Automated color detection in orchids using color labels and deep learning

The color of particular parts of a flower is often employed as one of the features to differentiate between flower types. Thus, color is also used in flower-image classification. Color labels, such as ‘green’, ‘red’, and ‘yellow’, are used by taxonomists and lay people alike to describe the color of...

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Autores principales:	Diah Harnoni Apriyanti, Luuk J. Spreeuwers, Peter J. F. Lucas, Raymond N. J. Veldhuis
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/d33a0c00ff364354930646d9d5bfef66
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spelling	oai:doaj.org-article:d33a0c00ff364354930646d9d5bfef662021-11-04T06:49:43ZAutomated color detection in orchids using color labels and deep learning1932-6203https://doaj.org/article/d33a0c00ff364354930646d9d5bfef662021-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC8550396/?tool=EBIhttps://doaj.org/toc/1932-6203The color of particular parts of a flower is often employed as one of the features to differentiate between flower types. Thus, color is also used in flower-image classification. Color labels, such as ‘green’, ‘red’, and ‘yellow’, are used by taxonomists and lay people alike to describe the color of plants. Flower image datasets usually only consist of images and do not contain flower descriptions. In this research, we have built a flower-image dataset, especially regarding orchid species, which consists of human-friendly textual descriptions of features of specific flowers, on the one hand, and digital photographs indicating how a flower looks like, on the other hand. Using this dataset, a new automated color detection model was developed. It is the first research of its kind using color labels and deep learning for color detection in flower recognition. As deep learning often excels in pattern recognition in digital images, we applied transfer learning with various amounts of unfreezing of layers with five different neural network architectures (VGG16, Inception, Resnet50, Xception, Nasnet) to determine which architecture and which scheme of transfer learning performs best. In addition, various color scheme scenarios were tested, including the use of primary and secondary color together, and, in addition, the effectiveness of dealing with multi-class classification using multi-class, combined binary, and, finally, ensemble classifiers were studied. The best overall performance was achieved by the ensemble classifier. The results show that the proposed method can detect the color of flower and labellum very well without having to perform image segmentation. The result of this study can act as a foundation for the development of an image-based plant recognition system that is able to offer an explanation of a provided classification.Diah Harnoni ApriyantiLuuk J. SpreeuwersPeter J. F. LucasRaymond N. J. VeldhuisPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Diah Harnoni Apriyanti Luuk J. Spreeuwers Peter J. F. Lucas Raymond N. J. Veldhuis Automated color detection in orchids using color labels and deep learning
description	The color of particular parts of a flower is often employed as one of the features to differentiate between flower types. Thus, color is also used in flower-image classification. Color labels, such as ‘green’, ‘red’, and ‘yellow’, are used by taxonomists and lay people alike to describe the color of plants. Flower image datasets usually only consist of images and do not contain flower descriptions. In this research, we have built a flower-image dataset, especially regarding orchid species, which consists of human-friendly textual descriptions of features of specific flowers, on the one hand, and digital photographs indicating how a flower looks like, on the other hand. Using this dataset, a new automated color detection model was developed. It is the first research of its kind using color labels and deep learning for color detection in flower recognition. As deep learning often excels in pattern recognition in digital images, we applied transfer learning with various amounts of unfreezing of layers with five different neural network architectures (VGG16, Inception, Resnet50, Xception, Nasnet) to determine which architecture and which scheme of transfer learning performs best. In addition, various color scheme scenarios were tested, including the use of primary and secondary color together, and, in addition, the effectiveness of dealing with multi-class classification using multi-class, combined binary, and, finally, ensemble classifiers were studied. The best overall performance was achieved by the ensemble classifier. The results show that the proposed method can detect the color of flower and labellum very well without having to perform image segmentation. The result of this study can act as a foundation for the development of an image-based plant recognition system that is able to offer an explanation of a provided classification.
format	article
author	Diah Harnoni Apriyanti Luuk J. Spreeuwers Peter J. F. Lucas Raymond N. J. Veldhuis
author_facet	Diah Harnoni Apriyanti Luuk J. Spreeuwers Peter J. F. Lucas Raymond N. J. Veldhuis
author_sort	Diah Harnoni Apriyanti
title	Automated color detection in orchids using color labels and deep learning
title_short	Automated color detection in orchids using color labels and deep learning
title_full	Automated color detection in orchids using color labels and deep learning
title_fullStr	Automated color detection in orchids using color labels and deep learning
title_full_unstemmed	Automated color detection in orchids using color labels and deep learning
title_sort	automated color detection in orchids using color labels and deep learning
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/d33a0c00ff364354930646d9d5bfef66
work_keys_str_mv	AT diahharnoniapriyanti automatedcolordetectioninorchidsusingcolorlabelsanddeeplearning AT luukjspreeuwers automatedcolordetectioninorchidsusingcolorlabelsanddeeplearning AT peterjflucas automatedcolordetectioninorchidsusingcolorlabelsanddeeplearning AT raymondnjveldhuis automatedcolordetectioninorchidsusingcolorlabelsanddeeplearning
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Automated color detection in orchids using color labels and deep learning

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