Pollen grain recognition using deep learning

Abstract

Pollen identification helps forensic scientists solve elusive crimes, provides data for climate-change modelers, and even hints at potential sites for petroleum exploration. Despite its wide range of applications, most pollen identification is still done by time-consuming visual inspection by well-trained experts. Although partial automation is currently available, automatic pollen identification remains an open problem. Current pollen-classification methods use pre-designed features of texture and contours, which may not be sufficiently distinctive. Instead of using pre-designed features, our pollen-recognition method learns both features and classifier from training data under the deep-learning framework. To further enhance our network’s classification ability, we use transfer learning to leverage knowledge from networks that have been pre-trained on large datasets of images. Our method achieved ≈94% classification rate on a dataset of 30 pollen types. These rates are among the highest obtained in this problem.