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
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.
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.
University of South Florida