001035348 001__ 1035348
001035348 005__ 20250203103434.0
001035348 037__ $$aFZJ-2025-00395
001035348 041__ $$aEnglish
001035348 1001_ $$0P:(DE-Juel1)164665$$aHe, Fang$$b0$$eCorresponding author
001035348 1112_ $$aEuropean Horticulture Congress$$cBucharest$$d2024-05-12 - 2024-05-16$$gEHC2024$$wRomania
001035348 245__ $$aSmart monitoring of the Arnica flower development for better harvest times
001035348 260__ $$c2024
001035348 3367_ $$0PUB:(DE-HGF)1$$2PUB:(DE-HGF)$$aAbstract$$babstract$$mabstract$$s1736934945_21562
001035348 3367_ $$033$$2EndNote$$aConference Paper
001035348 3367_ $$2BibTeX$$aINPROCEEDINGS
001035348 3367_ $$2DRIVER$$aconferenceObject
001035348 3367_ $$2DataCite$$aOutput Types/Conference Abstract
001035348 3367_ $$2ORCID$$aOTHER
001035348 520__ $$aIn biogenic value creation, medicinal plants are playing an important role including thecommercial cultivation to meet the growing global demand. This opens up newopportunities to improve harvest quantities through breeding, cultivation managementand harvesting techniques. An important aspect is the determination of optimal harvesttimes, which depend on the weather conditions, the state of the plant organ with regardto the harvesting process and the content of the active ingredients, which fluctuates overtime. In this study, we used the medicinal plant Arnica montana “Arbo” to examine theaspect of target compound yield of helenalin, dihydrohelenalin and their estersdepending on the life cycle of the flower, in order to develop methods for estimatingoptimal harvest time windows. A neural network was trained to classify seven stagesfrom time-lapse images in order to track the development of each flower stage. To gettypical content values, arnica plants were grown in the field and amounts of the two targetcompound classes were determined for each flower type. By combining both outcomes,it was possible to calculate the time course of the total amount of active compounds andthereby determine better harvest time windows. This method is interesting also for othercrops where external features can be used as a proxy for active compoundconcentrations.
001035348 536__ $$0G:(DE-HGF)POF4-2A6$$a2A6 - Bioeconomy  (CARF - CCA) (POF4-2A6)$$cPOF4-2A6$$fPOF IV$$x0
001035348 536__ $$0G:(DE-HGF)POF4-2171$$a2171 - Biological and environmental resources for sustainable use (POF4-217)$$cPOF4-217$$fPOF IV$$x1
001035348 65027 $$0V:(DE-MLZ)SciArea-160$$2V:(DE-HGF)$$aBiology$$x0
001035348 65017 $$0V:(DE-MLZ)GC-130-2016$$2V:(DE-HGF)$$aHealth and Life$$x0
001035348 7001_ $$0P:(DE-Juel1)142555$$aMüller-Linow, Mark$$b1
001035348 7001_ $$0P:(DE-Juel1)129349$$aKuhn, Arnd Jürgen$$b2
001035348 909CO $$ooai:juser.fz-juelich.de:1035348$$pVDB
001035348 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)164665$$aForschungszentrum Jülich$$b0$$kFZJ
001035348 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)142555$$aForschungszentrum Jülich$$b1$$kFZJ
001035348 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129349$$aForschungszentrum Jülich$$b2$$kFZJ
001035348 9131_ $$0G:(DE-HGF)POF4-2A6$$1G:(DE-HGF)POF4-2A0$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lCOOPERATION ACROSS RESEARCH FIELDS (CARFs)$$vBioeconomy  (CARF - CCA)$$x0
001035348 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$$x1
001035348 9141_ $$y2024
001035348 920__ $$lyes
001035348 9201_ $$0I:(DE-Juel1)IBG-2-20101118$$kIBG-2$$lPflanzenwissenschaften$$x0
001035348 980__ $$aabstract
001035348 980__ $$aVDB
001035348 980__ $$aI:(DE-Juel1)IBG-2-20101118
001035348 980__ $$aUNRESTRICTED