Identification and Classification of Invasive Insects Using Audible Signals

USDA has a goal to enhance detection and response capabilities in controlling destructive pests like the Mediterranean fruit fly. Currently, about a hundred thousand Medfly traps exist in southern California alone that requires USDA personnel to manually check for the existence of the target insect. This labor-intensive activity carries significant recurring costs. Moreover, any delays in the identification of an infestation can quickly produce millions of dollars of damage to crops. We submit that an effective electronic solution to this problem would have many important benefits: 1) reduce costs for monitoring for target insects; 2) dramatically improve response times when a problem occurs; 3) reduce damage to crops; 4) geographically isolate the problem areas for proper response (sterile release, pesticides) trapping, and so on. The purpose of this project is to design and develop a system that can automatically identify targeted classes of invasive flying insects, such as Medflies, using their audible wing-beat patterns. We propose a novel solution to this problem using electronic sensing (microphone and data processing) in a wireless sensor network, dubbed InsectNet, integrated with modern Medfly traps. The sensor nodes will also be endowed with wake-sleep behavior to minimize communications and conserve energy.