TY - JOUR
T1 - Camera-based automated monitoring of flying insects (Camfi). I. Field and computational methods
AU - Wallace, Jesse Rudolf Amenuvegbe
AU - Reber, Therese Maria Joanna
AU - Dreyer, David
AU - Beaton, Brendan
AU - Zeil, Jochen
AU - Warrant, Eric
N1 - Publisher Copyright:
Copyright © 2023 Wallace, Reber, Dreyer, Beaton, Zeil and Warrant.
PY - 2023
Y1 - 2023
N2 - The ability to measure flying insect activity and abundance is important for ecologists, conservationists and agronomists alike. However, existing methods are laborious and produce data with low temporal resolution (e.g. trapping and direct observation), or are expensive, technically complex, and require vehicle access to field sites (e.g. radar and lidar entomology). We propose a method called “Camfi” for long-term non-invasive population monitoring and high-throughput behavioural observation of low-flying insects using images and videos obtained from wildlife cameras, which are inexpensive and simple to operate. To facilitate very large monitoring programs, we have developed and implemented a tool for automatic detection and annotation of flying insect targets in still images or video clips based on the popular Mask R-CNN framework. This tool can be trained to detect and annotate insects in a few hours, taking advantage of transfer learning. Our method will prove invaluable for ongoing efforts to understand the behaviour and ecology of declining insect populations and could also be applied to agronomy. The method is particularly suited to studies of low-flying insects in remote areas, and is suitable for very large-scale monitoring programs, or programs with relatively low budgets.
AB - The ability to measure flying insect activity and abundance is important for ecologists, conservationists and agronomists alike. However, existing methods are laborious and produce data with low temporal resolution (e.g. trapping and direct observation), or are expensive, technically complex, and require vehicle access to field sites (e.g. radar and lidar entomology). We propose a method called “Camfi” for long-term non-invasive population monitoring and high-throughput behavioural observation of low-flying insects using images and videos obtained from wildlife cameras, which are inexpensive and simple to operate. To facilitate very large monitoring programs, we have developed and implemented a tool for automatic detection and annotation of flying insect targets in still images or video clips based on the popular Mask R-CNN framework. This tool can be trained to detect and annotate insects in a few hours, taking advantage of transfer learning. Our method will prove invaluable for ongoing efforts to understand the behaviour and ecology of declining insect populations and could also be applied to agronomy. The method is particularly suited to studies of low-flying insects in remote areas, and is suitable for very large-scale monitoring programs, or programs with relatively low budgets.
KW - Camfi
KW - computer vision
KW - flight behaviour
KW - image analysis
KW - insect conservation
KW - insect ecology
KW - population monitoring
KW - remote sensing
UR - http://www.scopus.com/inward/record.url?scp=85173000083&partnerID=8YFLogxK
U2 - 10.3389/finsc.2023.1240400
DO - 10.3389/finsc.2023.1240400
M3 - Article
SN - 2673-8600
VL - 3
JO - Frontiers in Insect Science
JF - Frontiers in Insect Science
M1 - 1240400
ER -