Stefan Paulus - Search Results

Year	Number of Results
2013	1
2014	4
2015	2
2019	1
2020	2
2021	2
2022	1
2023	3
2024	0

1

Measuring crops in 3D: using geometry for plant phenotyping.

Paulus S. Paulus S. Plant Methods. 2019 Sep 3;15:103. doi: 10.1186/s13007-019-0490-0. eCollection 2019. Plant Methods. 2019. PMID: 31497064 Free PMC article. Review.

2

Can Distributed Ledgers Help to Overcome the Need of Labeled Data for Agricultural Machine Learning Tasks?

Paulus S, Leiding B. Paulus S, et al. Plant Phenomics. 2023 Jul 10;5:0070. doi: 10.34133/plantphenomics.0070. eCollection 2023. Plant Phenomics. 2023. PMID: 37434757 Free PMC article. Review. No abstract available.

3

Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale.

Paulus S, Mahlein AK. Paulus S, et al. Gigascience. 2020 Aug 1;9(8):giaa090. doi: 10.1093/gigascience/giaa090. Gigascience. 2020. PMID: 32815537 Free PMC article. Review.

4

A Hyperspectral Library of Foliar Diseases of Wheat.

Bohnenkamp D, Behmann J, Paulus S, Steiner U, Mahlein AK. Bohnenkamp D, et al. Among authors: paulus s. Phytopathology. 2021 Sep;111(9):1583-1593. doi: 10.1094/PHYTO-09-19-0335-R. Epub 2021 Oct 14. Phytopathology. 2021. PMID: 33586995

5

Digital plant pathology: a foundation and guide to modern agriculture.

Kuska MT, Heim RHJ, Geedicke I, Gold KM, Brugger A, Paulus S. Kuska MT, et al. Among authors: paulus s. J Plant Dis Prot (2006). 2022;129(3):457-468. doi: 10.1007/s41348-022-00600-z. Epub 2022 Apr 28. J Plant Dis Prot (2006). 2022. PMID: 35502325 Free PMC article.

6

Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis.

Schunck D, Magistri F, Rosu RA, Cornelißen A, Chebrolu N, Paulus S, Léon J, Behnke S, Stachniss C, Kuhlmann H, Klingbeil L. Schunck D, et al. Among authors: paulus s. PLoS One. 2021 Aug 18;16(8):e0256340. doi: 10.1371/journal.pone.0256340. eCollection 2021. PLoS One. 2021. PMID: 34407122 Free PMC article.

7

Hyperspectral Imaging in the UV Range Allows for Differentiation of Sugar Beet Diseases Based on Changes in Secondary Plant Metabolites.

Brugger A, Yamati FI, Barreto A, Paulus S, Schramowsk P, Kersting K, Steiner U, Neugart S, Mahlein AK. Brugger A, et al. Among authors: paulus s. Phytopathology. 2023 Jan;113(1):44-54. doi: 10.1094/PHYTO-03-22-0086-R. Epub 2023 Jan 13. Phytopathology. 2023. PMID: 35904439

8

Disease Incidence and Severity of Cercospora Leaf Spot in Sugar Beet Assessed by Multispectral Unmanned Aerial Images and Machine Learning.

Barreto A, Ispizua Yamati FR, Varrelmann M, Paulus S, Mahlein AK. Barreto A, et al. Among authors: paulus s. Plant Dis. 2023 Jan;107(1):188-200. doi: 10.1094/PDIS-12-21-2734-RE. Epub 2023 Jan 24. Plant Dis. 2023. PMID: 35581914 Free article.

9

Statistical shape analysis of tap roots: a methodological case study on laser scanned sugar beets.

Heeren B, Paulus S, Goldbach H, Kuhlmann H, Mahlein AK, Rumpf M, Wirth B. Heeren B, et al. Among authors: paulus s. BMC Bioinformatics. 2020 Jul 29;21(1):335. doi: 10.1186/s12859-020-03654-8. BMC Bioinformatics. 2020. PMID: 32727350 Free PMC article.

10

Low-cost 3D systems: suitable tools for plant phenotyping.

Paulus S, Behmann J, Mahlein AK, Plümer L, Kuhlmann H. Paulus S, et al. Sensors (Basel). 2014 Feb 14;14(2):3001-18. doi: 10.3390/s140203001. Sensors (Basel). 2014. PMID: 24534920 Free PMC article.

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