Female mosquito detection by means of AI techniques inside release containers in the context of a Sterile Insect Technique program

Abstract

The Sterile Insect Technique (SIT) is a biological pest control technique based on the release into the environment of sterile males of the insect species whose population is to be controlled. The entire SIT process involves mass-rearing within a biofactory, sorting of the specimens by sex, sterilization, and subsequent release of the sterile males into the environment. The reason for avoiding the release of female specimens is because, unlike males, females bite, with the subsequent risk of disease transmission. In the case of Aedes mosquito biofactories for SIT, the key point of the whole process is sex separation. This process is nowadays performed by a combination of mechanical devices and AI-based vision systems. However, there is still a possibility of false negatives, so a last stage of verification is necessary before releasing them into the environment. It is known that the sound produced by the flapping of adult male mosquitoes is different from that produced by females, so this feature can be used to detect the presence of females in containers prior to environmental release. This paper presents a study for the detection of females in Aedes mosquito release vessels for SIT programs. The containers used consist of PVC a tubular design of 8.8cm diameter and 12.5cm height. The containers were placed in an experimental setup that allowed the recording of the sound of mosquito flight inside of them. Each container was filled with 250 specimens considering the cases of (i) only male mosquitoes, (ii) only female mosquitoes, and (iii) 75% males and 25% females. Case (i) was used for training and testing, whereas cases (ii) and (iii) were used only for testing. Two algorithms were implemented for the detection of female mosquitoes: an unsupervised outlier detection algorithm (iForest) and a one-class SVM trained with male-only recordings.