Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management

Mankin, Richard; Hagstrum, David; Guo, Min; Eliopoulos, Panagiotis; Njoroge, Anastasia

Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management

License

https://creativecommons.org/licenses/by/4.0/

Type

Article

Authors

Mankin, Richard

Hagstrum, David
Guo, Min
Eliopoulos, Panagiotis

Njoroge, Anastasia

KAUST Grant Number

58-6036-8-024F

Date

2021-03-19

Abstract

Acoustic technology provides information difficult to obtain about stored insect behavior, physiology, abundance, and distribution. For example, acoustic detection of immature insects feeding hidden within grain is helpful for accurate monitoring because they can be more abundant than adults and be present in samples without adults. Modern engineering and acoustics have been incorporated into decision support systems for stored product insect management, but with somewhat limited use due to device costs and the skills needed to interpret the data collected. However, inexpensive modern tools may facilitate further incorporation of acoustic technology into the mainstream of pest management and precision agriculture. One such system was tested herein to describe Sitophilus oryzae (Coleoptera: Curculionidae) adult and larval movement and feeding in stored grain. Development of improved methods to identify sounds of targeted pest insects, distinguishing them from each other and from background noise, is an active area of current research. The most powerful of the new methods may be machine learning. The methods have different strengths and weaknesses depending on the types of background noise and the signal characteristic of target insect sounds. It is likely that they will facilitate automation of detection and decrease costs of managing stored product insects in the future.

Citation

Mankin, R., Hagstrum, D., Guo, M., Eliopoulos, P., & Njoroge, A. (2021). Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management. Insects, 12(3), 259. doi:10.3390/insects12030259

Acknowledgements

This research was funded in part by a grant from King Abdullah University of Science and Technology (Agreement #58-6036-8-024F, Saudi Arabia), and was supported by the Fundamental Research Funds for the Central Universities (Grant No.GK202105006, China).