Pollen Grain Recognition Using Deep Learning

• Published in: Advances in Visual Computing Vol. 10072; pp. 321 - 330
• Main Authors: Daood, Amar, Ribeiro, Eraldo, Bush, Mark
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2016; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Classification Rate; Convolutional Neural Network; Deep Learning; Local Binary Pattern; Pollen Type
• ISBN: 9783319508344; 3319508342
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-50835-1_30

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 $$\approx $$ 94% classification rate on a dataset of 30 pollen types. These rates are among the highest obtained in this problem.