Efficient Convolutional Neural Network Model for the Taxonomy and Sex Identification of Three Phlebotomine Sandfly Species (Diptera, Psychodidae, and Phlebotominae)

Abstract

Sandflies, small insects primarily from the Psychodidae family, are commonly found in sandy, tropical, and subtropical regions. Most active during dawn and dusk, female sandflies feed on blood to facilitate egg production. In doing so, they can transmit infectious diseases that may cause symptoms such as fever, headaches, muscle pain, anemia, skin rashes, and ulcers. Importantly, sandflies are species-specific in their disease transmission. Determining the gender and species of sandflies typically involves examining their morphology and internal anatomy using established identification keys. However, this process requires expert knowledge and is labor-intensive, time-consuming, and prone to misidentification. In this paper, we develop a highly accurate and efficient convolutional network model that utilizes pharyngeal and genital images of sandfly samples to classify the sex and species of three sandfly species (i.e., Phlebotomus sergenti, Ph. alexandri, and Ph. papatasi). A detailed evaluation of the model’s structure and classification performance was conducted using multiple metrics. The results demonstrate an excellent sex-species classification accuracy exceeding 95%. Hence, it is possible to develop automated artificial intelligence-based systems that serve the entomology community at large and specialized professionals.