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.
Analysis of transfer learning for deep neural network based plant classification models
Aydın Kaya,A. Keçeli,C. Catal,Hamdi Yalin Yalic,Hüseyin Temuçin,B. Tekinerdogan
Published 2019 in Computers and Electronics in Agriculture
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- Publication year
2019
- Venue
Computers and Electronics in Agriculture
- Publication date
2019-03-01
- Fields of study
Biology, Computer Science, Environmental Science
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