Plankton community ecology and ecosystem research is currently greatly hampered by the bottleneck of acquiring species-level information from the communities due to the slow analysis process with traditional microscopy methods. The FASTVISION consortium will combine front-line plankton imaging instrumentation and taxonomic expertise of the Finnish Environment Institute and the computer vision and image analysis excellence of the LUT University. The project will use digitized microscopy images of plankton provided by novel automated imaging instruments, to train image recognition software for species identification using efficient machine learning approaches that produce interoperable data across instruments and habitats. This big data of plankton community composition with an unprecedented high resolution will be applied to experimental and field tests of key hypotheses in plankton community ecology, biodiversity and ecosystem functioning.

The FASTVISION project is funded by Academy of Finland. It is a collaborative project between the LUT University and Finnish Environment Institute.

LUT University    SYKE       AKA


Tuomas Eerola E-mail Office: 2425 Tel: +358 50 404 2054 Post-Doctoral Researcher, Project Manager
Heikki Kälviäinen E-mail Office: 2422 Tel: +358 40 586 7552 Professor, Project Leader
Lasse Lensu E-mail Office: 2420 Tel: +358 40 759 1720 Professor
Heikki Haario E-mail Office: 2412 Tel: +358 400 814 092 Professor


Plankton recognition from imaging flow cytometer data using convolutional neural networks

By Osku Grönberg, Master's thesis, LUT University, 2019.

In the master's theis, a deep convolutional neural network based classification method is evaluated on plankton images collected using imaging flow cytometer.

Download: URN