Machines powered by deep learning and CNNs are inspired by the way the human eye and brain work and they overperform the humans on many visual tasks.
Research isn’t just for researchers. More than 80 % of the 430 million records from the Global Biodiversity Informatics Facility (GBIF) are from citizen scientists.
Imagine being able to identify insects by just photographing them with your smartphone!
To apply cutting-edge image recognition technology to a widespread, diverse, but poorly-known insect groups.
Think "face recognition" but with insects instead of faces.
The primary source of specimens for the image acquisition are museums world wide (identified specimens) and SMTP material, which includes over 50 millions of specimens in alcohol, sorted into 50 taxonomic fractions by experts.
High resolution images taken in lab settings (camera or microscope) but also smartphone images taken with or without attachable lenses.
If inevitable, this step will be done using OpenCV, an open source library for Computer Vision
To train the machine to recognise objects and details in the picture we need training data (images), powerful learning algorithms (DEEP LEARNING) and sufficient computational power (GPU).
Classification involves dividing the data into various interrelated classes.
To verify if the machine clasified images properly, identification is required for all the specimens used in the study.
Test the model on new identification task.
Swedish Museum of Natural History (NRM)
KTH Royal Institute of Technology
This project is part of the BIG4 global consortium.
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642241.