Graduation Year
2021
Document Type
Thesis
Degree
M.S.C.S.
Degree Name
MS in Computer Science (M.S.C.S.)
Degree Granting Department
Computer Science and Engineering
Major Professor
Sriram Chellappan, Ph.D.
Committee Member
Shaun Canavan, Ph.D.
Committee Member
Yu Sun, Ph.D.
Keywords
Deep Learning, Neural Networks, Transfer Learning
Abstract
In this paper, we design Computer Vision techniques to determine stages in the Gonotrophic cycle of mosquitoes. The dataset for our problem came from 125 adult female mosquitoes - each of which belonged to one of three species - Aedes aegypti, Culex quinquefasciatus, and Anopheles stephensi. The mosquitoes were raised in a lab and passed through all fourGonotrophic stages (Un-fed, Fully-fed, Semi-gravid, and Gravid). At each stage, their images were captured on a plain background via a Xiaomi smartphone, resulting in a dataset of 1784 images. The images were then augmented using standard techniques to generate a larger dataset of 4000 images. We then trained multiple computer vision models for the problem of classifying Gonotrophic stages of mosquitoes. The accuracy of our models is very favorable and contextually relevant also. To the best of our knowledge, our work is the first to use computer vision techniques to identify stages of the Gonotrophic cycle of mosquitoes. We also present discussions on the practical impact of our study in this thesis.
Scholar Commons Citation
Kariev, Sherzod, "Automated Identification of Stages in Gonotrophic Cycle of Mosquitoes Using Computer Vision Techniques" (2021). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/9151
