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Towards Estimating the Stiffness of Soft Fruits using a Piezoresistive Tactile Sensor and Neural Network Schemes

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dc.contributor.authorErukainure, Frank Efeen
dc.contributor.authorParque, Victoren
dc.contributor.authorHassan, Mohsen A.en
dc.contributor.authorFathElbab, Ahmed M. R.en
dc.date.accessioned2023-11-27T16:07:37Zen
dc.date.available2023-11-27T16:07:37Zen
dc.date.issued2022en
dc.date.updated2023-11-27T11:51:01Zen
dc.description.abstractMeasuring the ripeness of fruits is one of the key challenges to enable optimal and just-in-time strategies across the fruit supply chain. In this paper, we study the performance of a tactile sensor to estimate the ground truth of the stiffness of fruits, with kiwifruit as a case study. Our sensor configuration is based on a three-beam cantilever arrangement with piezoresistive elements, enabling the stable acquisition of sensor readings over independent trials. Our estimation scheme is based on the com-pact feed-forward neural networks, allowing us to find effective nonlinear relationships between instantaneous sensor readings and the ground truth of stiffness of fruits. Our experiments using several kiwifruit specimens show the competitive performance frontiers of stiffness approximation using 25 compact feed-forward neural networks, converging to MSE loss at 10-5 across training-validation-testing in most of the cases, and the utmost predictive performance of a pyramidal class of feed-forward architectures. Our results pinpoint the potential to realize robust fruit ripeness measurement with intelligent tactile sensors.en
dc.description.versionAccepted versionen
dc.format.extentPages 290-295en
dc.format.extent6 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifier.doihttps://doi.org/10.1109/AIM52237.2022.9863245en
dc.identifier.isbn9781665413084en
dc.identifier.issn2159-6255en
dc.identifier.orcidErukainure, Frank [0000-0002-7640-391X]en
dc.identifier.urihttp://hdl.handle.net/10919/116695en
dc.identifier.volume2022-Julyen
dc.language.isoenen
dc.publisherIEEEen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectRoboticsen
dc.subjectEngineeringen
dc.subjectINDENTATION TESTSen
dc.subjectTISSUESen
dc.subject4605 Data Management and Data Scienceen
dc.subject46 Information and Computing Sciencesen
dc.subject40 Engineeringen
dc.subject4009 Electronics, Sensors and Digital Hardwareen
dc.titleTowards Estimating the Stiffness of Soft Fruits using a Piezoresistive Tactile Sensor and Neural Network Schemesen
dc.title.serial2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)en
dc.typeConference proceedingen
dc.type.dcmitypeTexten
dc.type.otherProceedings Paperen
dc.type.otherBook in seriesen
pubs.finish-date2022-07-15en
pubs.organisational-group/Virginia Techen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciencesen
pubs.organisational-group/Virginia Tech/Agriculture & Life Sciences/Biological Systems Engineeringen
pubs.organisational-group/Virginia Tech/Graduate studentsen
pubs.organisational-group/Virginia Tech/Graduate students/Doctoral studentsen
pubs.start-date2022-07-11en

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