| Plant disease detection by imaging sensors–parallels and specific demands for precision agriculture and plant phenotyping AK Mahlein Plant disease 100 (2), 241-251, 2016 | 1168 | 2016 |
| Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance T Rumpf, AK Mahlein, U Steiner, EC Oerke, HW Dehne, L Plümer Computers and electronics in agriculture 74 (1), 91-99, 2010 | 1071 | 2010 |
| Recent advances in sensing plant diseases for precision crop protection AK Mahlein, EC Oerke, U Steiner, HW Dehne European Journal of Plant Pathology 133, 197-209, 2012 | 663 | 2012 |
| Development of spectral indices for detecting and identifying plant diseases AK Mahlein, T Rumpf, P Welke, HW Dehne, L Plümer, U Steiner, ... Remote Sensing of Environment 128, 21-30, 2013 | 657 | 2013 |
| A review of advanced machine learning methods for the detection of biotic stress in precision crop protection J Behmann, AK Mahlein, T Rumpf, C Römer, L Plümer Precision agriculture 16, 239-260, 2015 | 401 | 2015 |
| Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases AK Mahlein, U Steiner, C Hillnhütter, HW Dehne, EC Oerke Plant methods 8, 1-13, 2012 | 390 | 2012 |
| 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 | 310 | 2018 |
| Low-cost 3D systems: suitable tools for plant phenotyping S Paulus, J Behmann, AK Mahlein, L Plümer, H Kuhlmann Sensors 14 (2), 3001-3018, 2014 | 305 | 2014 |
| Hyperspectral sensors and imaging technologies in phytopathology: state of the art AK Mahlein, MT Kuska, J Behmann, G Polder, A Walter Annual review of phytopathology 56 (1), 535-558, 2018 | 299 | 2018 |
| Spectral signatures of sugar beet leaves for the detection and differentiation of diseases AK Mahlein, U Steiner, HW Dehne, EC Oerke Precision agriculture 11, 413-431, 2010 | 282 | 2010 |
| Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping S Paulus, J Dupuis, AK Mahlein, H Kuhlmann BMC bioinformatics 14, 1-12, 2013 | 230 | 2013 |
| Making deep neural networks right for the right scientific reasons by interacting with their explanations P Schramowski, W Stammer, S Teso, A Brugger, F Herbert, X Shao, ... Nature Machine Intelligence 2 (8), 476-486, 2020 | 229 | 2020 |
| Specim IQ: evaluation of a new, miniaturized handheld hyperspectral camera and its application for plant phenotyping and disease detection J Behmann, K Acebron, D Emin, S Bennertz, S Matsubara, S Thomas, ... Sensors 18 (2), 441, 2018 | 205 | 2018 |
| From visual estimates to fully automated sensor-based measurements of plant disease severity: status and challenges for improving accuracy CH Bock, JGA Barbedo, EM Del Ponte, D Bohnenkamp, AK Mahlein Phytopathology Research 2, 1-30, 2020 | 203 | 2020 |
| 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 | 201 | 2015 |
| Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields C Hillnhütter, AK Mahlein, RA Sikora, EC Oerke Field Crops Research 122 (1), 70-77, 2011 | 158 | 2011 |
| 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 | 155 | 2016 |
| Fusion of sensor data for the detection and differentiation of plant diseases in cucumber CA Berdugo, R Zito, S Paulus, AK Mahlein Plant pathology 63 (6), 1344-1356, 2014 | 139 | 2014 |
| 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 | 137 | 2015 |
| Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Fusarium Head Blight of Wheat on Spikelet Scale AK Mahlein, E Alisaac, A Al Masri, J Behmann, HW Dehne, EC Oerke Sensors 19 (10), 2281, 2019 | 118 | 2019 |
Dehne, H.-W.Prof. of Plant Pathology, University of BonnVerified email at uni-bonn.de
