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001 | 1035348 | ||
005 | 20250203103434.0 | ||
037 | _ | _ | |a FZJ-2025-00395 |
041 | _ | _ | |a English |
100 | 1 | _ | |a He, Fang |0 P:(DE-Juel1)164665 |b 0 |e Corresponding author |
111 | 2 | _ | |a European Horticulture Congress |g EHC2024 |c Bucharest |d 2024-05-12 - 2024-05-16 |w Romania |
245 | _ | _ | |a Smart monitoring of the Arnica flower development for better harvest times |
260 | _ | _ | |c 2024 |
336 | 7 | _ | |a Abstract |b abstract |m abstract |0 PUB:(DE-HGF)1 |s 1736934945_21562 |2 PUB:(DE-HGF) |
336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
336 | 7 | _ | |a conferenceObject |2 DRIVER |
336 | 7 | _ | |a Output Types/Conference Abstract |2 DataCite |
336 | 7 | _ | |a OTHER |2 ORCID |
520 | _ | _ | |a In 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. |
536 | _ | _ | |a 2A6 - Bioeconomy (CARF - CCA) (POF4-2A6) |0 G:(DE-HGF)POF4-2A6 |c POF4-2A6 |f POF IV |x 0 |
536 | _ | _ | |a 2171 - Biological and environmental resources for sustainable use (POF4-217) |0 G:(DE-HGF)POF4-2171 |c POF4-217 |f POF IV |x 1 |
650 | 2 | 7 | |a Biology |0 V:(DE-MLZ)SciArea-160 |2 V:(DE-HGF) |x 0 |
650 | 1 | 7 | |a Health and Life |0 V:(DE-MLZ)GC-130-2016 |2 V:(DE-HGF) |x 0 |
700 | 1 | _ | |a Müller-Linow, Mark |0 P:(DE-Juel1)142555 |b 1 |
700 | 1 | _ | |a Kuhn, Arnd Jürgen |0 P:(DE-Juel1)129349 |b 2 |
909 | C | O | |o oai:juser.fz-juelich.de:1035348 |p VDB |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)164665 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 1 |6 P:(DE-Juel1)142555 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)129349 |
913 | 1 | _ | |a DE-HGF |b Forschungsbereich Erde und Umwelt |l COOPERATION ACROSS RESEARCH FIELDS (CARFs) |1 G:(DE-HGF)POF4-2A0 |0 G:(DE-HGF)POF4-2A6 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-200 |4 G:(DE-HGF)POF |v Bioeconomy (CARF - CCA) |x 0 |
913 | 1 | _ | |a DE-HGF |b Forschungsbereich Erde und Umwelt |l Erde im Wandel – Unsere Zukunft nachhaltig gestalten |1 G:(DE-HGF)POF4-210 |0 G:(DE-HGF)POF4-217 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-200 |4 G:(DE-HGF)POF |v Für eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten |9 G:(DE-HGF)POF4-2171 |x 1 |
914 | 1 | _ | |y 2024 |
920 | _ | _ | |l yes |
920 | 1 | _ | |0 I:(DE-Juel1)IBG-2-20101118 |k IBG-2 |l Pflanzenwissenschaften |x 0 |
980 | _ | _ | |a abstract |
980 | _ | _ | |a VDB |
980 | _ | _ | |a I:(DE-Juel1)IBG-2-20101118 |
980 | _ | _ | |a UNRESTRICTED |
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