GRASP-125: A Dataset for Greek Vascular Plant Recognition in Natural Environment

Kosmas Kritsis, Chairi Kiourt, Spyridoula Stamouli, Vasileios Sevetlidis, Alexandra Solomou, George Karetsos, Vassilis Katsouros and George Pavlidis
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Kosmas Kritsis: Athena Research Center, Institute for Language and Speech Processing, 15125 Athens, Greece
Chairi Kiourt: Athena Research Center, Institute for Language and Speech Processing, 15125 Athens, Greece
Spyridoula Stamouli: Athena Research Center, Institute for Language and Speech Processing, 15125 Athens, Greece
Vasileios Sevetlidis: Athena Research Center, Institute for Language and Speech Processing, 15125 Athens, Greece
Alexandra Solomou: Institute of Mediterranean Forest Ecosystems, Hellenic Agricultural Organization DEMETER, 11528 Athens, Greece
George Karetsos: Institute of Mediterranean Forest Ecosystems, Hellenic Agricultural Organization DEMETER, 11528 Athens, Greece
Vassilis Katsouros: Athena Research Center, Institute for Language and Speech Processing, 15125 Athens, Greece
George Pavlidis: Athena Research Center, Institute for Language and Speech Processing, 15125 Athens, Greece

Sustainability, 2021, vol. 13, issue 21, 1-15

Abstract: 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.

Keywords: deep learning; image classification; plant identification; transfer learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2021
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