Pollen Grain Recognition Using Deep Learning

  • Amar DaoodEmail author
  • Eraldo Ribeiro
  • Mark Bush
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10072)


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.


Classification Rate Local Binary Pattern Pollen Type Deep Learning Convolutional Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Flenley, J.: The problem of pollen recognition. In: Clowes, M.B., Penny, J.P. (eds.) Problems in Picture Interpretation, pp. 141–145. CSIRO, Canberra (1968)Google Scholar
  2. 2.
    Treloar, W.J., Taylor, G.E., Flenley, J.R.: Towards automation of palynology 1: analysis of pollen shape and ornamentation using simple geometric measures, derived from scanning electron microscope images. J. Quat. Sci. 19, 745–754 (2004)CrossRefGoogle Scholar
  3. 3.
    Mildenhall, D., Wiltshire, P., Bryant, V.: Forensic palynology: why do it and how it works. Foren. Sci. Int. 163, 163–172 (2006). Forensic PalynologyCrossRefGoogle Scholar
  4. 4.
    Hopping, C.: Palynology and the oil industry. Rev. Palaeobot. Palynol. 2, 23–48 (1967)CrossRefGoogle Scholar
  5. 5.
    del Pozo-Banos, M., Ticay-Rivas, J.R., Alonso, J.B., Travieso, C.M.: Features extraction techniques for pollen grain classification. Neurocomputing 150(Part B), 377–391 (2015)CrossRefGoogle Scholar
  6. 6.
    Dell’Anna, R., Cristofori, A., Gottardini, E., Monti, F.: A critical presentation of innovative techniques for automated pollen identification in aerobiological monitoring networks. Pollen: Structure, Types and Effects, 273–288 (2010)Google Scholar
  7. 7.
    Travieso, C.M., Briceno, J.C., Ticay-Rivas, J.R., Alonso, J.B.: Pollen classification based on contour features. In: 2011 15th IEEE International Conference on Intelligent Engineering Systems (INES), pp. 17–21. IEEE (2011)Google Scholar
  8. 8.
    García, N.M., Chaves, V.A.E., Briceño, J.C., Travieso, C.M.: Pollen grains contour analysis on verification approach. In: Corchado, E., Snášel, V., Abraham, A., Woźniak, M., Graña, M., Cho, S.-B. (eds.) HAIS 2012. LNCS (LNAI), vol. 7208, pp. 521–532. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-28942-2_47 CrossRefGoogle Scholar
  9. 9.
    Xie, Y., OhEigeartaigh, M.: 3D discrete spherical Fourier descriptors based on surface curvature voxels for pollen classification. In: 2010 WASE International Conference on Information Engineering (ICIE), vol. 1, pp. 207–211. IEEE (2010)Google Scholar
  10. 10.
    Fernandez-Delgado, M., Carrion, P., Cernadas, E., Galvez, J.F.: Improved classification of pollen texture images using SVM and MLP (2003)Google Scholar
  11. 11.
    Da Silva, D.S., Quinta, L.N.B., Gonccalves, A.B., Pistori, H., Borth, M.R.: Application of wavelet transform in the classification of pollen grains. Afr. J. Agric. Res. 9, 908–913 (2014)CrossRefGoogle Scholar
  12. 12.
    Ticay-Rivas, J.R., Pozo-Baños, M., Travieso, C.M., Arroyo-Hernández, J., Pérez, S.T., Alonso, J.B., Mora-Mora, F.: Pollen classification based on geometrical, descriptors and colour features using decorrelation stretching method. In: Iliadis, L., Maglogiannis, I., Papadopoulos, H. (eds.) AIAI/EANN -2011. IAICT, vol. 364, pp. 342–349. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-23960-1_41 CrossRefGoogle Scholar
  13. 13.
    Chica, M.: Authentication of bee pollen grains in bright-field microscopy by combining one-class classification techniques and image processing. Microsc. Res. Tech. 75, 1475–1485 (2012)CrossRefGoogle Scholar
  14. 14.
    Ngiam, J., Khosla, A., Kim, M., Nam, J., Lee, H., Ng, A.Y.: Multimodal deep learning. In: Proceedings of the 28th International Conference on Machine Learning, ICML 2011, June 28 – July 2 2011, Bellevue, Washington, USA, pp. 689–696 (2011)Google Scholar
  15. 15.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In Pereira, F., Burges, C., Bottou, L., Weinberger, K. (eds.) Advances in Neural Information Processing Systems 25, pp. 1097–1105. Curran Associates Inc. (2012)Google Scholar
  16. 16.
    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15, 1929–1958 (2014)MathSciNetzbMATHGoogle Scholar
  17. 17.
    Ba, J., Frey, B.: Adaptive dropout for training deep neural networks. In: Advances in Neural Information Processing Systems, pp. 3084–3092 (2013)Google Scholar
  18. 18.
    Vedaldi, A., Lenc, K.: MatConvNet - convolutional neural networks for MATLAB. CoRR abs/1412.4564 (2014)Google Scholar
  19. 19.
    Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? ArXiv e-prints (2014)Google Scholar
  20. 20.
    Chatfield, K., Simonyan, K., Vedaldi, A., Zisserman, A.: Return of the devil in the details: delving deep into convolutional nets. In: British Machine Vision Conference (2014)Google Scholar
  21. 21.
    Marcos, J.V., Nava, R., Cristobal, G., Redondo, R., Escalante-Ramirez, B., Bueno, G., Deniz, O., Gonzalez-Porto, A., Pardo, C., Chung, F., Rodriguez, T.: Automated pollen identification using microscopic imaging and texture analysis. Micron 68, 36–46 (2015)CrossRefGoogle Scholar
  22. 22.
    Sokolova, M., Lapalme, G.: A systematic analysis of performance measures for classification tasks. Inf. Process. Manage. 45(4), 427–437 (2009). ElsevierCrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Electrical and Computer EngineeringFlorida Institute of TechnologyMelbourneUSA
  2. 2.Department of Computer Sciences and CybersecurityFlorida Institute of TechnologyMelbourneUSA
  3. 3.Department of Biological SciencesFlorida Institute of TechnologyMelbourneUSA

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