Location: Insect Behavior and Biocontrol ResearchTitle: Automated applications of acoustics for stored product insect detection, monitoring, and management
|HAGSTRUM, DAVID - Kansas State University|
|GUO, MIN - Shaanxi Normal University|
|ELIOPOULOS, PANAGIOTIS - University Of Ioannina|
|NJOROGE, ANASTASIA - University Of Florida|
Submitted to: Insects
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 3/5/2021
Publication Date: 3/19/2021
Citation: Mankin, R.W., 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. https://doi.org/10.3390/insects12030259.
Interpretive Summary: Stored product insects are major contributors to economic losses to stored grains and processed foodstuffs. Scientists at the USDA-ARS Center for Medical, Agricultural, and Veterinary Entomology, Gainesville, FL, Kansas State University, Manhattan, KS, Shaanxi Normal University, Xi'an, China, the University of Thessaly, Larissa Greece, and the University of Florida, Homestead, FL, conducted studies to acoustically detect stored product insects with newly developed automated acoustic sensors, replacing equipment that is no longer commercially available. The results of the study indicate that hidden insect infestations in grain can be detected easily by the new system, and that the information can be incorporated into automated management systems. The future of automated insect acoustic detection systems for stored product insect management is discussed.
Technical 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 types of background noise and 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.