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Open AccessArticle

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

1
United States Department of Agriculture, Agricultural Research Service Center for Medical, Agricultural and Veterinary Entomology (CMAVE), Gainesville, FL 32608, USA
2
Department of Entomology, Kansas State University, Manhattan, KS 66502, USA
3
School of Computer Science, Shaanxi Normal University, Xi’an 710119, China
4
Department of Agrotechnology, University of Thessaly, 41500 Larissa, Greece
5
Tropical Research and Education Center, Institute of Food and Agricultural Sciences, University of Florida, Homestead, FL 33031, USA
*
Author to whom correspondence should be addressed.
Academic Editor: George N. Mbata
Insects 2021, 12(3), 259; https://doi.org/10.3390/insects12030259
Received: 19 February 2021 / Revised: 4 March 2021 / Accepted: 5 March 2021 / Published: 19 March 2021
A variety of different acoustic devices has been commercialized for detection of hidden insect infestations in stored products, trees, and soil, including a recently introduced device demonstrated in this report to successfully detect rice weevil immatures and adults in grain. Several of the systems have incorporated digital signal processing and statistical analyses such as neural networks and machine learning to distinguish targeted pests from each other and from background noise, enabling automated monitoring of the abundance and distribution of pest insects in stored products, and potentially reducing the need for chemical control. Current and previously available devices are reviewed in the context of the extensive research in stored product insect acoustic detection since 2011. It is expected that further development of acoustic technology for detection and management of stored product insect pests will continue, facilitating automation and decreasing detection and management costs.
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. View Full-Text
Keywords: Sitophilus oryzae; Tribolium castaneum; abundance; population density; neural networks; machine learning Sitophilus oryzae; Tribolium castaneum; abundance; population density; neural networks; machine learning
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MDPI and ACS Style

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

AMA Style

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

Chicago/Turabian Style

Mankin, Richard; Hagstrum, David; Guo, Min; Eliopoulos, Panagiotis; Njoroge, Anastasia. 2021. "Automated Applications of Acoustics for Stored Product Insect Detection, Monitoring, and Management" Insects 12, no. 3: 259. https://doi.org/10.3390/insects12030259

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