SBIR-STTR Award

Foundational to assessing biodiversity is an effective and cost-efficient method to identify and quantify the components of biodiversity. While a substantial proportion of present and proposed biofuel production systems are terrestrially based, the environmental impacts wash down the watershed into the aquatic ecosystem. Biodiversity monitoring can therefore be efficiently focused on surface waters draining the watershed, but it is first necessary to design and build modern sampling methodologies with which to collect data on biodiversity status and trends. The objective of this Phase I project is to modernize methods for identification and enumeration of organisms sampled from surface waters by advancing the design and operational range of an optical image acquisition and classification (OIC) system. Despite its potential to increase sample precision, reduce processing time and better manage costs, OIC has not achieved its potential in most sectors for which commercial opportunity exists. This opportunity will be addressed by designing, building, and testing a portable OIC system that will support spatially and temporally resolved sample collection and real-time identification and enumeration, allowing adaptive sampling to ensure acquisition of statistically robust data for the milieu of common and rare organisms of which biodiversity is composed. A portable OIC unit capable of imaging and classifying lentic and lotic organisms ranging in maximum cross-section from ~1-25 mm will be designed and built. Unit capabilities will include remote operation, internal data logging, a portable power supply, and robust construction to ensure operability in hostile field environments. External but complementary to the unit will be a sampling apparatus capable of extracting organisms from complex benthic environments in a predictable manner while preventing entry of potentially damaging non-biological items. Domestically and globally, thousands of streams and lakes are monitored to characterize biodiversity status and trends. In most cases, sampling is conducted using archaic net sampling techniques that require expensive manual sorting and identification and that result in poorly resolved and time-delayed data outcomes. Industry, government agencies, and commercial organizations that conduct these sampling efforts will benefit from reduced sample processing costs and more rapid data acquisition resulting from an OIC-based tool. The public will benefit from enhanced temporal and spatial data outcomes, allowing improved understanding of biodiversity status and trends and thereby better environmental decision-making. A successful Phase I project will lead to the development of a Phase II commercialization plan to achieve a supportable price point resulting in a multi-million-dollar revenue stream based on unit sales, system upgrades, and service.

Biofuels offer a promising alternative to fossil fuels if costs can be reduced, but biofuel farm production activities may impact biodiversity. Presently available methods for monitoring biodiversity trends are outdated and expensive. New methods are needed that are more efficient and less costly. Reducing these costs is of interest to the Department of Energy and the general public as a means to reduce overall biofuel production costs and environmental impacts, thereby providing a cost-effective and relatively clean alternative to fossil fuels. A substantial proportion of present and proposed biofuel production operations are terrestrially based, but the environmental impacts wash down the watershed into the aquatic ecosystem. Biodiversity monitoring can therefore be efficiently focused on surface waters draining the watershed, but it is first necessary to develop modern sampling methods to collect data on biodiversity trends in aquatic environments. The project objective is to modernize methods for identification and enumeration of ecological indicator organisms sampled from aquatic environments by advancing the design and operational range of an optical image acquisition and classification system. This is being accomplished by designing, building and testing the system components, including the imager, sampling equipment, and software, within the context of customer needs. Phase I advanced the prototype imaging unit, developed novel sampling designs for surface waters such as streams and lakes, evaluated approaches to configure an instrument that can be used in the field or lab, and conducted interviews with potential customers to ensure compatibility between design and need. An initial prototype imaging unit will be completed during this project phase, and equipment to collect samples and deliver them through the imaging unit have been designed, built, and tested. Interviews with potential customers revealed that biological sensing systems are the “holy grail” of environmental sensing, with substantial support for a relatively inexpensive system that can integrate with data from other sensors in an easy-to-use package. During the next project phase, system hardware development and testing will continue, the manufacturing design of system components finalized, the control and classification software advanced using deep-learning programming, a cloud-based image library implemented, and a most-viable-product cooperatively tested with early adopters, resulting in a system with cost and performance features that ensure customer acceptance. Waterways are monitored nationwide to characterize biodiversity trends. Thousands of industries, government agencies, and commercial organizations conduct sampling and are willing to pay for a cheaper, faster method. Optical imaging meets this need. The biofuel industry will benefit from reduced costs. The public will benefit from better understanding of biodiversity trends. Economically, the environmental sampling industry is growing at 10% annually, ensuring financial viability of this endeavor.