Wissenschaftliche Publikationen

Auswahl relevanter Publikationen

Lück, Stefanie; Strickert, Marc; Lorbeer, Maximilian; Melchert, Friedrich; Backhaus, Andreas; Kilias, David; Seiffert, Udo; Douchkov, Dimitar: “Macrobot”: An Automated Segmentation-Based System for Powdery Mildew Disease Quantification. Plant Phenomics (2020)

Bendel, Nele; Kicherer, Anna; Backhaus, Andreas, Klück, Hans-Christian; Seiffert, Udo; Fischer, Michael; Voegele, Ralf T.; Töpfer, Reinhard: Evaluating the suitability of hyer- and multispectral imaging to detect foliar symptoms of the grapevine trunk disease Esca in vineyards. Plant Methods (2020)

Bendel, Nele; Kicherer, Anna; Backhaus, Andreas; Köckerling, Janine; Maixner, Michael; Bleser, Elvira; Klück, Hans-Christian; Seiffert, Udo; Voegele, Ralf; Töpfer, Reinhard: Detection of Grapevine Leafroll-associated Virus 1 and 3 in White and Red Grapevine Cultivars Using Hyperspectral Imaging. Remote Sensing 12(10), 1693 (2020)

Knauer, Uwe; Styp von Reckowski, Cornelius; Stecklina, Marianne; Krokotsch, Tilman; Minh; Tuan Pham; Hauffe, Viola; Kilias, David; Ehrhardt, Ina; Sagischewski, Herbert; Chmara; Sergej; Seiffert, Udo: Tree Species Classification Based on Hybrid Ensembles of a Convolutional Neural Network (CNN) and Random Forest Classifiers. Remote Sensing (2019)

Menz, Patrick; Backhaus, Andreas; Seiffert, Udo: Transfer learning for transferring machine-learning based models among hyperspectral sensors.  Proceedings of the 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 589–594 (2019)

Herzig, Paul; Backhaus, Andreas; Seiffert, Udo; von Wirèn, Nicolaus; Pillen, Klaus; Maurer, Andreas: Genetic dissection of grain elements predicted by hyperspectral imaging associated with yield-related traits in a wild barley NAM population. Plant Science 285, 151–164 (2019)

Matros, Andrea; Peukert, Manuela; Lahnstein, Jelle; Seiffert, Udo; Burton, Rachel: Determination of fructans in plants: Current analytical means for extraction, detection and quantification. Annual plants reviews, 1–39. (2019)

Behnaz, Soleimani; Ralf, Sammler; Backhaus, Andreas; Beschow, Heidrun; Schumann, Erika; Mock, Hans-Peter; von Wirèn, Nicolaus; Seiffert, Udo; Pillen, Klaus: Genetic regulation of growth and nutrient content under phosphorus deficiency in the wild barley introgression library S42IL. Plant Breeding 136, 892-907 (2017)

Kicherer, Anna; Herzog, Katja; Bendel, Nele; Klück, Hans-Christian; Backhaus, Andreas; Wieland, Markus; Rose, Johann Christian; Klingbeil, Lasse; Läbe, Thomas; Hohl, Christian; Petry, Willi; Kuhlmann, Heiner; Seiffert, Udo; Töpfer, Reinhard: Phenoliner: A new field phenotyping platform for grapevine research, Sensors. Online journal 17, 1625, 18 (2017)

Knauer, Uwe; Matros, Andrea; Petrovic, Tijana; Zanker, Timothy; Scott, Eileen; Seiffert, Udo: Improved classification accuracy of powdery mildew infection levels of wine grapes by spatial-spectral analysis of hyperspectral images. Plant Methods 13, 15 (2017)

Peukert, Manuela; Lim, Wai Li; Seiffert, Udo; Matros, Andrea: Mass spectrometry imaging of metabolites in barley grain tissues. Current Protocols in Plant Biology, 574-591 (2016)

Arens, Nadja; Backhaus, Andreas; Döll, Stefanie; Fischer, Sandra; Seiffert, Udo; Mock, Hans-Peter: Non-invasive presymptomatic detection of Cercospora beticola infection and identification of early metabolic responses in sugar beet. Frontiers in Plant Science 7, 1377 (2016)

Kaspar-Schoenefeld, Stephanie; Merx, Kathleen; Jozefowicz, Anna Maria; Hartmann, Anja; Seiffert, Udo; Weschke, Winfriede; Matros, Andrea; Mock, Hans-Peter: Label-free proteome profiling reveals developmental-dependent patterns in young barley grains. Journal of Proteomics 143, 106-121 (2016)

Melchert, Friedrich; Seiffert, Udo; Biehl, Michael: Functional representation of prototypes in LVQ and relevance learning. Advances in self-organizing maps and learning vector quantization, 428, 317-327 (2016)

Knauer, Uwe; Backhaus, Andreas; Seiffert, Udo: Fusion trees for fast and accurate classification of hyperspectral data with ensembles of γ-divergence-based RBF networks. Neural Computing and Applications, 25, 1-10 (2014)

Backhaus, Andreas; Seiffert, Udo: Classification in high-dimensional spectral data: Accuracy vs. interpretability vs. model size. Neurocomputing 131, 15-22 (2014)

Douchkov, Dimitar; Baum, Tobias; Ihlow, Alexander; Schweizer, Patrick; Seiffert, Udo: Microphenomics for interactions of barley with fungal pathogens, Genomics of plant genetic resources. 123-148 (2014)

Seiffert, Udo: ANNIE—Artificial Neural Network-based Image Encoder, Neurocomputing, 125, 229-235 (2014)

Scharf, Stefan; Sander, Bastian; Kujath, Marc; Richter, Hans; Riedel, Eric; Stein, Hagen; tom Felde, Joerg: FOUNDRY 4.0: An innovative technology for sustainable and flexible process design in foundries. Procedia CIRP, 73-78 (2021)