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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| Veröffentlicht in: | Sustainability 2021-11, Vol.13 (21), p.11865 |
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| creator | Kritsis, Kosmas Kiourt, Chairi Stamouli, Spyridoula Sevetlidis, Vasileios Solomou, Alexandra Karetsos, George Katsouros, Vassilis Pavlidis, George |
| 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. |
| doi_str_mv | 10.3390/su132111865 |
| format | Article |
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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. 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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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Using transfer learning, we report 91% top-1 and 98% top-5 accuracy.</description><subject>Automation</subject><subject>Botanists</subject><subject>Computer programs</subject><subject>Computer vision</subject><subject>Data acquisition</subject><subject>Datasets</subject><subject>Deep learning</subject><subject>Flowers & plants</subject><subject>Identification</subject><subject>Identification and classification</subject><subject>Image classification</subject><subject>Image processing</subject><subject>Laboratories</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Machine vision</subject><subject>Morphology</subject><subject>National parks</subject><subject>Pattern recognition</subject><subject>Plants</subject><subject>Transfer learning</subject><subject>Trees</subject><issn>2071-1050</issn><issn>2071-1050</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNpVkVFLwzAQx4soOHRPfoGATyKduaRNU9_KnHMwdGzqa0nb6-jskpmkot_eynzY7h7uOH7_u4N_EFwBHXGe0jvXAWcAIEV8EgwYTSAEGtPTg_48GDq3oX1wDimIQbCYLrPVIgQW35OMPCivHHpSG0umFvGDvCtXdq2yZNEq7ckSS7PWjW-MJo0mz8p3VrVkor8aa_QWtb8MzmrVOhz-14vg7XHyOn4K5y_T2TibhyVPwIdJyhKZ1lL0P1dYsapG4JFIJcYRSwqBqeJFwrAUkNBCFSoqY0mByqqmUVoJfhFc7_furPns0Pl8Yzqr-5M5i1NBhYgk9NRoT61Vi3mja-OtKvuscNuURmPd9PNMQpxQJiPaC26OBD3j8duvVedcPlstj9nbPVta45zFOt_ZZqvsTw40_7MkP7CE_wKByHn8</recordid><startdate>20211101</startdate><enddate>20211101</enddate><creator>Kritsis, Kosmas</creator><creator>Kiourt, Chairi</creator><creator>Stamouli, Spyridoula</creator><creator>Sevetlidis, Vasileios</creator><creator>Solomou, Alexandra</creator><creator>Karetsos, George</creator><creator>Katsouros, Vassilis</creator><creator>Pavlidis, George</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>ISR</scope><scope>4U-</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0002-4185-2344</orcidid><orcidid>https://orcid.org/0000-0002-9909-1584</orcidid><orcidid>https://orcid.org/0000-0001-8501-8899</orcidid><orcidid>https://orcid.org/0000-0001-9348-8786</orcidid><orcidid>https://orcid.org/0000-0003-4513-5040</orcidid></search><sort><creationdate>20211101</creationdate><title>GRASP-125: A Dataset for Greek Vascular Plant Recognition in Natural Environment</title><author>Kritsis, Kosmas ; 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| subjects | Automation Botanists Computer programs Computer vision Data acquisition Datasets Deep learning Flowers & plants Identification Identification and classification Image classification Image processing Laboratories Learning algorithms Machine learning Machine vision Morphology National parks Pattern recognition Plants Transfer learning Trees |
| title | GRASP-125: A Dataset for Greek Vascular Plant Recognition in Natural Environment |
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