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

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 recognitio...

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Autores principales:	Kosmas Kritsis, Chairi Kiourt, Spyridoula Stamouli, Vasileios Sevetlidis, Alexandra Solomou, George Karetsos, Vassilis Katsouros, George Pavlidis
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	deep learning image classification plant identification transfer learning Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
Acceso en línea:	https://doaj.org/article/901f98e36e5642ffad46e2b4b5e3030f
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id	oai:doaj.org-article:901f98e36e5642ffad46e2b4b5e3030f
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spelling	oai:doaj.org-article:901f98e36e5642ffad46e2b4b5e3030f2021-11-11T19:33:51ZGRASP-125: A Dataset for Greek Vascular Plant Recognition in Natural Environment10.3390/su1321118652071-1050https://doaj.org/article/901f98e36e5642ffad46e2b4b5e3030f2021-10-01T00:00:00Zhttps://www.mdpi.com/2071-1050/13/21/11865https://doaj.org/toc/2071-1050Plant 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.Kosmas KritsisChairi KiourtSpyridoula StamouliVasileios SevetlidisAlexandra SolomouGeorge KaretsosVassilis KatsourosGeorge PavlidisMDPI AGarticledeep learningimage classificationplant identificationtransfer learningEnvironmental effects of industries and plantsTD194-195Renewable energy sourcesTJ807-830Environmental sciencesGE1-350ENSustainability, Vol 13, Iss 11865, p 11865 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	deep learning image classification plant identification transfer learning Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350
spellingShingle	deep learning image classification plant identification transfer learning Environmental effects of industries and plants TD194-195 Renewable energy sources TJ807-830 Environmental sciences GE1-350 Kosmas Kritsis Chairi Kiourt Spyridoula Stamouli Vasileios Sevetlidis Alexandra Solomou George Karetsos Vassilis Katsouros George Pavlidis GRASP-125: A Dataset for Greek Vascular Plant Recognition in Natural Environment
description	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.
format	article
author	Kosmas Kritsis Chairi Kiourt Spyridoula Stamouli Vasileios Sevetlidis Alexandra Solomou George Karetsos Vassilis Katsouros George Pavlidis
author_facet	Kosmas Kritsis Chairi Kiourt Spyridoula Stamouli Vasileios Sevetlidis Alexandra Solomou George Karetsos Vassilis Katsouros George Pavlidis
author_sort	Kosmas Kritsis
title	GRASP-125: A Dataset for Greek Vascular Plant Recognition in Natural Environment
title_short	GRASP-125: A Dataset for Greek Vascular Plant Recognition in Natural Environment
title_full	GRASP-125: A Dataset for Greek Vascular Plant Recognition in Natural Environment
title_fullStr	GRASP-125: A Dataset for Greek Vascular Plant Recognition in Natural Environment
title_full_unstemmed	GRASP-125: A Dataset for Greek Vascular Plant Recognition in Natural Environment
title_sort	grasp-125: a dataset for greek vascular plant recognition in natural environment
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/901f98e36e5642ffad46e2b4b5e3030f
work_keys_str_mv	AT kosmaskritsis grasp125adatasetforgreekvascularplantrecognitioninnaturalenvironment AT chairikiourt grasp125adatasetforgreekvascularplantrecognitioninnaturalenvironment AT spyridoulastamouli grasp125adatasetforgreekvascularplantrecognitioninnaturalenvironment AT vasileiossevetlidis grasp125adatasetforgreekvascularplantrecognitioninnaturalenvironment AT alexandrasolomou grasp125adatasetforgreekvascularplantrecognitioninnaturalenvironment AT georgekaretsos grasp125adatasetforgreekvascularplantrecognitioninnaturalenvironment AT vassiliskatsouros grasp125adatasetforgreekvascularplantrecognitioninnaturalenvironment AT georgepavlidis grasp125adatasetforgreekvascularplantrecognitioninnaturalenvironment
_version_	1718431480916475904

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

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