Project Summaries

12-377  Project Manager: P. F. O'Leary

SENSOR-BASED TECHNOLOGY FOR DETECTION AND SITE-SPECIFIC MANAGEMENT OF STINK BUGS IN COTTON

Ahmad Khalilian, Clemson University

The chemical sensory tool trials are in progress.  In 2012, a prototype stink bug damage detector (SBDD) was built.  The SBDD pumps volatiles over sensor arrays composed of polymer-carbon black composites, and the percent change in resistance is calculated by dividing the difference in the maximum and initial resistances by the initial resistance.   In June 2012, the SBDD was refined by incorporating a smaller battery and microcontroller to decrease weight and size, and the sampling cycle was optimized to reduce testing time from >20 s to 9 s.  Also, the SBDD was used to test for sensor response to volatiles released by stink bugs. Three replicates of three sensor arrays were constructed.  Each sensor array was comprised of four different polymer-carbon black composite sensors.  Each array was exposed to following volatiles replicated three times: a) an empty 15 mL polystyrene tube, b) a polystyrene tube containing one female southern green stink bug, and c) a polystyrene tube containing one male southern green stink bug. The percent change in resistance was used to identify the stink bug volatiles from the control. The SBDD will be tested for detecting damaged cotton bolls in summer of 2012.

Also, substrate-borne "songs" from brown and southern green stink bugs were investigated. The temporal and spectral characteristics of these songs were used to identify and detect these insects.  The duration, dominant frequency, and linear frequency cepstral coefficients (LFCCs) were calculated and used to create a Gaussian mixture model (GMM) and probabilistic neural network (PNN).  The GMM and PNN were used to detect substrate-borne songs of the brown and southern green stink bugs as well as identify the species from which the songs originated.

 

Project Year: 2012
 

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