Dataset of vector mosquito images
Reshma Pise, Kailas Patil, Meena Laad, Neeraj Pise
Abstract
Mosquitoes pose substantial threat to public health resulting in million number of deaths wordlwide every year. They act as the vectors responsible for diseases such as Dengue, Yellow fever,Chikungunya, Zika etc. The harmful mosquito species are contained in the genera Aedes, Anopheles and Culex. Automated species identification of vectors is essential to implement targeted vector control strategies. The objective of the proposed paper is to construct a novel dataset of images of dangerous mosquito species. We have prepared a dataset of images of adult mosquitoes belonging to three species: Aedes Aegypti, Anopheles stephensi and Culex quinquefasciatus stored in two folders. The first folder comprises of total 2640 augmented images of mosquitoes belonging to the three species. The second folder contains original images of the the three species. The dataset is valuable for training machine and deep learning models for automatic species classification.