Learning to count mosquitoes for the Sterile Insect Technique
Abstract
Mosquito-borne illnesses such as dengue, chikungunya, and Zika
are major global health problems, which are not yet addressable
with vaccines and must be countered by reducing mosquito popula-
tions. The Sterile Insect Technique (SIT) is a promising alternative
to pesticides; however, effective SIT relies on minimal releases of
female insects. This paper describes a multi-objective convolutional
neural net to significantly streamline the process of counting male
and female mosquitoes released from a SIT factory and provides a
statistical basis for verifying strict contamination rate limits from
these counts despite measurement noise. These results are a promis-
ing indication that such methods may dramatically reduce the cost
of effective SIT methods in practice.
Research Areas
