Our team

Articles in peer reviewed journals [45 Results]

• 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
• 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
• 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
• 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
• 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.; 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
• 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. (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
• de Camargo, T.; Schirrmann, M.; Landwehr, N.; Dammer, K.; Pflanz, M. (2021): Optimized Deep Learning Model as a Basis for Fast UAV Mapping of Weed Species in Winter Wheat Crops. Remote Sensing. (9): p. 1704. Online: https://doi.org/10.3390/rs13091704
• Bieganowski, A.; Dammer, K.; Siedliska, A.; Bzowska-Bakalarz, M.; Beres, P.; Dabrowska-Zielinska, K.; Pflanz, M.; Schirrmann, M.; Garz, A. (2021): Sensor-based outdoor monitoring of insects in arable crops for their precise control. Pest Management Science. : p. 1109-1114. Online: https://doi.org/10.1002/ps.6098

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 [46 Results]

• 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
• Dammer, K. (2016): Sensor-controlled field sprayer for target-orientated plant protection. In: Agroekosistemue w estestwennuech i reguliruemuech uslobijach. Proceedings. Agroekosistemue w estestwennuech i reguliruemuech uslobijach. p. 1-7.
• Intreß, J.; Geyer, M.; Dammer, K. (2015): Unterscheidung von Pflanzenarten anhand ihres Spektralpro-fils mittels einer Spektralen Datenbank am Beispiel von Bei-fußblättriger Ambrosie (Ambrosia artemisiifolia L.). In: Zude-Sasse, M.; Kraft, M.(eds.): Tagungsband. 21. Workshop Computer-Bildanalyse in der Landwirtschaft 3. Workshop Unbemannte autonom fliegende Systeme (UAS) in der Landwirtschaft. Eigenverlag, Potsdam, (0947-7314), p. 101-112. Online: https://opus4.kobv.de/opus4-slbp/frontdoor/index/index/searchtype/series/id/6/rows/10/start/17/docId/7595

Orals and posters [83 Results]

• Dammer, K. (2023): Vertikalsensoren im präzisen Pflanzenschutz für den Blick unter der Bestandesoberfläche - erste Ergebnisse in Getreide und Kartoffeln.
• Dammer, K. (2022): Präziser Pflanzenschutz - Bedeutung einer kleinräumigen sensorgestützen Erfassung von Schaderregern.
• Karimi, H.; Navid, H.; Dammer, K. (2022): A pixel-wise segmentation model to identify bur chervil (Anthriscus caucalis M. Bieb.) in cereal fields.
• 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.
• Dammer, K. (2022): Sensor-based precise crop protection.
• Dammer, K. (2021): Digitalisierung durch Sensoren für einen präzisen Pflanzenschutz.
• Dammer, K. (2019): Digitalisation in crop production - precision crop protection.
• Dammer, K. (2019): Sensorbasiertes Monitoring für eine zielgenaue Anwendung von Pflanzenschutzmitteln.
• 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.
• 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.