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dc.contributor.authorNturambirwe, Jean Frederic Isingizwe
dc.contributor.authorPerold, Willem Jacobus
dc.contributor.authorOpara, Umezuruike Linus
dc.date.accessioned2021-11-11T12:01:03Z
dc.date.available2021-11-11T12:01:03Z
dc.date.issued2021
dc.identifier.citationNturambirwe, J. F. I. et al. (2021). Classification learning of latent bruise damage to apples using shortwave infrared hyperspectral imaging. Sensors, 21(15),4990. https://doi.org/10.3390/ s21154990en_US
dc.identifier.issn1424-8220
dc.identifier.urihttps://doi.org/10.3390/ s21154990
dc.identifier.urihttp://hdl.handle.net/10566/7006
dc.description.abstractBruise damage is a very commonly occurring defect in apple fruit which facilitates disease occurrence and spread, leads to fruit deterioration and can greatly contribute to postharvest loss. The detection of bruises at their earliest stage of development can be advantageous for screening purposes. An experiment to induce soft bruises in Golden Delicious apples was conducted by applying impact energy at different levels, which allowed to investigate the detectability of bruises at their latent stage. The existence of bruises that were rather invisible to the naked eye and to a digital camera was proven by reconstruction of hyperspectral images of bruised apples, based on effective wavelengths and data dimensionality reduced hyperspectrograms. Machine learning classifiers, namely ensemble subspace discriminant (ESD), k-nearest neighbors (KNN), support vector machine (SVM) and linear discriminant analysis (LDA) were used to build models for detecting bruises at their latent stage, to study the influence of time after bruise occurrence on detection performance and to model quantitative aspects of bruises (severity), spanning from latent to visible bruises. Over all classifiers, detection models had a higher performance than quantitative ones. Given its highest speed in prediction and high classification performance, SVM was rated most recommendable for detection tasks.en_US
dc.language.isoenen_US
dc.publisherMPDIen_US
dc.subjectMachine learningen_US
dc.subjectBruise detectionen_US
dc.subjectClassification modelen_US
dc.subjectLatent damageen_US
dc.subjectApple fruiten_US
dc.titleClassification learning of latent bruise damage to apples using shortwave infrared hyperspectral imagingen_US
dc.typeArticleen_US


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