Unsupervised domain adaptation for highlight detection and removal in agricultural robot vision system.
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| Title: | Unsupervised domain adaptation for highlight detection and removal in agricultural robot vision system. |
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| Authors: | Laide Guan1 guanlaide@gmail.com, Bole Li1 |
| Source: | Journal of Biotech Research. 2024, Vol. 19, p355-364. 10p. |
| Subjects: | Robot vision, Ubuntu (Operating system), Agricultural robots, Feature extraction, Agriculture |
| Abstract: | Assessment of the cotton boll health is essential for field oversight and maturity rating. Typical methods for agricultural image acquiring are unsuitable for cotton boll images due to the possibility of distribution mismatch that resulted from environmental variables such as duration, climate, farming activities, and regions. Adapting a domain can solve this problem. This study applied a domain-adversarial neural network-driven unsupervised domain adaptation (DANN-UDA) approach to gather the cotton bolls datasets, which involved multiple steps of target label inference and dense inherent ConvNet-based feature extraction. The proposed approach was executed in the agricultural robot with Ubuntu and Robot Operating System (ROS) environment and verified using an agricultural robot captured cotton boll image. The efficiency of proposed DANN-UDA method in cotton boll identification was evaluated and demonstrated better results. The performance of the cotton boll stage detection of proposed DANN-UDA was compared using Visual Geometry Group (VGG) and You Only Look Once version 5 (YOLOv5) networks in agricultural robot vision system. The results demonstrated that the proposed approach obtained the best identification outcomes in a variety of scenarios. Additionally, the proposed model could serve as a helpful substitute for human observation and conventional categorization techniques. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Biotech Research is the property of Bio Tech System and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 184976303 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Unsupervised domain adaptation for highlight detection and removal in agricultural robot vision system. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Laide+Guan%22">Laide Guan</searchLink><relatesTo>1</relatesTo><i> guanlaide@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Bole+Li%22">Bole Li</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Journal+of+Biotech+Research%22">Journal of Biotech Research</searchLink>. 2024, Vol. 19, p355-364. 10p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Robot+vision%22">Robot vision</searchLink><br /><searchLink fieldCode="DE" term="%22Ubuntu+%28Operating+system%29%22">Ubuntu (Operating system)</searchLink><br /><searchLink fieldCode="DE" term="%22Agricultural+robots%22">Agricultural robots</searchLink><br /><searchLink fieldCode="DE" term="%22Feature+extraction%22">Feature extraction</searchLink><br /><searchLink fieldCode="DE" term="%22Agriculture%22">Agriculture</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Assessment of the cotton boll health is essential for field oversight and maturity rating. Typical methods for agricultural image acquiring are unsuitable for cotton boll images due to the possibility of distribution mismatch that resulted from environmental variables such as duration, climate, farming activities, and regions. Adapting a domain can solve this problem. This study applied a domain-adversarial neural network-driven unsupervised domain adaptation (DANN-UDA) approach to gather the cotton bolls datasets, which involved multiple steps of target label inference and dense inherent ConvNet-based feature extraction. The proposed approach was executed in the agricultural robot with Ubuntu and Robot Operating System (ROS) environment and verified using an agricultural robot captured cotton boll image. The efficiency of proposed DANN-UDA method in cotton boll identification was evaluated and demonstrated better results. The performance of the cotton boll stage detection of proposed DANN-UDA was compared using Visual Geometry Group (VGG) and You Only Look Once version 5 (YOLOv5) networks in agricultural robot vision system. The results demonstrated that the proposed approach obtained the best identification outcomes in a variety of scenarios. Additionally, the proposed model could serve as a helpful substitute for human observation and conventional categorization techniques. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Journal of Biotech Research is the property of Bio Tech System and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 355 Subjects: – SubjectFull: Robot vision Type: general – SubjectFull: Ubuntu (Operating system) Type: general – SubjectFull: Agricultural robots Type: general – SubjectFull: Feature extraction Type: general – SubjectFull: Agriculture Type: general Titles: – TitleFull: Unsupervised domain adaptation for highlight detection and removal in agricultural robot vision system. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Laide Guan – PersonEntity: Name: NameFull: Bole Li IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 10 Text: 2024 Type: published Y: 2024 Identifiers: – Type: issn-print Value: 19443285 Numbering: – Type: volume Value: 19 Titles: – TitleFull: Journal of Biotech Research Type: main |
| ResultId | 1 |
