Analysis of transfer learning for deep neural network based plant classification models

Aydin Kaya*, Ali Seydi Keceli, Cagatay Catal, Hamdi Yalin Yalic, Huseyin Temucin, Bedir Tekinerdogan

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

34 Citations (Scopus)


Plant species classification is crucial for biodiversity protection and conservation. Manual classification is time-consuming, expensive, and requires experienced experts who are often limited available. To cope with these issues, various machine learning algorithms have been proposed to support the automated classification of plant species. Among these machine learning algorithms, Deep Neural Networks (DNNs) have been applied to different data sets. DNNs have been however often applied in isolation and no effort has been made to reuse and transfer the knowledge of different applications of DNNs. Transfer learning in the context of machine learning implies the usage of the results of multiple applications of DNNs. In this article, the results of the effect of four different transfer learning models for deep neural network-based plant classification is investigated on four public datasets. Our experimental study demonstrates that transfer learning can provide important benefits for automated plant identification and can improve low-performance plant classification models.

Original languageEnglish
Pages (from-to)20-29
Number of pages10
JournalComputers and Electronics in Agriculture
Publication statusPublished - 1 Mar 2019


  • Convolutional neural networks
  • Deep neural networks
  • Fine-tuning
  • Plant classification
  • Transfer learning

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