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

KAYA A., KEÇELİ A. S., Catal C., YALIÇ H. Y., TEMUÇİN H., Tekinerdogan B.

COMPUTERS AND ELECTRONICS IN AGRICULTURE, vol.158, pp.20-29, 2019 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 158
  • Publication Date: 2019
  • Doi Number: 10.1016/j.compag.2019.01.041
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.20-29
  • Keywords: Plant classification, Transfer learning, Deep neural networks, Fine-tuning, Convolutional neural networks, COLOR TEXTURE FEATURES, WEED-DETECTION, REAL-TIME, IDENTIFICATION, RECOGNITION, IMAGE, VENATION, SYSTEM, WHEAT, SHAPE
  • Hacettepe University Affiliated: Yes


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.