AIMC Topic: Insect Control

Clear Filters Showing 1 to 10 of 15 articles

Novel automation, artificial intelligence, and biomimetic engineering advancements for insect studies and management.

Current opinion in insect science
Entomology has seen remarkable advancements through the integration of robotics, artificial intelligence (AI), and biomimetic engineering. These technological innovations are revolutionizing how scientists study insect behavior, ecology, and manageme...

Using AI to prevent the insect apocalypse: toward new environmental risk assessment procedures.

Current opinion in insect science
Insect populations are declining globally, with multiple potential drivers identified. However, experimental data are needed to understand their relative contributions. We highlight the sublethal effects of pesticides at field-relevant concentrations...

Trap colour strongly affects the ability of deep learning models to recognize insect species in images of sticky traps.

Pest management science
BACKGROUND: The use of computer vision and deep learning models to automatically classify insect species on sticky traps has proven to be a cost- and time-efficient approach to pest monitoring. As different species are attracted to different colours,...

The implementation of robotic dogs in automatic detection and surveillance of red imported fire ant nests.

Pest management science
BACKGROUND: The Red Imported Fire Ant (RIFA), scientifically known as Solenopsis invicta, is a destructive invasive species causing considerable harm to ecosystems and generating substantial economic costs globally. Traditional methods for RIFA nests...

Machine learning provides insights for spatially explicit pest management strategies by integrating information on population connectivity and habitat use in a key agricultural pest.

Pest management science
BACKGROUND: Insect pests have garnered increasing interest because of anthropogenic global change, and their sustainable management requires knowledge of population habitat use and spread patterns. To enhance this knowledge for the prevalent tea pest...

Detection and recognition of the invasive species, Hylurgus ligniperda, in traps, based on a cascaded convolution neural network.

Pest management science
BACKGROUND: Hylurgus ligniperda, an invasive species originating from Eurasia, is now a major forestry quarantine pest worldwide. In recent years, it has caused significant damage in China. While traps have been effective in monitoring and controllin...

An effective segmentation and attention-based reptile residual capsule auto encoder for pest classification.

Pest management science
PURPOSE: Insect pests are a major global factor affecting agricultural crop productivity and quality. Rapid and precise insect pest detection is crucial for improving handling and prediction techniques. There are several methods for pest detection an...

First use of unmanned aerial vehicles to monitor Halyomorpha halys and recognize it using artificial intelligence.

Pest management science
BACKGROUND: Halyomorpha halys is one of the most damaging invasive agricultural pests in North America and southern Europe. It is commonly monitored using pheromone traps, which are not very effective because few bugs are caught and some escape and/o...

Rapid and low-cost insect detection for analysing species trapped on yellow sticky traps.

Scientific reports
While insect monitoring is a prerequisite for precise decision-making regarding integrated pest management (IPM), it is time- and cost-intensive. Low-cost, time-saving and easy-to-operate tools for automated monitoring will therefore play a key role ...

Deep learning for automated detection of Drosophila suzukii: potential for UAV-based monitoring.

Pest management science
BACKGROUND: The fruit fly Drosophila suzukii, or spotted wing drosophila (SWD), is a serious pest worldwide, attacking many soft-skinned fruits. An efficient monitoring system that identifies and counts SWD in crops and their surroundings is therefor...