Bayesian model for semi-automated zooplankton classification with predictive confidence and rapid category aggregation

Zooplankton play a critical role in aquatic ecosystems and are commonly used as bio-indicators to assess anthropogenic and climate impacts. Nevertheless, traditional microscope-based identification of zooplankton is inefficient. To overcome the low efficiency, computer-based methods have been developed. Yet, the performance of automated classification remains unsatisfactory because of the low accuracy of recognition. Here we propose a novel framework for automated plankton classification based on a naive Bayesian classifier (NBC). We take advantage of the posterior probability of NBC to facilitate category aggregation and to single out objects of low predictive confidence for manual re-classifying in order to achieve a high level of final accuracy. This method was applied to East China Sea zooplankton samples with 154 289 objects, and the Bayesian automated zooplankton classification model showed a reasonable overall accuracy of 0.69 in unbalanced and 0.68 in balanced training for 25 planktonic and 1 aggregated non-planktonic categories. More importantly, after manually checking 17 to 38% of the objects of low confidence (depending on how one defines 'low confidence'), the final accuracy increased to 0.85-0.95 in the unbalanced training case, and after checking 18 to 42% of the low-confidence objects in the balanced training case, the final accuracy increased to 0.84-0.95. Our semi-automated approach is significantly more accurate than automated classifiers in recognizing rare categories, thereby facilitating ecological applications by improving the estimates of taxa richness and diversity. Our approach can make up for the deficiencies in current automated zooplankton classifiers and facilitates an efficient semi-automated zooplankton classification, which may have a broad application in environmental monitoring and ecological research.