Publications by authors named "K Bjerge"
Towards a standardized framework for AI-assisted, image-based monitoring of nocturnal insects.
Philos Trans R Soc Lond B Biol Sci
June 2024
Article Synopsis
- Automated sensors can enhance global monitoring of nocturnal insects by providing a systematic approach from device development to data processing.
- These systems use lights to attract insects, cameras to capture images, and computers for managing data and processing schedules, while metadata helps streamline sampling efficiency.
- Effective data processing techniques, including bias correction in species detection, are essential to cope with the large volumes of images collected, emphasizing the need for improved monitoring strategies to address biodiversity loss.
As pollinators, insects play a crucial role in ecosystem management and world food production. However, insect populations are declining, necessitating efficient insect monitoring methods. Existing methods analyze video or time-lapse images of insects in nature, but analysis is challenging as insects are small objects in complex and dynamic natural vegetation scenes.
View Article and Find Full Text PDFArticle Synopsis
- High-resolution monitoring is crucial for understanding ecosystem dynamics amidst global changes and biodiversity loss, especially for tracking biotic components like species behaviors and abundances.* -
- Recent advancements in affordable recording hardware and accessible AI technologies are enabling better data collection and analysis for ecological monitoring.* -
- This text reviews existing technologies and proposes new frameworks to create automated systems that can effectively track and analyze multiple species and their traits at unprecedented resolutions.*
Image-based methods for species identification offer cost-efficient solutions for biomonitoring. This is particularly relevant for invertebrate studies, where bulk samples often represent insurmountable workloads for sorting, identifying, and counting individual specimens. On the other hand, image-based classification using deep learning tools have strict requirements for the amount of training data, which is often a limiting factor.
View Article and Find Full Text PDFClimate change is rapidly altering the Arctic environment. Although long-term environmental observations have been made at a few locations in the Arctic, the incomplete coverage from ground stations is a main limitation to observations in these remote areas. Here we present a wind and sun powered multi-purpose mobile observatory (ARC-MO) that enables near real time measurements of air, ice, land, rivers, and marine parameters in remote off-grid areas.
View Article and Find Full Text PDF