Going Deeper on BioImages Classification: A Plant Leaf Dataset Case Study

  • Daniel H. A. Alves
  • Luís F. Galonetti
  • Claiton de Oliveira
  • Pedro H. Bugatti
  • Priscila T. M. Saito
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10657)


In this paper, we present and evaluate the accuracy of a Deep Convolutional Neural Network (DCNN) architecture, with other traditional methods, to solve a bioimage classification problem. The main contributions of this work are the application of a DCNN architecture and the further comparison of different types of classification and feature extraction techniques applied to a plant leaf image dataset. Furthermore, we go deeper on the analysis of a cross-domain transfer learning approach using a state-of-the-art deep neural network called Inception-v3. Our results show that we manage to classify a subset of 53 species of leafs with a notable mean accuracy of \(98.2\%\).


Deep learning Transfer learning Pattern recognition Image classification Bioimages 



This research was supported by grants from CNPq (#431668/2016-7 and #422811/2016-5), Fundação Araucária, FAPESP, SETI and UTFPR.


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© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of ComputingFederal University of Technology - ParanáCornélio ProcópioBrazil
  2. 2.Institute of ComputingUniversity of CampinasCampinasBrazil

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