• Issue

    Methods in Ecology and Evolution: Volume 12, Issue 5

    May 2021


Free Access

Cover Picture and Issue Information

  • Pages: 767-769
  • First Published: 04 May 2021
Description unavailable

This month's cover image shows a random sample of pollen grains from a large library of fuchsine stained pollen, produced by Olsson et al. for their article ‘Efficient, automated and robust pollen analysis using deep learning’. Each image represents a pollen grain of a different species, scanned at 0.25 μm, and varying in size from <10 to >100 μm. With training, humans can learn to identify pollen based on variation in shape, features, and texture. However, some species are similar and there is also variation within species, so it is not possible to identify all species with certainty. Convoluted neural networks (CNN) can learn to identify pollen, but Olsson et al. show that they are approximately on par with humans, struggling to separate the same groups as us. As usual, though, the computers outperform us in speed, and once trained, a CNN can locate, classify and count a sample with up to 10,000 pollen in less than a minute. Thus, the method described by Olsson et al. can increase efficiency of pollen analysis dramatically, allowing large-scale pollen analysis in situations where it was previously too expensive.

Image credit: ©Ola Olsson