Automatic Plankton Recognition


Earths oceans are a soup of living micro-organisms known as plankton. As the foundation of the food chain for marine life, plankton are also an integral component of the global carbon cycle which regulates the planet's temperature. The importance of plankton for the global ecosystem cannot be overestimated. Studying plankton is important to ecological research. For example, understanding the carbon cycle is necessary to be able to predict global climate changes. On a less global scale, studying plankton can allow marine biologists to create early warning systems for detecting harmful algal blooms in coastal waters. Applications in other fields could include ship ballast water treatment, drinking water treatment, public health, bio-terrorism defense, and industrial chemical processing.

There are several image acquisition tools such as the FlowCam and VPR (Video Plankton Recorder) that are capable of taking thousands of images in a very short period of time. Hand labeling this large quantity of images requires an enormous number of man hours. In collaboration with the Machine Learning Lab at UMass, Bigelow Lab for Ocean Sciences, and the Coastal Fisheries Institute at Louisiana State University we are automating this process by classifying plankton species from low-resolution images collected in-situ. For more information on the project, click here.


Graduate Students

Undergraduate Students

  • Steven Hannum




  • M. B. Blaschko.
    Support Vector Classification of Images with Local Features.
    M.S. Thesis, Department of Computer Science, University of Massachusetts Amherst, May 2005.
  • D. Lisin, M. Mattar, M. Blaschko, M. Benfield, and E. Learned-Miller.
    Combining Local and Global Image Features for Object Class Recognition.
    IEEE Workshop on Learning in Computer Vision and Pattern Recognition (in conjunction with CVPR), June 2005.
  • M. Sieracki, E. Riseman, W. Balch, M. Benfield, A. Hanson, C. Pilskaln, H. Schultz, C. Sieracki, P. Utgoff, M. Blaschko, G. Holness, M. Mattar, D. Lisin, and B. Tupper.
    Automatic Classification of Plankton from Digital Images.
    ASLO Aquatic Sciences Meeting, February 2005.
  • M. Blaschko, G. Holness, M. Mattar, D. Lisin, P. Utgoff, A. Hanson, H. Schultz, E. Riseman, M. Sieracki, W. Balch, and B. Tupper.
    Automatic In Situ Identification of Plankton,
    IEEE Workshop on Applications of Computer Vision, January 2005.