign objects in packaged food. Specific objectives areObjective 1: Develop a microwave dual-polarized MIMO-based sensing system and machine learning algorithm using simulation models.Objective 2: Assess the performance of the microwave sensing system using real packaged food.The proposed microwave imaging system will include an ultra-wideband (UWB) antenna array in a circular arrangement fashion around an inline packaging facility. The unique features are to support UWB and dual polarization microwave propagation to the packaged food to identify foreign objects that can cause hazard to human. In addition, radar-based image construction with MIMO will expedite the imaging process with high pixel resolution.