Pest control with drones and deep learning

Adoption of control techniques that can be customized to specific local conditions begins with early detection of pest infestations. Although remote sensing technology is useful for quickly scanning large areas, it is not always suitable because some objects are difficult to locate.

Nest of a pine crawler moth. Image credit: Garcia et al.

On the other hand, drones or unmanned aerial vehicles (UAVs) can help by getting close to individual trees and detecting small, unusual signals.

The pine crawler moth is a pest that attacks trees in gardens and parks and is a public health concern because the hairs emitted by its larvae sting or irritate. The pest is a species of concern due to its rapid population growth and expansion into new areas.

Researchers have explored several deep learning techniques to detect nests of pine crawler moth larvae in cedar and pine trees. Artificial intelligence (AI) was used to detect and identify nests using images taken by drones flying above the trees.

The use of AI on drone images was successful in detecting pine processing moth nests in trees of different types and sizes, even under different densities. This technique works well for detecting moth nests in urban and rural areas.

By knowing where nests are located, tree health managers can take the necessary precautions to limit damage and public health threats.

The study authors stated, “The study demonstrated the advantage of using UAVs to document the presence of at least one nest per tree. Therefore, it represents a significant step forward in the integration of UAV surveys with ground observations in monitoring colonies of an important deforestation pest in the Mediterranean region.

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The study was included in the journal NeobiotaSpecial Edition on European Forest Insects.

They also suggested that this technique could be applied to different insects.

The authors concluded, “This technique can lead to new ways of monitoring and managing emerging and non-native pests of trees, where early detection and early action must go hand in hand to achieve a satisfactory protection.

Journal Note:

Garcia, A., and many others. (2023) Early detection of pine crawler moth Thaumetopea pityocampa nests using UAV-based methods. Neobiota. doi:10.3897/neobiota.84.95692.


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