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LarvaCam: AI-Enabled Morphological Identification of Mosquito Larva​

Project Description:

LarvaCam is an artificial intelligence (AI) driven mosquito larva identification and classification system. The system utilizes imaging capabilities of smartphone devices in tandem with a hardware system to facilitate image capture of larva and a software application that guides users through the automated identification process.

Project Photo:

Multiple screenshots of LarvaCam, demonstrating the full hardware system, a user capturing an image of a mosquito larva, and the application’s output of the identified larval species.

LarvaCam is an integrated solution with a hardware system to facilitate image capture of larva and a software application to provide real-time species identification. With LarvaCam, identifying larvae is as easy as taking a picture.

Project Poster

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Student Team Members

Norah Crowley
John Cutrone
Catalina Muñoz Duhart
Madison Ferris
Scout Rice
Minh Tran

Course Faculty

Soumya Acharya
Youseph Yazdi

Project Mentors, Sponsors, and Partners

David Bigio Roitman
Paola Astrid Ríos Tapias
Margarita Arboleda Naranjo
Jinna Ceron
Dr. Neil Lobo
Michael MacDonald
Javier Patiño Jaramillo
Celene Paz
Sunny Patel
Dr. Douglas Norris
Marina Rincon Torroella
Andrés Vélez Mira
Jessica Salazar