Stored-Grain Bin Monitoring by Electromagnetic Imaging

The safe storage of grains is crucial for the food supply worldwide; for example, the storage loss is estimated to be between 2% to 30% depending on different geographic locations. In this project, an advanced signal processing algorithm (a deep learning approach) is developed to enhance the identification process of the moisture contents (MC) of grain bins from the measured electromagnetic data. This deep learning approach for grain bin monitoring will significantly accelerate the identification process of the MC as compared to existing techniques. In addition, the proposed technique provides the industrial partner with the confidence level associated with the predicted MC, which is not possible with conventional approaches.

Pedram Mojabi
Faculty Supervisor: 
Joe LoVetri
Partner University: