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Device in the field: minimon setup with 3D printed flowers
Example image: view from the camera on the 3D printed flowers

The miniMon (mini–monitoring device) is a small-sized camera that can be used for monitoring insects and terrestrial arthropods.

The camera records timelapses of images at set intervals (between 30 seconds to 1 hour). 

Monitoring of day pollinators is done by either pointing the camera at flowers, or using the platform with 3D printed flowers. 

Monitoring of ground-dwelling arthropods is done by pointing the camera at the ground, and a small bathroom tile can be used to improve the visibility of the critters on the ground.

Insect detection is performed offline, by running off-the-shelves deep learning models on the computer.

The camera trap is waterproof and solar powered, enabling continuous monitoring throughout a whole season. The camera trap can be assembled at a low cost (~ 100 EUR), and the hardware, firmware and analysis software will be open-sourced by the end of 2026.

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Device in the field: minimon setup with white tile
Example image: view from the camera on the white tile

Technical details (hardware and software)

  • Processing unit: XIAO ESP32S3 (Sense)
  • Camera: OV5460 AF (5MP, auto focus)
  • Battery: 3.7V 18650 battery (>= 3000 mAh)
  • Power: 6W solar panel
  • Power consumption: ~ 24h on battery, indefinitely with solar panel.
  • Custom PCB: with power management, RTC, 2x I2C slots, external flash
  • Memory: microSD card (32 GB – 128 GB)
  • Background: 3D printed flowers, plain background or natural background.
  • Data processing: offline (i.e., external computer)
  • Components price: ~ 100 EUR

Offline data pipeline: the images are offloaded from the microSD card on the computer. The metadata is automatically converted to the CamtrapDP format. The images are first fed to ‘FlatBug’, which automatically detects insects in the images.
From there, the detected insects are cropped from the image, and fed to BioCLIP2, which can classify the insects at different taxonomic levels.

Contact:
Helena Russello
helena@qgg.au.dk

Quentin Geissman
qgeissmann@qgg.au.dk 

Developed at: Center for Quantitative Genetics and Genomics (QGG), Aarhus University, Denmark