Image classification and control of microfluidic systems

Current microfluidic-based microencapsulation systems rely on human experts to monitor and oversee the entire process spanning hours in order to detect and rectify when defects are found. This results in high labor costs, degradation and loss of quality in the desired collected material, and damage to the physical device. We propose an automated monitoring and classification system based on deep learning techniques to train a model for image classification into four discrete states. Then we develop an actuation control system to regulate the flow of material based on the predicted states. Experimental results of the image classification model show class average recognition rate of 95.5%. In addition, simulated test runs of our valve control system verify its robustness and accuracy.