Our team

Articles in peer reviewed journals [45 Results]

• Wojcieszak, D.; Pawlowski, A.; Dammer, K.; Przybyl, J. (2023): Chemical and Energetical Properties in Methane Fermentation of Morphological Parts of Corn with Different Variety Earliness Standard FAO. Agricultural Engineering. (1): p. 273-287. Online: https://doi.org/10.2478/agriceng-2023-0020
• Dammer, K. (2023): Arbeitstagung Sensorgestützte Erkennung von Schaderregern in Freilandkulturen am Leibniz-Institut für Agrartechnik und Bioökonomie Potsdam-Bornim (ATB), 11. und 12. Mai 2022. Gesunde Pflanzen. : p. 1-4. Online: https://doi.org/10.1007/s10343-022-00799-9
• Karimi, H.; Navid, H.; Dammer, K. (2023): A Pixel-wise Segmentation Model to Identify Bur Chervil (Anthriscus caucalis M. Bieb.) Within Images from a Cereal Cropping Field. Gesunde Pflanzen. (1): p. 25-36. Online: https://doi.org/10.1007/s10343-022-00764-6
• 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
• Dammer, K. (2023): Methoden zur Erkennung des Kartoffelkäfers (Leptinotarsa decemlineata (Say))mit Multispektral- und Farbbildkamera-Sensoren. Gesunde Pflanzen. (1): p. 13-23. Online: https://doi.org/10.1007/s10343-022-00765-5
• Dammer, K. (2022): Proof of concept study - a novel mobile in-canopy imaging system for detecting symptoms of fungal diseases in cereals. Journal of Plant Diseases and Protection. (4): p. 769-773. Online: https://doi.org/10.1007/s41348-022-00638-z
• 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
• Dammer, K.; Schirrmann, M. (2022): Primarily tests of a optoelectronic in-canopy sensor for evaluation of vertical disease infection in cereals. Pest Management Science. (1): p. 143-149. Online: https://doi.org/10.1002/ps.6623
• Qin, Z.; Wang, W.; Dammer, K.; Guo, L.; Cao, Z. (2021): Ag-YOLO: A Real-Time Low-Cost Detector for Precise Spraying With Case Study of Palms. Frontiers in Plant Science. (December): p. 753603. Online: https://doi.org/10.3389/fpls.2021.753603
• Schirrmann, M.; Landwehr, N.; Giebel, A.; Garz, A.; Dammer, K. (2021): Early Detection of Stripe Rust in Winter Wheat using Deep Residual Neural Networks. Frontiers in Plant Science. : p. 469689. Online: https://doi.org/10.3389/fpls.2021.469689

Monographs by authorship [2 Results]

• Spaar, D.; Zaharenko, A.; Jukaschew, V.; Arefjeva, V.; Auernhammer, H.; Brunsch, R.; Wagner, P.; Wartenberg, G.; Wenkel, K.; Werner, A.; Woitjuk, D.; Gerhards, R.; Lysow, A.; Dohmen, B.; Kalenskaja, S.; Kaufmann, O.; Klotschkow, A.; Kochan, S.; Leithold, P.; Mazirow, M.; Michailenko, I.; Nash, E.; Nechai, A.; Nordmeyer, H.; Reckleben, Y.; Schein, J.; Herbst, R.; Schuhmann, P.; Ehlert, D.; Ellmer, F.; Dammer, K. (2009): Totschnoe selskoe choejaistwo (Precision Agriculture). Sankt Petersburg - Puschkin, (ISBN 978-5-93717-041-5), 397 S.
• Dammer, K. (2005): Demonstration der Langzeitwirkung bedarfsorientierter Fungizidbehandlung mit dem CROP-Meter. Bornimer Agrartechnische Berichte, Heft 41. Eigenverlag, Potsdam, (ISSN 0947-7314), 38 S.

Bookchapters and proceedings [48 Results]

• Dammer, K.; Dworak, V. (2024): Future perspectives of sensor-based real time fungicide and insecticide spraying from the point of view of the previous ATB results. In: Lorencowicz, E.; Uziak, J.; Huyghebaert, B.(eds.): Farm machinery and processes management in sustainable agriculture. Book fo abstracts. XII International Scientific Symposium Farm machinery and processes management in sustainable agriculture. Reprographic Centre, University of Life Science, Lublin, (978-83-937433-3-9), p. 85-0.
• Dammer, K. (2024): In-canopy sensing below the crop surface for reducing pesticide use in agricultural fields. In: 30th Jubilee Scientific Conference Scientific, Technical and Organizational Progress in Agriculture (Proceedings). 30th Jubilee Scientific Conference Scientific, Technical and Organizational Progress in Agriculture. WIR, Wydawnictwo, (978-83-64377-59-4), p. 19-0.
• Yutsis, A.; Zhelezova, S.; Dammer, K. (2019): Soil conditions and the iron chlorosis of mature vine. In: Proceedings "Key concepts of soil physics: development, future prospects and current applications". Key concepts of soil physics: development, future prospects and current applications. IOP, Moskau, p. 1-5. Online: https://iopscience.iop.org/article/10.1088/1755-1315/368/1/012057/pdf
• 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.
• Bzowska-Bakalarz, M.; Dabrowski, R.; Turos, P.; Dammer, K.; Sprawka, M.; Krawczuk, A. (2019): Spatial variability of hyperspectral indicators in relation to cultivation methods - study with the use of a gyrocopter-mounted remote sensing system. In: Lorencowicz, E.; Uziak, J.; Huyghebeart, B.(eds.): Farm machinery and processes management in sustainable agriculture. X internationl scientific symposium FMPMSA 2019. X International Scientific Symposium Farm machinery and processes management in sustainable agriculture. Instytut Naukowo-Wydawniczy "Spatium", Radom, (978-83-66017-74-0), p. 103-108.
• 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
• Dammer, K. (2018): Sensorgestützte online Detektion von Krankheiten im Getreide (FungiDetect). In: Tagungsband Innovationstage 2018 - innovative Ideen - smarte Produkte. Innovationstage 2018. p. 321-326. Online: https://ble-medienservice.de/frontend/esddownload/index/id/1163/on/1018/act/dl
• 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.
• Dammer, K.; Schirrmann, M. (2018): Variable-rate application of pesticides in cereals with a camera-operated field sprayer. In: Universytet Przyrodnuczy w Lublinie(eds.): Wspolczesne problemy inzynierii produkcji. Konferencja Naukowa. p. 13-13.
• Bzowska-Bakalarz, M.; Bieganowski, A.; Berés, P.; Dammer, K.; Ostroga, K.; Siekaniec, L.; Wieczorek, A. (2017): Monitoring the state of agrocenosis with the use of remote-sensing gyro system. In: Lorencowicz, E.; Uziak, J.; Huyghebaert, B.(eds.): Farm machinery and processes management in sustainable agriculture. IX International Scientific Symposium Farm Machinery and Processes Management in Sustainable Agriculture. University of Life Sciences in Lublin, Lublin, (978-83-937433-2-2), p. 64-69. Online: https://www.researchgate.net/publication/321997128_Monitoring_the_state_of_agrocenosis_with_use_of_remote-sensing_gyro_system

Orals and posters [85 Results]

• Dammer, K.; Dworak, V. (2024): Future perspectives of sensor-based real time fungicide and insecticide spraying from the point of view of the previous ATB results.
• Dammer, K. (2024): In-canopy sensing below the crop surface for reducing pesticide use in agricultural fields.
• Dammer, K. (2023): Vertikalsensoren im präzisen Pflanzenschutz für den Blick unter der Bestandesoberfläche - erste Ergebnisse in Getreide und Kartoffeln.
• Dammer, K. (2022): Sensor-based precise crop protection.
• Akhtari, H.; Navid, H.; Karimi, H.; Dammer, K. (2022): Deep learning-based object detection model for location and recognition weeds in cereal fields using color imagery.
• Karimi, H.; Navid, H.; Dammer, K. (2022): A pixel-wise segmentation model to identify bur chervil (Anthriscus caucalis M. Bieb.) in cereal fields.
• Dammer, K. (2022): Präziser Pflanzenschutz - Bedeutung einer kleinräumigen sensorgestützen Erfassung von Schaderregern.
• Dammer, K. (2021): Digitalisierung durch Sensoren für einen präzisen Pflanzenschutz.
• Yutsis, A.; Zhelezova, S.; Dammer, K. (2019): Soil conditions and the iron chlorosis of mature vine.
• Dammer, K.; Garz, A.; Schirrmann, M. (2019): Sensor-based detection of diseases in field crops.

Others [4 Results]

• Dammer, K. (2016): Mit Sensoren sparsamer spritzen. Bauernzeitung. Für Brandenburg, Mecklenburg-Vorpommern und Sachsen-Anhalt. p. 30-34.
• Dammer, K. (2015): Site specific fungicide.On-the-go systems to vary fungicide inputs are the focus of German research that could be of value to Australian grain growers. Precision Ag News. p. 17-18. Online: http://www.spaa.com.au/newsletter_details.php?newsletter=37
• Dammer, K. (2012): Herbizidapplikation unter Einsatz eines Kamerasensors. GetreideMagazin. p. 14-16.
• Idler, C.; Dammer, K.; Mellmann, J.; Hassenberg, K. (2010): Sensoren zur Erkennung und Vermeidung von Schimmelpilzen und Mykotoxinen in der Getreidekette. Mühle + Mischfutter. p. 354-357.