Gesunde Lebensmittel

Aufsätze in referierten Fachzeitschriften [45 Ergebnisse]

• Shamshiri, R.; Sturm, B.; Weltzien, C.; Fulton, J.; Khosla, R.; Schirrmann, M.; Raut, S.; Hanike Basavegowda, D.; Yamin, M.; Hameed, I. (2024): Digitalization of agriculture for sustainable crop production: a use-case review. Frontiers in Environmental Science. : p. 1-32. Online: https://doi.org/10.3389/fenvs.2024.1375193
• Hobart, M.; Schirrmann, M.; Abubakari, A.; Badu-Marfo, G.; Kraatz, S.; Zare, M. (2024): Drought Monitoring and Prediction in Agriculture: Employing Earth Observation Data, Climate Scenarios and Data Driven Methods; a Case Study: Mango Orchard in Tamale, Ghana. Remote Sensing. (11): p. 1942. Online: https://www.mdpi.com/2072-4292/16/11/1942
• Darvishi, A.; Yousefi, M.; Schirrmann, M.; Ewert, F. (2024): Exploring biodiversity patterns at the landscape scale by linking landscape energy and land use/land cover heterogeneity. Science of the Total Environment. : p. 170163. Online: https://doi.org/10.1016/j.scitotenv.2024.170163
• Alirezazadeh, P.; Schirrmann, M.; Stolzenburg, F. (2023): A comparative analysis of deep learning methods for weed classification of high-resolution UAV images. Journal of Plant Diseases and Protection. : p. 227-236. Online: https://doi.org/10.1007/s41348-023-00814-9
• Salamut, C.; Kohnert, I.; Landwehr, N.; Pflanz, M.; Schirrmann, M.; Zare, M. (2023): Deep Learning Object Detection for Image Analysis of Cherry Fruit Fly (Rhagoletis cerasi L.) on Yellow Sticky Traps. Gesunde Pflanzen. (1): p. 37-48. Online: https://doi.org/10.1007/s10343-022-00794-0
• Alirezazadeh, P.; Schirrmann, M.; Stolzenburg, F. (2023): Improving Deep Learning-based Plant Disease Classification with Attention Mechanism. Gesunde Pflanzen. (1): p. 49-59. Online: https://doi.org/10.1007/s10343-022-00796-y
• Tang, Z.; Wang, M.; Schirrmann, M.; Dammer, K.; Li, X.; Brueggeman, R.; Sankaran, S.; Carter, A.; Pumphrey, M.; Hu, Y.; Chen, X.; Zhang, Z. (2023): Affordable High Throughput Field Detection of Wheat Stripe Rust Using Deep Learning with Semi-Automated Image Labeling. Computers and Electronics in Agriculture. (April): p. 107709. Online: https://doi.org/10.1016/j.compag.2023.107709
• Li, M.; Shamshiri, R.; Weltzien, C.; Schirrmann, M. (2022): Crop Monitoring Using Sentinel-2 and UAV Multispectral Imagery: A Comparison Case Study in Northeastern Germany. Remote Sensing. (17): p. 4426. Online: https://doi.org/10.3390/rs14174426
• Li, M.; Shamshiri, R.; Schirrmann, M.; Weltzien, C.; Shafian, S.; Laursen, M. (2022): UAV Oblique Imagery with an Adaptive Micro-Terrain Model for Estimation of Leaf Area Index and Height of Maize Canopy from 3D Point Clouds. Remote Sensing. (2): p. 585. Online: https://doi.org/10.3390/rs14030585
• Dammer, K.; Garz, A.; Hobart, M.; Schirrmann, M. (2022): Combined UAV- and tractor-based stripe rust monitoring in winter wheat under field conditions. Agronomy Journal. (1): p. 651-661. Online: https://doi.org/10.1002/agj2.20916

Monografien nach Autorenschaft [2 Ergebnisse]

• Schirrmann, M. (2012): Potentials of soil sampling, proximal soil sensing and spatial prediction for mapping soil fertility parameters at field scale. Hochschulschrift Berlin, Humboldt-Universität, Berlin, 151 S. Online: https://portal.dnb.de/opac.htm;jsessionid=806BA2655A7A4A9867B3A9C0CAC00E98.prod-worker4?method=showFullRecord¤tResultId=schirrmann%26any¤tPosition=1
• Domsch, H.; Schirrmann, M. (2009): Teilflächenspezifische Grunddüngung. Bornimer agrartechnische Berichte, Heft 72. Eigenverlag, Potsdam, (ISSN 0947-7314), 130 S.

Monografien nach Herausgeberschaft [3 Ergebnisse]

• Pflanz, M.; Behmann, J.; Klingbeil, L.; Schirrmann, M.; Hobart, M.; Praeger, U. (2019): Proceedings 25. Workshop Computer-Bildanalyse in der Landwirtschaft. Bornimer Agrartechnische Berichte. Heft 102. 25. Workshop Computer-Bildanalyse in der Landwirtschaft. Leibniz-Institut für Agrartechnik und Bioökonomie e.V. (ATB), Potsdam, (0947-7314), 209 S.
• Kraft, M.; Pflanz, M.; Schirrmann, M. (2018): 24. Workshop Computer-Bildanalyse in der Landwirtschaft. Bornimer Agrartechnische Berichte, Heft 99. 24. Workshop Computer-Bildanalyse in der Landwirtschaft. Leibniz-Institut für Argartechnik und Bioökonomie, Potsdam-Bornim, (ISSN 0947-7314), 122 S. Online: https://www.atb-potsdam.de/fileadmin/docs/BABs/Heft_99_Workshop_Computerbildanalyse_Landwirtschaft.pdf
• Stolzenburg, F.; Pundt, H.; Pflanz, M.; Schirrmann, M. (2017): 22. Workshop Computer-Bildanalyse und Unbemannte autonom fliegende Systeme in der Landwirtschaft - 23. Workshop Computer-Bildanalyse in der Landwirtschaft. Bornimer Agrartechnische Berichte, Heft 93. 23. Workshop Computer-Bildanalyse in der Landwirtschaft. Leibniz-Institut für Agrartechnik und Bioökonomie, Potsdam-Bornim, (ISSN 0947-7314), 393 S. Online: https://www.atb-potsdam.de/fileadmin/docs/Publikationen/Heft_93_kl.pdf

Beiträge zu Sammelwerken [25 Ergebnisse]

• 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.

Vorträge und Poster [53 Ergebnisse]

• Schirrmann, M. (2024): Crop monitoring with Unmanned Aerial Vehicles (UAV).
• Schirrmann, M. (2024): Weed AI Seek. Development of an intelligent UAV based weed mapping system for selective and site specific herbicide application.
• Zare, M.; Hobart, M.; Boussadia, O.; Ben-Hamouda, A.; Chaieb, N.; Ellßel, P.; Schirrmann, M. (2023): Drought monitoring/prediction using remotely-sensed data and SSP climate change scenarios in a Tunisian olive orchard.
• Alirezazadeh, P.; Schirrmann, M.; Stolzenburg, F. (2023): Weed detection in winter wheat field using improved-YOLOv4 with attention module from UAV imagery.
• Hobart, M.; Giebel, A.; Schirrmann, M. (2023): Plant health assessment with thermal and multi-spectral UAV imagery in winter rye crops.
• Zare, M.; Hobart, M.; Abubakari, A.; Issahaku, G.; Anin-Adjei, E.; Badu-Marfo, G.; Schirrmann, M. (2023): Drought monitoring and prediction for mango orchard in Tamale, Ghana with earth observation data and SSP climate scenarios.
• Schirrmann, M. (2023): Crop monitoring with Unmanned Aerial Vehicles (UAV).
• Ben Hamouda, A.; Boussadia, O.; Saussure, S.; Ellssel, P.; Schirrmann, M.; Hobart, M.; Young, G. (2022): Integrating agro-ecological practices and smart farming to improve agricultural production and insect pest control.
• Hobart, M.; Adjei, E.; Hanyabui, E.; Badu-Marfo, G.; Schiller, N.; Schirrmann, M. (2022): Photogrammetrically assessed smallholder pineapple fields in Ghana using small unmanned aircraft systems.
• Zare, M.; Pflanz, M.; Schirrmann, M. (2022): Introducing a smart monitoring system (PHLIP) for integrated pest management in commercial orchards.

Sonstige Artikel [1 Ergebnis]

• Schirrmann, M.; Gebbers, R.; Berg, W.; Kramer, E.; Palme, S. (2014): Ist der Boden sauer?. Forschungsfelder (früher Forschungsreport). p. 4-5.