Work

Towards real-time monitoring of insect species populations

Year: 2024

Type: article

Abstract: Insect biodiversity and abundance are in global decline, potentially leading to a crisis with profound ecological and economic consequences. Methods and technologies to monitor insect species to aid in preservation efforts are rapidly being developed yet their adoption has been slow and focused on specific use cases. We propose a computer vision model that works towards multi-objective insect species identification in real-time and on a large scale. We leverage an image data source with 16 million instances and a recent improvement in the YOLO computer vision architecture to present a quick and open-access method to develop visual AI models to monitor insect species across climatic regions.

Source: Scientific Reports

DOI: 10.1038/s41598-024-68502-8

Authors: Titus Venverloo, Fábio Duarte

Institutions: Amsterdam Institute for Advanced Metropolitan Solutions, Massachusetts Institute of Technology

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Life on land

Open Access status: gold

Estimated APC paid: $2,590