Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method

Accurately extracting the grape cluster at the front of overlapping grape clusters is the primary problem of the grape-harvesting robot. To solve the difficult problem of identifying and segmenting the overlapping grape clusters in the cultivation environment of a trellis, a simple method based on t...

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Autores principales:	Yun Peng, Shengyi Zhao, Jizhan Liu
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	grape segmentation DeepLabV3+ deep learning region growing Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/7e7381277b52491ea55063a92b3f76a9
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id	oai:doaj.org-article:7e7381277b52491ea55063a92b3f76a9
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spelling	oai:doaj.org-article:7e7381277b52491ea55063a92b3f76a92021-11-25T17:24:52ZSegmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method10.3390/electronics102228132079-9292https://doaj.org/article/7e7381277b52491ea55063a92b3f76a92021-11-01T00:00:00Zhttps://www.mdpi.com/2079-9292/10/22/2813https://doaj.org/toc/2079-9292Accurately extracting the grape cluster at the front of overlapping grape clusters is the primary problem of the grape-harvesting robot. To solve the difficult problem of identifying and segmenting the overlapping grape clusters in the cultivation environment of a trellis, a simple method based on the deep learning network and the idea of region growing is proposed. Firstly, the region of grape in an RGB image was obtained by the finely trained DeepLabV3+ model. The idea of transfer learning was adopted when training the network with a limited number of training sets. Then, the corresponding region of the grape in the depth image captured by RealSense D435 was processed by the proposed depth region growing algorithm (DRG) to extract the front cluster. The depth region growing method uses the depth value instead of gray value to achieve clustering. Finally, it fils the holes in the clustered region of interest, extracts the contours, and maps the obtained contours to the RGB image. The images captured by RealSense D435 in a natural trellis environment were adopted to evaluate the performance of the proposed method. The experimental results showed that the recall and precision of the proposed method were 89.2% and 87.5%, respectively. The demonstrated performance indicated that the proposed method could satisfy the requirements of practical application for robotic grape harvesting.Yun PengShengyi ZhaoJizhan LiuMDPI AGarticlegrape segmentationDeepLabV3+deep learningregion growingElectronicsTK7800-8360ENElectronics, Vol 10, Iss 2813, p 2813 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	grape segmentation DeepLabV3+ deep learning region growing Electronics TK7800-8360
spellingShingle	grape segmentation DeepLabV3+ deep learning region growing Electronics TK7800-8360 Yun Peng Shengyi Zhao Jizhan Liu Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method
description	Accurately extracting the grape cluster at the front of overlapping grape clusters is the primary problem of the grape-harvesting robot. To solve the difficult problem of identifying and segmenting the overlapping grape clusters in the cultivation environment of a trellis, a simple method based on the deep learning network and the idea of region growing is proposed. Firstly, the region of grape in an RGB image was obtained by the finely trained DeepLabV3+ model. The idea of transfer learning was adopted when training the network with a limited number of training sets. Then, the corresponding region of the grape in the depth image captured by RealSense D435 was processed by the proposed depth region growing algorithm (DRG) to extract the front cluster. The depth region growing method uses the depth value instead of gray value to achieve clustering. Finally, it fils the holes in the clustered region of interest, extracts the contours, and maps the obtained contours to the RGB image. The images captured by RealSense D435 in a natural trellis environment were adopted to evaluate the performance of the proposed method. The experimental results showed that the recall and precision of the proposed method were 89.2% and 87.5%, respectively. The demonstrated performance indicated that the proposed method could satisfy the requirements of practical application for robotic grape harvesting.
format	article
author	Yun Peng Shengyi Zhao Jizhan Liu
author_facet	Yun Peng Shengyi Zhao Jizhan Liu
author_sort	Yun Peng
title	Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method
title_short	Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method
title_full	Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method
title_fullStr	Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method
title_full_unstemmed	Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method
title_sort	segmentation of overlapping grape clusters based on the depth region growing method
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/7e7381277b52491ea55063a92b3f76a9
work_keys_str_mv	AT yunpeng segmentationofoverlappinggrapeclustersbasedonthedepthregiongrowingmethod AT shengyizhao segmentationofoverlappinggrapeclustersbasedonthedepthregiongrowingmethod AT jizhanliu segmentationofoverlappinggrapeclustersbasedonthedepthregiongrowingmethod
_version_	1718412417562574848

Segmentation of Overlapping Grape Clusters Based on the Depth Region Growing Method

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