Title: Automatic detection and identification of brown stink bug, Euschistus servus, and southern green stink bug, Nezara viridula, (Heteroptera: Pentatomidae) using intraspecific substrate-borne vibrational signals Authors
Submitted to: Computers and Electronics in Agriculture
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: December 3, 2012
Publication Date: February 1, 2013
Citation: Lampson, B.D., Han, Y.J., Khaliliann, A., Greene, J., Mankin, R.W., Foreman, E.G. 2013. Automatic detection and identification of brown stink bug, Euschistus servus, and southern green stink bug, Nezara viridula, (Heteroptera: Pentatomidae) using intraspecific substrate-borne vibrational signals. Computers and Electronics in Agriculture. 91:154-159. Interpretive Summary: Stink bugs are increasingly agricultural pests. Scientists at the USDA Agriculture Research Service, Center for Medical, Agricultural, and Veterinary Entomology, Gainesville, FL, the Edisto Research and Education Center, Blackville, SC, and Clemson University, Clemson, SC, have investigated the vibratory communication of the brown stink bug and the southern green stink bug. These two pests of cotton are often found together in the southern US and it is important for management that they can be told apart. Statistical analyses were designed that enable the two insects to be distinguished on the basis of their communicatory vibrations.
Technical Abstract: Stink bugs cost the southeastern cotton industry millions of dollars each year in crop losses and control costs. These losses are reduced by strategic pesticide applications; however, current methods of monitoring these pests for making management decisions are time-consuming and costly. Therefore, improved methods to identify and monitor these bugs must be investigated in order to optimize pesticide applications. One such method would be to exploit the substrate-borne vibrational signals (SBVS) of these insects. Recordings of SBVS for the brown stink bug, Euschistus servus, and southern green stink bug, Nezara viridula, were segmented into separate pulses of variable duration based on signal energy.. For each pulse, the linear frequency cepstral coefficients, dominant frequency, and duration were calculated and used as features. These features were classified using a Gaussian mixture model (GMM) and a probabilistic neural network (PNN) to discriminate these SBVS from incidental sounds and SBVS of different species from each other. Detection of SBVS generated by brown stink bugs was performed with over 92% accuracy for single male-female pairs with both PNN and GMM and with over 86% accuracy for 30 individuals with both PNN and GMM. Detection of SBVS generated by southern green stink bugs was performed with up to 82.5% accuracy with PNN and 68.0% accuracy with GMM for 30 individuals. Also, both PNN and GMM were over 90% accurate in identifying SBVS of brown and southern green stink bugs. Concurrent detection of SBVS from noise and identification of SBVS of brown and southern green stink bugs was 83.3% accurate using PNN and 71.5% accurate using GMM. These results indicated the capability of detecting and identifying stink bug species using their SBVS.