On the Road of Automated Pollen Recognition

Abstract

Identifying all the pollen species present on earth, and more particularly in a territory, is a major concern for palynologists. This is an arduous task that can be automated using artificial intelligence. Many studies have tried to solve this problem by using machine learning and deep learning. In this paper, we present three pollen recognition approaches: Classification with no examples, recognition with a sufficient number of examples, recognition with un-sufficient number of examples. For each of them, we propose respectively to use Visual Bag of Word and expectation-maximization clustering algorithms, Classification using Local Binary patterns and the Gabor Filter Feature, Local Binary Patterns and Prototypical Networks. We find 77,38% recognition for 10 pollen species rate for the first one, 90.80 % for training with a sufficient number of examples and 80 species, and 20 different pollen species and finally 84,30% for the third approach with one example for training and 20 species.