000916363 001__ 916363
000916363 005__ 20231027114350.0
000916363 0247_ $$2doi$$a10.1002/rob.22122
000916363 0247_ $$2ISSN$$a1556-4959
000916363 0247_ $$2ISSN$$a0741-2223
000916363 0247_ $$2ISSN$$a1097-4563
000916363 0247_ $$2ISSN$$a1556-4967
000916363 0247_ $$2Handle$$a2128/33859
000916363 0247_ $$2WOS$$aWOS:000868525100001
000916363 037__ $$aFZJ-2022-06164
000916363 041__ $$aEnglish
000916363 082__ $$a620
000916363 1001_ $$00000-0003-1145-1555$$aKierdorf, Jana$$b0$$eCorresponding author
000916363 245__ $$aGrowliFlower: An image time‐series dataset for GROWth analysis of cauLIFLOWER
000916363 260__ $$aNew York, NY$$bWiley$$c2023
000916363 3367_ $$2DRIVER$$aarticle
000916363 3367_ $$2DataCite$$aOutput Types/Journal article
000916363 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1675435055_29945
000916363 3367_ $$2BibTeX$$aARTICLE
000916363 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000916363 3367_ $$00$$2EndNote$$aJournal Article
000916363 520__ $$aIn this paper, we present GrowliFlower, a georeferenced, image-based unmanned aerial vehicle time-series dataset of two monitored cauliflower fields (0.39 and 0.60 ha) acquired in 2 years, 2020 and 2021. The proposed dataset contains RGB and multispectral orthophotos with coordinates of approximately 14,000 individual cauliflower plants. The coordinates enable the extraction of complete and incomplete time-series of image patches showing individual plants. The dataset contains the collected phenotypic traits of 740 plants, including the developmental stage and plant and cauliflower size. The harvestable product is completely covered by leaves, thus, plant IDs and coordinates are provided to extract image pairs of plants pre- and post-defoliation. In addition, to facilitate classification, detection, segmentation, instance segmentation, and other similar computer vision tasks, the proposed dataset contains pixel-accurate leaf and plant instance segmentations, as well as stem annotations. The proposed dataset was created to facilitate the development and evaluation of various machine-learning approaches. It focuses on the analysis of growth and development of cauliflower and the derivation of phenotypic traits to advance automation in agriculture. Two baseline results of instance segmentation tasks at the plant and leaf level based on labeled instance segmentation data are presented. The complete GrowliFlower dataset is publicly available (http://rs.ipb.uni-bonn.de/data/growliflower/).
000916363 536__ $$0G:(DE-HGF)POF4-2171$$a2171 - Biological and environmental resources for sustainable use (POF4-217)$$cPOF4-217$$fPOF IV$$x0
000916363 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
000916363 7001_ $$0P:(DE-Juel1)168454$$aJunker-Frohn, Laura Verena$$b1$$ufzj
000916363 7001_ $$0P:(DE-HGF)0$$aDelaney, Mike$$b2
000916363 7001_ $$0P:(DE-HGF)0$$aOlave, Mariele Donoso$$b3
000916363 7001_ $$0P:(DE-Juel1)145906$$aBurkart, Andreas$$b4
000916363 7001_ $$0P:(DE-HGF)0$$aJaenicke, Hannah$$b5
000916363 7001_ $$0P:(DE-Juel1)161185$$aMuller, Onno$$b6$$ufzj
000916363 7001_ $$0P:(DE-Juel1)129388$$aRascher, Uwe$$b7$$ufzj
000916363 7001_ $$0P:(DE-Juel1)186079$$aRoscher, Ribana$$b8
000916363 773__ $$0PERI:(DE-600)2224269-7$$a10.1002/rob.22122$$gp. rob.22122$$n2$$p173-192$$tJournal of field robotics$$v40$$x1556-4959$$y2023
000916363 8564_ $$uhttps://juser.fz-juelich.de/record/916363/files/Journal%20of%20Field%20Robotics%20-%202022%20-%20Kierdorf%20-%20GrowliFlower%20An%20image%20time%E2%80%90series%20dataset%20for%20GROWth%20analysis%20of%20cauLIFLOWER.pdf$$yOpenAccess
000916363 909CO $$ooai:juser.fz-juelich.de:916363$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000916363 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)168454$$aForschungszentrum Jülich$$b1$$kFZJ
000916363 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)161185$$aForschungszentrum Jülich$$b6$$kFZJ
000916363 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129388$$aForschungszentrum Jülich$$b7$$kFZJ
000916363 9131_ $$0G:(DE-HGF)POF4-217$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2171$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vFür eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten$$x0
000916363 9141_ $$y2023
000916363 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2022-11-19
000916363 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000916363 915__ $$0StatID:(DE-HGF)3001$$2StatID$$aDEAL Wiley$$d2022-11-19$$wger
000916363 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2022-11-19
000916363 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000916363 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2023-10-25
000916363 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2023-10-25
000916363 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2023-10-25
000916363 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2023-10-25
000916363 915__ $$0StatID:(DE-HGF)1160$$2StatID$$aDBCoverage$$bCurrent Contents - Engineering, Computing and Technology$$d2023-10-25
000916363 920__ $$lyes
000916363 9201_ $$0I:(DE-Juel1)IBG-2-20101118$$kIBG-2$$lPflanzenwissenschaften$$x0
000916363 980__ $$ajournal
000916363 980__ $$aVDB
000916363 980__ $$aUNRESTRICTED
000916363 980__ $$aI:(DE-Juel1)IBG-2-20101118
000916363 9801_ $$aFullTexts