Dr. rer. nat. Michael Schirrmann
Book chapters and proceedings [25 Results]
- Alirezazadeh, P.; Schirrmann, M.; Stolzenburg, F. (2023): Weed detection in winter wheat field using improved-YOLOv4 with attention module from UAV imagery. In: Stafford, J.(eds.): Precision Agriculture ´23, Papers presented at the 14th European Conference on Precision Agriculture. 14th European Conference on Precision Agriculture (ECPA 2023). Wageningen Academic Publishers, Wageningen, p. 369-376.
- Hobart, M.; Giebel, A.; Schirrmann, M. (2023): Plant health assessment with thermal and multi-spectral UAV imagery in winter rye crops. In: Stafford, J.(eds.): Precision Agriculture ´23, Papers presented at the 14th European Conference on Precision Agriculture. 14th European Conference on Precision Agriculture (ECPA 2023). Wageningen Academic Publishers, Wageningen, p. 917-924.
- Zare, M.; Pflanz, M.; Schirrmann, M. (2023): Introducing a smart monitoring system (PHLIP) for integrated pest management in commercial orchards. In: Yousaf, A.(eds.): Proceedings der 2022 International Conference on Engineering and Emerging Technologies (ICEET). 8th International Conference on Engineering and Emerging Technologies (ICEET 2022). IEEE Conference Operations, Piscataway, (2831-3682/978-1-6654-9106-8), p. 1-4. Online: https://doi.org/10.1109/ICEET56468.2022.10007399
- Hobart, M.; Anin-Adjei, E.; Hanyabui, E.; Badu-Marfo, G.; Schiller, N.; Schirrmann, M. (2022): Photogrammetrically Assessed Smallholder Pineapple Fields in Ghana Using Small Unmanned Aircraft Sysytems. In: Proceedings of the 2nd African Conference on Precision Agriculture (AfCPA). 2nd African Conference on Precision Agriculture. African Plant Nutrition Institute, Benguérir, Morocco, p. 209-212. Online: https://paafrica.org/proceedings/?action=download&item=9439
- Dammer, K.; Garz, A.; Schirrmann, M. (2019): Sensor-based detection of diseases in field crops. In: Lorencowicz, E.; Uziak, J.; Huyghebeart, B.(eds.): Farm machinery and processes management in sustainable agriculture. X International Scientific Symposium Farm machinery and processes management in sustainable agriculture. Instytut Naukowo-Wydawniczy "Spatium", Radom, (978-83-66017-74-0), p. 115-120.
- Ustyuzhanin, A.; Dammer, K.; Schirrmann, M. (2019): A universal model for non-destructive estimating the wheat biomass. In: Blokhina, S.; Ageenkova, O.; Tsivilev, A.(eds.): Proceedings of the 2nd International Conference "Agrophysical Trends: From Actual Challenges in Arable Farming and Crop Growing towards Advanced Technologies". 2nd International Conference "Agrophysical trends: From actual Challenges in Arable Farming and Crop Growing towards Advanced Technologies". St. Petersburg, (978-5-905200-40-3), p. 520-525. Online: http://www.agrophys.ru/Media/Default/Conferences/2019/sbornik_AFI_2019.pdf
- Hobart, M.; Schirrmann, M.; Pflanz, M. (2019): 3D point clouds from UAV imagery for precise plant protection in fruit orchards. In: Stafford(eds.): Precision agriculture ’19. 12th European Conference on Precision Agriculture. Wageningen Academic Publishers, Wageningen, (978-90-8686-337-2), p. 109-114. Online: https://www.wageningenacademic.com/doi/abs/10.3920/978-90-8686-888-9_12
- Pflanz, M.; Schirrmann, M.; Wellhausen, C.; Nordmeyer, H. (2019): Automatisierte flugrobotergestützte Unkrauterkennung als Voraussetzung für eine teilflächenspezifische Herbizidbehandlung im Ackerbau. In: Behmann, J.; Klingbeil, L.; Pflanz, M.(eds.): 25. Workshop Computer-Bildanalyse in der Landwirtschaft. 25. Workshop Computer-Bildanalyse in der Landwirtschaft. Eigenverlag, Potsdam, (ISSN 0947-7314), p. 95-106. Online: https://opus4.kobv.de/opus4-slbp/frontdoor/index/index/searchtype/series/id/6/rows/10/start/1/docId/15092
- Hobart, M.; Schirrmann, M.; Pflanz, M. (2019): Automatische Baumidentifizierung und Baumhöhenbestimmung zur Erstellung präziser Applikationskarten. In: Behmann, J.; Klingbeil, L.; Pflanz, M.(eds.): 25. Workshop Computer-Bildanalyse in der Landwirtschaft. 25. Workshop Computer-Bildanalyse in der Landwirtschaft. Eigenverlag, Potsdam, (ISSN 0947-7314), p. 21-28. Online: https://opus4.kobv.de/opus4-slbp/frontdoor/index/index/searchtype/series/id/6/rows/10/start/1/docId/15092
- Schirrmann, M.; Ustyuzhanin, A.; Giebel, A.; Dammer, K. (2018): Chapter III/42: Convolutional Neural Network for Identifyinf Common Ragweed from Digital Images. In: Müller, L.; Sychev, V.(eds.): Novel Methods and Results of Landscape Research in Europe, Central Asia and Siberia (in five volumes). Vol. 3. Landscape Monitoring and Modelling. . Publishing House FSBSI "Pryanishnikov Institute of Agrochemistry", Moskau, (ISSN 978-5-9238-0246-7), p. 201-204.