293 - Machine Learning and Artificial Intelligence for Increasing Mosquito Surveillance Efficiency: An Update on the Aedes Egg Counter App

Abstract: In a typical mosquito surveillance and control operation, the bottleneck of mosquito identification and processing is counting collected adults and or eggs from the field or laboratory. Further, when collecting eggs in the field, lighting and magnification are not always optimal to receive results in a timely matter—much less information on species. This project, as part of a larger Aedes ecology and breeding habitat NIH grant, aimed to simplify and expedite the egg counting and species identification portion of egg collections—from the field and from a laboratory colony. Through collaboration with the University of South Carolina’s Research Cyberinfrastructure program, a machine-learning-based mosquito egg counting algorithm was constructed and applied to count Aedes eggs on germination paper, and a new Convolutional Neural Network (CNN) based algorithm to detect the eggs and egg clusters. Images were taken from multiple smartphones, different colored egg papers were used, various angles and magnification were tested, and a stand for phone stabilization was 3D printed to standardize image capture. All images were counted by a laboratory technician to compare the app accuracy to human counting. This is an update on the progress of this Aedes egg counter application, where in the past two years, we have retrained our CNN model on 165 field ovicup and laboratory colony egg papers and redesigned our application in a cloud Artificial Intelligence architecture. Overall accuracy for the application in distinguishing eggs and egg clusters from debris or non-eggs is 82%. Future directions include distinguishing Aedes aegypti and Aedes albopictus species through egg images and expanding the phone-based application to various geographies across the United States. We anticipate this application to have wide-spread impacts on those in mosquito control and surveillance who need to identify and count mosquito eggs quickly for field collection and laboratory colony maintenance.