| Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective S Thomas, MT Kuska, D Bohnenkamp, A Brugger, E Alisaac, ... Journal of Plant Diseases and Protection 125, 5-20, 2018 | 349 | 2018 |
| Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions M Kuska, M Wahabzada, M Leucker, HW Dehne, K Kersting, EC Oerke, ... Plant methods 11, 1-15, 2015 | 203 | 2015 |
| Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants M Wahabzada, AK Mahlein, C Bauckhage, U Steiner, EC Oerke, ... Scientific reports 6 (1), 22482, 2016 | 157 | 2016 |
| Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis C Römer, M Wahabzada, A Ballvora, F Pinto, M Rossini, C Panigada, ... Functional Plant Biology 39 (11), 878-890, 2012 | 149 | 2012 |
| Metro maps of plant disease dynamics—automated mining of differences using hyperspectral images M Wahabzada, AK Mahlein, C Bauckhage, U Steiner, EC Oerke, ... Plos one 10 (1), e0116902, 2015 | 139 | 2015 |
| Automated interpretation of 3D laserscanned point clouds for plant organ segmentation M Wahabzada, S Paulus, K Kersting, AK Mahlein BMC bioinformatics 16, 1-11, 2015 | 110 | 2015 |
| Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed! AK Mahlein, MT Kuska, S Thomas, M Wahabzada, J Behmann, ... Current opinion in plant biology 50, 156-162, 2019 | 108 | 2019 |
| Descriptive matrix factorization for sustainability adopting the principle of opposites C Thurau, K Kersting, M Wahabzada, C Bauckhage Data Mining and Knowledge Discovery 24, 325-354, 2012 | 91 | 2012 |
| Convex non-negative matrix factorization for massive datasets C Thurau, K Kersting, M Wahabzada, C Bauckhage Knowledge and information systems 29, 457-478, 2011 | 64 | 2011 |
| Observation of plant–pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements S Thomas, M Wahabzada, MT Kuska, U Rascher, AK Mahlein Functional Plant Biology 44 (1), 23-34, 2016 | 63 | 2016 |
| Hyperspectral imaging reveals the effect of sugar beet quantitative trait loci on Cercospora leaf spot resistance M Leucker, M Wahabzada, K Kersting, M Peter, W Beyer, U Steiner, ... Functional Plant Biology 44 (1), 1-9, 2016 | 56 | 2016 |
| Monitoring wound healing in a 3D wound model by hyperspectral imaging and efficient clustering M Wahabzada, M Besser, M Khosravani, MT Kuska, K Kersting, ... PloS one 12 (12), e0186425, 2017 | 47 | 2017 |
| Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. C Rümer, M Wahabzada, A Ballvora, F Pinto, M Rossini, C Panigada, ... Functional Plant Biology 39 (11), 2012 | 43 | 2012 |
| Spectral Patterns Reveal Early Resistance Reactions of Barley Against Blumeria graminis f. sp. hordei MT Kuska, A Brugger, S Thomas, M Wahabzada, K Kersting, EC Oerke, ... Phytopathology 107 (11), 1388-1398, 2017 | 40 | 2017 |
| Plant disease detection by hyperspectral imaging: from the lab to the field AK Mahlein, MT Kuska, S Thomas, D Bohnenkamp, E Alisaac, J Behmann, ... Advances in Animal Biosciences 8 (2), 238-243, 2017 | 39 | 2017 |
| Hierarchical convex NMF for clustering massive data K Kersting, M Wahabzada, C Thurau, C Bauckhage Proceedings of 2nd Asian Conference on Machine Learning, 253-268, 2010 | 38 | 2010 |
| Non-negative factor analysis supporting the interpretation of elemental distribution images acquired by XRF M Alfeld, M Wahabzada, C Bauckhage, K Kersting, G Wellenreuther, ... Journal of physics: conference series 499 (1), 012013, 2014 | 35 | 2014 |
| Pre-symptomatic prediction of plant drought stress using dirichlet-aggregation regression on hyperspectral images K Kersting, Z Xu, M Wahabzada, C Bauckhage, C Thurau, C Roemer, ... Proceedings of the AAAI Conference on Artificial Intelligence 26 (1), 302-308, 2012 | 35 | 2012 |
| AI for crop production–Where can large language models (LLMs) provide substantial value? MT Kuska, M Wahabzada, S Paulus Computers and electronics in agriculture 221, 108924, 2024 | 21* | 2024 |
| Non-negative matrix factorization for the near real-time interpretation of absorption effects in elemental distribution images acquired by X-ray fluorescence imaging M Alfeld, M Wahabzada, C Bauckhage, K Kersting, G Wellenreuther, ... Synchrotron Radiation 23 (2), 579-589, 2016 | 21 | 2016 |
Kristian KerstingProfessor of AI & ML, Technical University of Darmstadt, Co-Director hessian.ai, DFKI, CAIRNE, ELLISPatvirtintas el. paštas cs.tu-darmstadt.de