Expert and machine discrimination of marine flora: a comparison of recognition accuracy of field-collected phytoplankton

The categorisation of labelling of phytoplankton specimens is carried out manually using microscopes by marine ecologists and taxonomists. Research to automate the task has been on going for many years. Although many systems have been shown to work in small-scale laboratory conditions with cultured populations, few have succeeded when applied to field collected specimens. The reasons are diverse, but are principally due to severely degraded performance of the chosen processing algorithms in the presence of noise and natural morphological variability of the organisms. The application of statistical and neural network pattern learning methods have allowed progress to be made in this difficult area. The machine learning system DiCANN was trained on 128 of the 310 image data set and tested on the 182 samples. This study has highlighted the difficulties facing human ecologists and has shown that automation methods can perform as well as humans on complex categorisations.