This five-year project finished in mid-2022.
Many insects are common pests in horticulture, agriculture, and forestry sectors, posing biosecurity risks to trading countries worldwide. Efficient species-specific semiochemical lures are available for some of these pests, facilitating the implementation of post-border surveillance programmes via trapping networks. These networks have a long history of success in detecting incursions of invasive species; however, their reliance on manual trap inspections makes these post-border surveillance programmes expensive to run. Novel smart traps integrating sensor technology are being developed to detect insects automatically but are so far limited to expensive camera-based sensors or optoelectronics sensors for fast-moving insects.
For this project, we have developed an optoelectronic sensor that can fit in different types of traps to record wing-beat frequencies of flying insects and send remotely the digital detection via wireless communication for real-time biosecurity alerts.
Our laboratory and field trials have proven that insects flying in/out of the trap can be detected automatically and generally before visual trap catch, thus improving earlier detection while decreasing significantly the cost of labour by targeting biosecurity efforts only in the location of detection. These new smart traps, combined with machine learning algorithms, can further facilitate diagnostics via species identification through biometrics.
The deployment of such smart traps for biosecurity provides a sustainable low-carbon solution with faster and earlier detection of invasive pests.
The Zotero database is on the B3 homepage under ‘Outputs’.