Automated Monitoring of Insects system: Building a non-lethal and scalable solution for monitoring of nocturnal insects
Lord, W.; Teagle, S.; Alton, J.; Bannister, G.; Beuchert, J.; Bjerge, K.; Howson, T.; Hoye, T.; Carbone, D.; Gomez Segura, A.; Lawson, J.; Ravivarma, A.; Roy, D.; Rylett, D.; Skinner, G.; Warwick, A.; August, T.
Show abstract
There is growing evidence that human-induced climate change and habitat loss are having negative impacts on insect populations. New technologies have a vital role in improving and expanding global biodiversity monitoring capacity to understand where change is happening and to support restoration. Monitoring of insects traditionally needs entomologists in the field, but insect camera traps powered by AI are emerging as a scalable approach to monitoring semi-autonomously. These systems attract, detect, and identify insects using AI algorithms and are being developed by a network of researchers across the world, notably in Europe and North America. The first version of a system for monitoring nocturnal insects was developed by Bjerge et al. 2021. An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning. Here we describe the second generation of the system as an open-source solution. This paper aims to enable anyone to build their own system, and to iterate and improve the design for their needs. This system captures images at set intervals or based on motion detection to monitor insects that are attracted to lights at night. The UKCEH Automated Monitoring of Insects System (UKCEH AMI-system) is an insect camera trap designed using a single board computer, USB camera and attractant lights as the primary components along with peripheral accessories to make an autonomous system capable of long-term deployment in the field. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=126 SRC="FIGDIR/small/704388v1_ufig1.gif" ALT="Figure 1"> View larger version (52K): org.highwire.dtl.DTLVardef@17ccdc4org.highwire.dtl.DTLVardef@a92179org.highwire.dtl.DTLVardef@1d4aaf8org.highwire.dtl.DTLVardef@1248d07_HPS_FORMAT_FIGEXP M_FIG C_FIG
Matching journals
The top 1 journal accounts for 50% of the predicted probability mass.
Similar papers in this journal
- Artificial intelligence tool for the study of COVID-19 microdroplet spread across the human diameter and airborne space 93%
- A low-cost smart system for electrophoresis-based nucleic acids detection at the visible spectrum 93%
- Implementing QR Codes in Academia to Improve Sample Tracking, Data Accessibility, and Traceability in Multicampus Interdisciplinary Collaborations 93%
Similar papers in this journal
- An automated light trap to monitor moths (Lepidoptera) using computer vision-based tracking and deep learning 93%
- Combining Indoor Positioning Using Wi-Fi Round-trip Time with Dust Measurement in the Field of Occupational Health 92%
- High-Throughput and Accurate 3D Scanning of Cattle Using Time-of-Flight Sensors and Deep Learning 91%
Similar papers in this journal
- ESPERDYNE: A Dual-Band Heterodyne Monitor and Ultrasound Recorder for Bioacoustic Field Surveys 94%
- Real-time alerts from AI-enabled camera traps using the Iridium satellite network: a case-study in Gabon, Central Africa 94%
- A wireless, user-friendly, and unattended robotic flower system to assess pollinator foraging behaviour 92%
Similar papers in this journal
- Scalable, effective, and rapid decontamination of SARS-CoV-2 contaminated N95 respirators using germicidal ultra-violet C (UVC) irradiation device 91%
- The optics of the human eye at 8.6 µm lateral resolution 91%
- A low power flexible dielectric barrier discharge disinfects surfaces and improves the action of hydrogen peroxide 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.