Anne-Katrin Mahlein
Anne-Katrin Mahlein
Institute of Sugar Beet Research, Göttingen
Verified email at - Homepage
Cited by
Cited by
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
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
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
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
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
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
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
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
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, 535-558, 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
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
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
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
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
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
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
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
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
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
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
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