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Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion
Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illum...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4801551/ https://www.ncbi.nlm.nih.gov/pubmed/26840313 http://dx.doi.org/10.3390/s16020173 |
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author | Zhao, Yuanshen Gong, Liang Huang, Yixiang Liu, Chengliang |
author_facet | Zhao, Yuanshen Gong, Liang Huang, Yixiang Liu, Chengliang |
author_sort | Zhao, Yuanshen |
collection | PubMed |
description | Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. |
format | Online Article Text |
id | pubmed-4801551 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-48015512016-03-25 Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion Zhao, Yuanshen Gong, Liang Huang, Yixiang Liu, Chengliang Sensors (Basel) Article Automatic recognition of mature fruits in a complex agricultural environment is still a challenge for an autonomous harvesting robot due to various disturbances existing in the background of the image. The bottleneck to robust fruit recognition is reducing influence from two main disturbances: illumination and overlapping. In order to recognize the tomato in the tree canopy using a low-cost camera, a robust tomato recognition algorithm based on multiple feature images and image fusion was studied in this paper. Firstly, two novel feature images, the a*-component image and the I-component image, were extracted from the L*a*b* color space and luminance, in-phase, quadrature-phase (YIQ) color space, respectively. Secondly, wavelet transformation was adopted to fuse the two feature images at the pixel level, which combined the feature information of the two source images. Thirdly, in order to segment the target tomato from the background, an adaptive threshold algorithm was used to get the optimal threshold. The final segmentation result was processed by morphology operation to reduce a small amount of noise. In the detection tests, 93% target tomatoes were recognized out of 200 overall samples. It indicates that the proposed tomato recognition method is available for robotic tomato harvesting in the uncontrolled environment with low cost. MDPI 2016-01-29 /pmc/articles/PMC4801551/ /pubmed/26840313 http://dx.doi.org/10.3390/s16020173 Text en © 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Zhao, Yuanshen Gong, Liang Huang, Yixiang Liu, Chengliang Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion |
title | Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion |
title_full | Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion |
title_fullStr | Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion |
title_full_unstemmed | Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion |
title_short | Robust Tomato Recognition for Robotic Harvesting Using Feature Images Fusion |
title_sort | robust tomato recognition for robotic harvesting using feature images fusion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4801551/ https://www.ncbi.nlm.nih.gov/pubmed/26840313 http://dx.doi.org/10.3390/s16020173 |
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