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.USF inventors have developed a method for automatic identification of mosquito genus, species, and anatomy from smart-phone images. The method involves anyone (including untrained personnel) to take pictures of mosquitos with smartphones, the species type is identified, and images and population results are automatically uploaded to a cloud system. Through the use of this digital image processing and a deep learning application, everyone across the globe could take a picture of dead and physically un-deformed mosquitos. Therefore, the species would be identified immediately and reported to the Mosquito Control Board remotely. This invention is suitable for application in vector control programs, health organizations, and pest control industries. It is a fast and cost-effective method which takes advantage of everyone’s access to a smartphone. The efficiency of the system will improve with usage and population.
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