[PDF] Perspective and Promise: a Century of Insect Acoustic Detection and Monitoring | Semantic Scholar (2024)

A recently introduced device was demonstrated in this report to successfully detect rice weevil immatures and adults in grain, enabling automated monitoring of the abundance and distribution of pest insects in stored products, trees, and soil and potentially reducing the need for chemical control.

Potential areas for growth in the use of acoustic technology in pest management include the production of signals that disrupt vibrational communication, particularly in the Hemiptera, and the development of control treatments that combine pheromones and precisely patterned sonic or vibrational signals.

This review paper aims to explore the relative technologies and find a sensing method that has potential to detect and identify common invertebrates on crops, such as butterflies, locusts, snails and slugs.

A sound reinforced piezoelectric sensor along with a detection circuit based on a database was designed in order to receive audio signals with intensity lower than human hearing limit (zero dB), designed to detect larvae, adult, or both at a distance of 30 cm.

A remotely controlled device that records and wirelessly transmits on a scheduled basis short recordings of the internal vibrations of a tree to a server to infer the infestation state of the tree with wood-boring insects that feed or move inside the tree.

Differences in the rates of sounds per insect in treatments with different numbers ranging from 5 to 50 insects suggested that the sound rates of adults of different species at different population densities may be noticeably affected by aggregation pheromones or other behaviorally active semiochemicals.

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A multi layered enclosure was constructed that attenuates sound by 70–85 dB between 1 and 10 kHz to enable detection of internally feeding larvae in grain samples at inspection facilities at commercial grain elevators, which have 50–80 dB SPL noise backgrounds.

To reliably count insects with widely varying sound production patterns, the sound pattern identification algorithm needs to self correct, depending on the input received from the grain sample, and the cluster size, the number of contiguous Ist:2nd detection pairs, was proportional to insect weight.

The focus of this task was to develop a novel detection system utilising incidental acoustic signal of grain insects at their active developmental stages (larva or adult stage) using a reliable transducer or insect noise spectra to an artificial intelligence tool enabling specific sound signature recognition.

Cowpea weevil, Cal/osobruchus macuJatus (E), developing in cowpea seeds as a model, is used to detect feeding activity by monitoring ultrasonic emissions at 40 KHz.

Recent experiments with currently available acoustic systems to assess the detectability and interpretability of sounds produced by D. abbreviatus larvae and other organisms in root systems of individual trees in citrus groves confirmed that such instruments could successfully predict the presence or absence of subterranean insects under individual trees.

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