Autonomous Camera Traps for Insects / Feed

Camera trapping for insects is becoming a reality using advances in camera, AI, and autonomous systems technologies. This group discusses the latest advances, shares experiences, and offers a space for anyone interested in the technology, from beginners to experts.


Project introductions and updates

Tell us about your project!If you are just starting out with autonomous camera traps for insects, or if you are a seasoned expert, this is the place to share your...

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Diopsis insect camera

The DIOPSIS (Digital Identification Of Photographically Sampled Insect Species) project started in 2018 with the aim of exploring the possibilities for automated observations of insects. So far, the project has been successful in establishing a standardized method for photographing insects with an autonomous camera, which has been demonstrated at a large number of sites across the Netherlands.

The Diopsis camera is a smart camera system to monitor flying insect populations. The system includes a yellow screen with an ultraviolet layer that attracts insects. The camera takes a picture when insects are detected on the screen as well as at intervals of 8-15 seconds, both during the day and at night. Images can be stored on the camera, but can also be sent to a server via 4G. The camera is powered by a battery that can be connected to a solar panel. Due to these features, the camera works completely autonomous even in remote locations. The Diopsis camera can be purchased or leased from Faunabit BV.

Specialised image recognition software, developed by Naturalis and Cosmonio/Intel, is used to analyse the images for species identification and biomass estimations. Entomologists from EIS Kenniscentrum Insecten provide annotations for newly collected images. Currently, the software is trained for species in the Netherlands, but this can be expanded with annotated data from other countries as well.

Diopsis camera v2Specialised image recognition software is used for detection, species identification and biomass estimates

Currently, 150 cameras are deployed during the summer months throughout the Netherlands in natural, urban and agricultural locations. For each location, a report with results is available, and a nationwide ecological analysis of the results is conducted by researchers from the Radboud University Nijmegen.

The ambition of DIOPSIS is to create a monitoring network of insect cameras throughout the Netherlands (and beyond) in order to map the long-term trend of insects. We want to know whether the decline of insects continues and whether there are groups of insects that are doing better or worse than average. Besides this general monitoring goal, the cameras can also be used for targeted research.

Diopsis is a collaboration of Naturalis, Faunabit, EIS, Radboud University and Cosmonio/Intel.

For more information, visit the website or sent me a message!


Easy RIDER: Real-time IDentification for Ecological Research and Monitoring

Easy RIDER is a networking project funded by the UK Natural Environment Research Council for two years (2022 and 2023) to bring together complementary expertise to develop automated sensors using computer vision and deep learning to monitor insects.  So far, we have run a series of webinars:

Workshop I: Automated moth monitoring deployments | WILDLABS

Workshop II: Assessing automated insect monitoring | WILDLABS

Workshop III: Designing machine learning tools to process camera trap data automatically | WILDLABS

We held a face-to-face meeting in May 2022, hosted by the marvelous and inspiring Montreal Insectarium.  We are also field testing the AMI camera systems in multiple locations (e.g. Canada, Denmark, UK and USA) and plan to extend this to Panama in early 2023.

This is all part of our project plan (see figure below) to gain experience of running camera systems, and build a wider partnership of individuals and organisations interested in this exciting and emerging technologies. 

Easy RIDER plan

We are delighted to have this WildLabs group to help us reach new audiences and to share Easy RIDER  activities and to advertise opportunities and outputs.




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Identify animal from Image

I am thankful to the members of Wildlabs net for giving us the right information to enable us to plan Bioacoustics solution implementation. It seems to be on track as of now....

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Hi Jitendra.

If they are still images, many people are using Megadetector to analyze their images. I'm not sure how it will do in species classification, but it can tell you if there are images of interest in the shots. Others here can probably give you more detailed instructions on how to use it to batch process camera trap images.


Have you considered creating a Kaggle competition? If you already have lots of images, and some that have been labelled, then this could be a good way to get people working on a solution

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Welcome to the Autonomous camera traps for insects group!

Hello and welcome to the Autonomous Camera Traps for Insects group :).In this group we will be discussing the use of autonomous camera traps as a tool for long-term remote...

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Hi all, I am Joe Bowden, a research scientist with Natural Resources Canada, Canadian Forest Service, where I study (among other things) macromoth biodiversity across ecological gradients and human augmented landscapes. I have been working with the AMI group with the goal of its end use...I see great potential in the use of these systems for many agencies, especially those with mandates like mine to understand forest insects (e.g., irruptive, non-native species, rare species, biodiversity etc...). I have been running two of these units here in Newfoundland, Canada for over a month now (our summers are not super long ; )), and will be contributing data to aid in their further development. For me, the dream is edge processing of data such that we could have these systems deployed in various locations and the data can be obtained by anyone at anytime. 

Hi all,

I am Chantal Huijbers, working at Naturalis Biodiversity Center in the Netherlands. I have a background in biology, but am now working as a team lead in the ARISE program to develop a national infrastructure for biodiversity monitoring and digital species identification. One of my main goals is to translate the needs of the research community to IT specialists, so that we can develop the tools to make data better available to the wider research community. 

Besides my work for ARISE, I am also the project lead for the Diopsis insect camera project, which was established to monitor insect populations in the Netherlands. Check out my post in the Project Introductions to read more about this!


I am David Roy, working at the UK Centre for Ecology & Hydrology.  I am an ecologist and most of my career has been working on biodiversity monitoring schemes, often using citizen science approaches.  I am the head of the Biological Records Centre which has supported wildlife recording in the UK for almost 60 years.

I am particularly interested in lepidoptera and am excited by the potential of automated camera systems to better monitor their status around the world. I am one of the leads of the Easy RIDER project

I look forward to learning a lot from this group!

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