Vis enkel innførsel

dc.contributor.authorZhao, Jiangsan
dc.contributor.authorKechasov, Dmitry
dc.contributor.authorRewald, Boris
dc.contributor.authorBodner, Gernot
dc.contributor.authorVerheul, Michel
dc.contributor.authorClarke, Nicholas
dc.contributor.authorClarke, Jihong Liu
dc.date.accessioned2021-01-28T13:17:36Z
dc.date.available2021-01-28T13:17:36Z
dc.date.created2020-10-08T06:41:21Z
dc.date.issued2020-10-07
dc.identifier.citationRemote Sensing. 2020, 12 (19), .en_US
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/11250/2725210
dc.description.abstractHyperspectral imaging has many applications. However, the high device costs and low hyperspectral image resolution are major obstacles limiting its wider application in agriculture and other fields. Hyperspectral image reconstruction from a single RGB image fully addresses these two problems. The robust HSCNN-R model with mean relative absolute error loss function and evaluated by the Mean Relative Absolute Error metric was selected through permutation tests from models with combinations of loss functions and evaluation metrics, using tomato as a case study. Hyperspectral images were subsequently reconstructed from single tomato RGB images taken by a smartphone camera. The reconstructed images were used to predict tomato quality properties such as the ratio of soluble solid content to total titratable acidity and normalized anthocyanin index. Both predicted parameters showed very good agreement with corresponding “ground truth” values and high significance in an F test. This study showed the suitability of hyperspectral image reconstruction from single RGB images for fruit quality control purposes, underpinning the potential of the technology—recovering hyperspectral properties in high resolution—for real-world, real time monitoring applications in agriculture any beyond.en_US
dc.language.isoengen_US
dc.publisherMDPI, Basel, Switzerlanden_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleDeep Learning in Hyperspectral Image Reconstruction from Single RGB images—A Case Study on Tomato Quality Parametersen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.rights.holder© 2020 by the authorsen_US
dc.source.pagenumber14en_US
dc.source.volume12en_US
dc.source.journalRemote Sensingen_US
dc.source.issue19en_US
dc.identifier.doi10.3390/rs12193258
dc.identifier.cristin1838082
dc.relation.projectEC/H2020/N 774233en_US
dc.relation.projectNorges forskningsråd: 297301en_US
dc.relation.projectNorges forskningsråd: 255613en_US
dc.relation.projectUtenriksdepartementet: CHN-2152, CHN-17/0019en_US
dc.source.articlenumber3258en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


Tilhørende fil(er)

Thumbnail

Denne innførselen finnes i følgende samling(er)

Vis enkel innførsel

Navngivelse 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Navngivelse 4.0 Internasjonal