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Apple Fruit Recognition Algorithm Based on Multi-Spectral Dynamic Image Analysis
The ability to accurately recognize fruit on trees is a critical step in robotic harvesting. Many researchers have investigated a variety of image analysis methods based on different imaging technologies for fruit recognition. However, challenges still occur in the implementation of this goal due to...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412272/ https://www.ncbi.nlm.nih.gov/pubmed/30813417 http://dx.doi.org/10.3390/s19040949 |
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author | Feng, Juan Zeng, Lihua He, Long |
author_facet | Feng, Juan Zeng, Lihua He, Long |
author_sort | Feng, Juan |
collection | PubMed |
description | The ability to accurately recognize fruit on trees is a critical step in robotic harvesting. Many researchers have investigated a variety of image analysis methods based on different imaging technologies for fruit recognition. However, challenges still occur in the implementation of this goal due to various factors, especially variable light and proximal color background. In this study, images with fruit were acquired with a Forward Looking Infrared (FLIR) camera based on the Multi-Spectral Dynamic Imaging (MSX) technology. In view of its imaging mechanism, the optimal timing and shooting angle for image acquisition were pre-analyzed to obtain the maximum contrast between fruit and background. An effective algorithm was developed for locking potential fruit regions, which was based on the pseudo-color and texture information from MSX images. The algorithm was applied to 506 training and 340 evaluating images, including a variety of fruit and complex backgrounds. Recognition precision and sensitivity of these complete fruit regions were both above 92%, and those of incomplete fruit regions were not lower than 72%. The average processing time for each image was less than 1 s. The results indicated that the developed algorithm based on MSX imaging was effective for fruit recognition and could be suggested as a potential method for the automation of orchard production. |
format | Online Article Text |
id | pubmed-6412272 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-64122722019-04-03 Apple Fruit Recognition Algorithm Based on Multi-Spectral Dynamic Image Analysis Feng, Juan Zeng, Lihua He, Long Sensors (Basel) Article The ability to accurately recognize fruit on trees is a critical step in robotic harvesting. Many researchers have investigated a variety of image analysis methods based on different imaging technologies for fruit recognition. However, challenges still occur in the implementation of this goal due to various factors, especially variable light and proximal color background. In this study, images with fruit were acquired with a Forward Looking Infrared (FLIR) camera based on the Multi-Spectral Dynamic Imaging (MSX) technology. In view of its imaging mechanism, the optimal timing and shooting angle for image acquisition were pre-analyzed to obtain the maximum contrast between fruit and background. An effective algorithm was developed for locking potential fruit regions, which was based on the pseudo-color and texture information from MSX images. The algorithm was applied to 506 training and 340 evaluating images, including a variety of fruit and complex backgrounds. Recognition precision and sensitivity of these complete fruit regions were both above 92%, and those of incomplete fruit regions were not lower than 72%. The average processing time for each image was less than 1 s. The results indicated that the developed algorithm based on MSX imaging was effective for fruit recognition and could be suggested as a potential method for the automation of orchard production. MDPI 2019-02-23 /pmc/articles/PMC6412272/ /pubmed/30813417 http://dx.doi.org/10.3390/s19040949 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Feng, Juan Zeng, Lihua He, Long Apple Fruit Recognition Algorithm Based on Multi-Spectral Dynamic Image Analysis |
title | Apple Fruit Recognition Algorithm Based on Multi-Spectral Dynamic Image Analysis |
title_full | Apple Fruit Recognition Algorithm Based on Multi-Spectral Dynamic Image Analysis |
title_fullStr | Apple Fruit Recognition Algorithm Based on Multi-Spectral Dynamic Image Analysis |
title_full_unstemmed | Apple Fruit Recognition Algorithm Based on Multi-Spectral Dynamic Image Analysis |
title_short | Apple Fruit Recognition Algorithm Based on Multi-Spectral Dynamic Image Analysis |
title_sort | apple fruit recognition algorithm based on multi-spectral dynamic image analysis |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6412272/ https://www.ncbi.nlm.nih.gov/pubmed/30813417 http://dx.doi.org/10.3390/s19040949 |
work_keys_str_mv | AT fengjuan applefruitrecognitionalgorithmbasedonmultispectraldynamicimageanalysis AT zenglihua applefruitrecognitionalgorithmbasedonmultispectraldynamicimageanalysis AT helong applefruitrecognitionalgorithmbasedonmultispectraldynamicimageanalysis |