期刊
SUSTAINABILITY
卷 13, 期 21, 页码 -出版社
MDPI
DOI: 10.3390/su132111865
关键词
deep learning; image classification; plant identification; transfer learning
资金
- European Union
- Greek national funds through the Operational Programme Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH-CREATE-INNOVATE [T1EDK-03844]
Plant identification from images is a rapidly developing research field in computer vision. Advances in image processing, pattern recognition, and machine learning have led to accurate and reliable image-based plant identification techniques. In this paper, a dataset of 125 different species for automatic identification of vascular plants of Greece is introduced, achieving high accuracy using popular deep learning architectures and transfer learning.
Plant identification from images has become a rapidly developing research field in computer vision and is particularly challenging due to the morphological complexity of plants. The availability of large databases of plant images, and the research advancements in image processing, pattern recognition and machine learning, have resulted in a number of remarkably accurate and reliable image-based plant identification techniques, overcoming the time and expertise required for conventional plant identification, which is feasible only for expert botanists. In this paper, we introduce the GReek vAScular Plants (GRASP) dataset, a set of images composed of 125 classes of different species, for the automatic identification of vascular plants of Greece. In this context, we describe the methodology of data acquisition and dataset organization, along with the statistical features of the dataset. Furthermore, we present results of the application of popular deep learning architectures to the classification of the images in the dataset. Using transfer learning, we report 91% top-1 and 98% top-5 accuracy.
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