Submitted to: Journal of Bionics
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/1/2008
Publication Date: 12/15/2008
Citation: Lan, Y., Zheng, X., Westbrook, J.K., Lopez, J., Lacey, R., Hoffmann, W.C. 2008. Identification of stink bugs using an electronic nose. Journal of Bionic Engineering. 5(Suppl. 1):doi:10.1016/S1672-6529(08)60090-6. Interpretive Summary: Stink bugs are economic pests in soybean, cotton, and many other crops, but their populations are often difficult to assess in the field. New approaches are needed to aid in the detection of stink bugs, which may reduce or eliminate laborious field scouting. Research was conducted to determine if computerized sensors (commonly called electronic noses or E-noses) that mimic human, animal, or insect sense of smell can detect airborne chemical compounds associated with stink bugs. The E-nose was able to detect the presence of stink bugs and also to differentiate between male and female stink bugs. However, the E-nose was unable to distinguish between different species of stink bugs. Results from this laboratory research may lead to use of E-noses that electronically detect the presence of insects in a field, which may help determine if insect infestations warrant a spray application to prevent damage to the crop.
Technical Abstract: Stink bugs are recognized as pests of several economically important crops, including cotton, soybean and a variety of tree fruits. Furthermore, stink bugs have become a major problem in current cotton varieties that incorporate the Bacillus thuringiensis toxin because of reduced use of insecticides that have provided coincidental control of these bugs. The Cyranose 320 was used for the classified investigation of stink bug. Stink bugs including males and females of the southern green stink bugs, Nezara viridula, were collected from crop fields around College Station, TX. We found that the young adults were very soft and did not release the alarm pheromone (defensive secretion). Mature stink bugs released the alarm pheromone when touched. Results showed that the released chemicals and chemical intensity are both critical factors, which determine the rate that the Cyranose 320 correctly identified the stink bugs. An iterative training process for the Cyranose 320 is the key to building a high-confidence predictive model. The Cyranose 320 showed significant potential in identifying stink bug species, and classifying stink bugs by gender.