An annotated wing interferential pattern dataset of dipteran insects of medical interest for deep learning

Abstract

Several Diptera species are known to transmit pathogens of medical and veterinary interest. However, identifying these species using conventional methods can be time-consuming, labor-intensive, or expensive. A computer vision-based system that uses Wing interferential patterns (WIPs) to identify these insects could solve this problem. This study introduces a dataset for training and evaluating a recognition system for dipteran insects of medical and veterinary importance using WIPs. The dataset includes pictures of Culicidae, Calliphoridae, Muscidae, Tabanidae, Ceratopogonidae, and Psychodidae. The dataset is complemented by previously published datasets of Glossinidae and some Culicidae members. The new dataset contains 2,399 pictures of 18 genera, with each genus documented by a variable number of species and annotated as a class. The dataset covers species variation, with some genera having up to 300 samples.

An optical system to detect, surveil, and kill flying insect vectors of human and crop pathogens

“…Retrieval of specimens from traps is required for genetic, morphological and biochemical analyses and examination, which are necessary steps for conducting fundamental studies on insecticide resistance in vector populations and for developing and implementing insecticide resistance management strategies 33 . Recent advances in sensor and computer vision, machine learning, and the Internet of Things technology have greatly improved the functionality and effectiveness of insect traps with respect to their capability to identify and classify vectors and to perform these tasks autonomously 89 – 96 . However, most insect traps in use today have the drawback of being labor- and time intensive to set-up, retrieve, and analyze, are difficult to use in unwelcoming terrain or over extensive sampling areas, and are subject to weather conditions and events 97 , 98 .…”