Document Type
Article
Publication Date
November 2022
Patent Number
11501113
CPC
G06K 9/6259, G06K 9/00677, G06K 9/2054, G06K 9/4604, G06K 9/4652, G06K 9/6218, G06K 9/6228, G06V 10/44, G06V 20/00, G06V 10/22, G06V 10/56, G06V 20/30
Abstract
Identifying insect species integrates image processing, feature selection, unsupervised clustering, and a support vector machine (SVM) learning algorithm for classification. Results with a total of 101 mosquito specimens spread across nine different vector carrying species demonstrate high accuracy in species identification. When implemented as a smart-phone application, the latency and energy consumption were minimal. The currently manual process of species identification and recording can be sped up, while also minimizing the ensuing cognitive workload of personnel. Citizens at large can use the system in their own homes for self-awareness and share insect identification data with public health agencies.
Application Number
17/168941
Recommended Citation
Chellappan, Sriram; Bharti, Pratool; Minakshi, Mona; McClinton, Willie; and Mirzakhalov, Jamshidbek, "Leveraging smart-phone cameras and image processing techniques to classify mosquito genus and species" (2022). USF Patents. 1311.
https://digitalcommons.usf.edu/usf_patents/1311
Assignees
University of South Florida
Filing Date
02/05/2021
