Downloads: 31

Files in This Item:
File Description SizeFormat 
1343943X.2023.2210767.pdf4.78 MBAdobe PDFView/Open
Full metadata record
DC FieldValueLanguage
dc.contributor.authorNakajima, Kotaen
dc.contributor.authorTanaka, Yuen
dc.contributor.authorKatsura, Keisukeen
dc.contributor.authorYamaguchi, Tomoakien
dc.contributor.authorWatanabe, Tomoyaen
dc.contributor.authorShiraiwa, Tatsuhikoen
dc.contributor.alternative中嶌, 洸太ja
dc.contributor.alternative田中, 佑ja
dc.contributor.alternative白岩, 立彦ja
dc.date.accessioned2023-11-13T08:17:37Z-
dc.date.available2023-11-13T08:17:37Z-
dc.date.issued2023-05-
dc.identifier.urihttp://hdl.handle.net/2433/286020-
dc.description.abstractAbove-ground biomass (AGB) is an important indicator of crop productivity. Destructive measurements of AGB incur huge costs, and most non-destructive estimations cannot be applied to diverse cultivars having different canopy architectures. This insufficient access to AGB data has potentially limited improvements in crop productivity. Recently, a deep learning technique called convolutional neural network (CNN) has been applied to estimate crop AGB due to its high capacity for digital image recognition. However, the versatility of the CNN-based AGB estimation for diverse cultivars is still unclear. We established and evaluated a CNN-based estimation method for rice AGB using digital images with 59 diverse cultivars which were mostly in World Rice Core Collection. Across two years at two locations, we took 12, 183 images of 59 cultivars with commercial digital cameras and manually obtained their corresponding AGB. The CNN model was established by using 28 cultivars and showed high accuracy (R2 = 0.95) to the test dataset. We further evaluated the performance of the CNN model by using 31 cultivars, which were not in the model establishment. The CNN model successfully estimated AGB when the observed AGB was lesser than 924 g m−2 (R2 = 0.87), whereas it underestimated AGB when the observed AGB was greater than 924 g m−2 (R2 = 0.02). This underestimation might be improved by adding training data with a greater AGB in further study. The present study indicates that this CNN-based estimation method is highly versatile and could be a practical tool for monitoring crop AGB in diverse cultivars.en
dc.language.isoeng-
dc.publisherTaylor & Francis Groupen
dc.rights© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.en
dc.rightsThis is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License, which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.en
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/-
dc.subjectAbove-ground biomassen
dc.subjectBiomass estimationen
dc.subjectConvolutional neural networken
dc.subjectDigital imageen
dc.subjectRiceen
dc.subjectWorld rice core collectionen
dc.titleBiomass estimation of World rice (Oryza sativa L.) core collection based on the convolutional neural network and digital images of canopyen
dc.typejournal article-
dc.type.niitypeJournal Article-
dc.identifier.jtitlePlant Production Scienceen
dc.identifier.volume26-
dc.identifier.issue2-
dc.identifier.spage187-
dc.identifier.epage196-
dc.relation.doi10.1080/1343943X.2023.2210767-
dc.textversionpublisher-
dcterms.accessRightsopen access-
datacite.awardNumber21K19104-
datacite.awardNumber.urihttps://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-21K19104/-
dc.identifier.pissn1343-943X-
dc.identifier.eissn1349-1008-
jpcoar.funderName日本学術振興会ja
jpcoar.awardTitle時系列並列解析によるイネ光合成誘導の多様性および遺伝要因の解明ja
Appears in Collections:Journal Articles

Show simple item record

Export to RefWorks


Export Format: 


This item is licensed under a Creative Commons License Creative Commons