SBIR Phase I: A Multimodal Sensor Platform for Automated Detection and Classification of Pest Insects

This Small Business Innovation Research (SBIR) Phase I project proposes to develop a multi-modal sensor platform for robust detection and classification of insect pests for automated monitoring of insect populations. More specifically, the project will investigate the feasibility of extracting meaningful features of insect pests using a set of low-cost, low-power sensors?namely ultrasound, infrared light and bio-impedance sensors?and develop multi-modal sensor fusion algorithms for robust detection and classification of multiple target insect species against any non-target organisms that may enter the trapping device. Measurement signals generated by the sensors will be analyzed to determine a set of distinct features that can be computed on an embedded platform for real-time processing. The critical requirements of employing low-cost, low-power sensors with real-time processing capability make this research unique and challenging. Different sensor fusion strategies based on these features will be investigated and the performance of each fusion algorithm will be evaluated. Successful conclusion of Phase I and II of this project will result in fully automated, wireless insect traps capable of providing accurate, real-time populations of multiple pest species. The broader impact/commercial potential of this project is that the proposed system could revolutionize pest management practices in agriculture and drastically reduce the amount of pesticide applications. The proposed system not only eliminates one of the most laborious and dreaded activities of manually inspecting insect traps, but also provides unprecedented access to accurate, real-time insect population information to make more effective pest management decisions. This leads to reduced, spatially-restricted pesticide applications, better understanding of insect pest behaviors, and enhanced biological control. The potential market for the proposed technology is quite broad. In fact, the technology can benefit any industry that requires regular monitoring of insect populations such as tree fruits (e.g., apple, grape, citrus, pear, etc.), row crops (e.g., corn, soy bean, sugar cane, etc.), vegetables, nuts, coffee, ornamental trees, stored food, etc. Furthermore, the proposed technology could be used for various state and federal pest monitoring programs?such as the Slow the Spread (STS) Project administered by USDA to monitor gypsy moths?that requires placing and managing up to a few hundreds of thousands of pheromone traps annually.