SBIR-STTR Award

The broader impact of this Small Business Innovation Research (SBIR) Phase I project will result from the development of new tools to prevent future mosquito disease outbreaks. Most US mosquito control organizations lack the capability or capacity to conduct routine mosquito surveillance, a necessary task for effective mosquito control. Globally, 80% of the world population is at risk for mosquito-borne disease, and mosquito surveillance, monitoring, and evaluation are widely recognized as critical public health activities o be scaled globally. The technology developed through this proposal will develop and demonstrate the feasibility of new identification methods to reduce operational mosquito surveillance costs, while improving accuracy and standardization of data. The result will be improved decisions that will reduce the incidence of mosquito-borne diseases. This Small Business Innovation Research (SBIR) Phase I project will build on advances in computer vision for high accuracy identification of mosquito species in operational contexts. While high accuracy classification of mosquito species has been demonstrated using deep convolutional neural networks (CNNs), evidence has been limited to controlled laboratory environments, with small datasets of few species, or with lab reared specimens. Operational environments face a significantly more complex problem, with hundreds of potential species that may be encountered, and variation in morphology and quality of wild-caught mosquitoes. This proposal seeks to overcome and mitigate the core technical challenges unaddressed by the current state of research, including: fine-grain classification techniques required to distinguish medically relevant species from over three thousand mosquito species found in nature with significant overlapping morphology, novel species detection methods to identify when a presented specimen is from species unknown to the species classification algorithms, and characterizing the feeding state and the physical quality of specimens, such as damage to wings, legs, scales, and body. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

The broader impact of this Small Business Innovation Research (SBIR) Phase II project is to enable the provision of high quality vector surveillance data to public health institutions domestically and internationally. Vectors, or organisms that transmit diseases to other organisms, like mosquitoes and ticks, have a significant impact on human health and agriculture, with associated mortality and morbidity. This project aims to advance artificial intelligence methods to identify mosquito species from high resolution images. While well studied and documented, mosquito species identification remains a highly skilled task, where the few capable of this skill for a given region often have many other job responsibilities, making time devoted to the laborious task of mosquito identification difficult to justify at scale, despite the necessity of the data created. This project and its derivative works will enable organizations without this skill in-house to acquire this highly valuable data. The solution will also allow organizations with this skill in-house to task shift identification to seasonal technicians, and field a larger dataset. This larger dataset would enable better decision making for the control of mosquito borne disease. If successful, these methodologies can be translated to other vectors for disease, further benefiting public health.This Small Business Innovation Research (SBIR) Phase II project is centered around the problem of mosquito species identification. There are more than 3,000 species of mosquitoes in the world, each with different behaviors and capacities for carrying disease. Regionally trained taxonomic experts can identify them through visual inspection, but there is a shortage of such experts. Some artificial intelligence (AI) methods for image-based identification have already been developed, but they are only designed for a limited number of species and face issues due to complex mosquito morphology and the variability incurred in practical use by vector control organizations. This project seeks to enhance existing methodologies for artificial intelligence (AI)-based insect identification by making use of generative models to address issues in training datasets caused by sampling biases. These models will be used to modulate the presence of underrepresented attributes to make a more robust and less biased model. The generative models used for this task will also be used to translate the data for viability in one constrained image domain to another. The final task is to use these models to modulate the training datasets for closely related mosquito species to fine tune performance for minute, but important, distinctions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.