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.
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
