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Estimating the stiffness of kiwifruit based on the fusion of instantaneous tactile sensor data and machine learning schemes

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dc.contributor.authorErukainure, Frank Efeen
dc.contributor.authorParque, Victoren
dc.contributor.authorHassan, M. A.en
dc.contributor.authorFathEl-Bab, Ahmed M. R.en
dc.date.accessioned2023-11-27T14:49:48Zen
dc.date.available2023-11-27T14:49:48Zen
dc.date.issued2022-10en
dc.date.updated2023-11-27T11:41:55Zen
dc.description.abstractMeasuring the ripeness of fruits is one of the critical factors in achieving real-time quality control and sorting of fruit by growers and postharvest managers. However, recent tactile sensing approaches for fruit ripeness detection have suffered setbacks due to: (1) the nonlinear relationship between the sensor output and the true stiffness of fruits; and (2) the angle of contact, referred to as the inclination angle, between the sensor and the outer surface of the fruit. In this paper, we propose a non-destructive tactile sensing approach for estimating the stiffness of fruits, using kiwifruit as a case study. Our sensor configuration is based on a three-probe piezoresistive cantilever beam, allowing us to obtain relatively stable sensor outputs that are independent of the inclination angle of the fruit surface. Our stiffness estimation approach is based on the combination of instantaneous sensor outputs with 63 regression-based machine learning models comprising of neural networks, Gaussian process, support vector machines, and decision trees. For experiments, we used several kiwifruit samples at diverse ripeness levels. The extracted sensor data was used to train the learning models over a 10-fold cross-validation technique, allowing us to find the nonlinear relationships between the instantaneous sensor outputs and the ground truth stiffness of the fruit. Our pairwise statistical comparison by the Wilcoxon test at 5% significance revealed the competitive performance frontiers of our approach for stiffness prediction; the Gaussian process kernel functions and the binary trees outperformed other models at a mean squared error (MSE) of 1.0 and 2×10−23, respectively. Most neural network models achieved competitive learning performance at MSE less than 10−5 and the utmost performance being a pyramidal class of feed-forward neural architectures. The results portray the potential of achieving accurate ripeness estimation of fruit using intelligent tactile sensors with fast machine learning schemes across the supply chain.en
dc.description.versionAccepted versionen
dc.format.extent15 page(s)en
dc.format.mimetypeapplication/pdfen
dc.identifierARTN 107289 (Article number)en
dc.identifier.doihttps://doi.org/10.1016/j.compag.2022.107289en
dc.identifier.eissn1872-7107en
dc.identifier.issn0168-1699en
dc.identifier.orcidErukainure, Frank [0000-0002-7640-391X]en
dc.identifier.urihttp://hdl.handle.net/10919/116693en
dc.identifier.volume201en
dc.language.isoenen
dc.publisherElsevieren
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectBinary decision treesen
dc.subjectGaussian processen
dc.subjectMachine learningen
dc.subjectNeural networksen
dc.subjectSupport vector machinesen
dc.subjectTactile sensingen
dc.subjectINDENTATION TESTSen
dc.subjectNEURAL-NETWORKen
dc.subject4605 Data Management and Data Scienceen
dc.subject46 Information and Computing Sciencesen
dc.subject40 Engineeringen
dc.subject4009 Electronics, Sensors and Digital Hardwareen
dc.subject30 Agricultural, veterinary and food sciencesen
dc.titleEstimating the stiffness of kiwifruit based on the fusion of instantaneous tactile sensor data and machine learning schemesen
dc.title.serialComputers and Electronics in Agricultureen
dc.typeArticle - Refereeden
dc.type.dcmitypeTexten
dc.type.otherArticleen
dc.type.otherJournalen
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

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