| 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 | 306 | 2014 |
| High-precision laser scanning system for capturing 3D plant architecture and analysing growth of cereal plants S Paulus, H Schumann, H Kuhlmann, J Léon Biosystems Engineering 121, 1-11, 2014 | 241 | 2014 |
| 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 |
| Measuring crops in 3D: using geometry for plant phenotyping S Paulus Plant methods 15 (1), 103, 2019 | 177 | 2019 |
| Accuracy analysis of a multi-view stereo approach for phenotyping of tomato plants at the organ level JC Rose, S Paulus, H Kuhlmann Sensors 15 (5), 9651-9665, 2015 | 148 | 2015 |
| 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 |
| Automated analysis of barley organs using 3D laser scanning: An approach for high throughput phenotyping S Paulus, J Dupuis, S Riedel, H Kuhlmann Sensors 14 (7), 12670-12686, 2014 | 127 | 2014 |
| Generation and application of hyperspectral 3D plant models: methods and challenges J Behmann, AK Mahlein, S Paulus, J Dupuis, H Kuhlmann, EC Oerke, ... Machine Vision and Applications 27, 611-624, 2016 | 109 | 2016 |
| 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 | 105 | 2015 |
| Calibration of hyperspectral close-range pushbroom cameras for plant phenotyping J Behmann, AK Mahlein, S Paulus, H Kuhlmann, EC Oerke, L Plümer ISPRS Journal of Photogrammetry and Remote Sensing 106, 172-182, 2015 | 84 | 2015 |
| Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis D Schunck, F Magistri, RA Rosu, A Cornelißen, N Chebrolu, S Paulus, ... Plos one 16 (8), e0256340, 2021 | 67 | 2021 |
| Hyperspectral imaging of symptoms induced by Rhizoctonia solani in sugar beet: Comparison of input data and different machine learning algorithms A Barreto, S Paulus, M Varrelmann, AK Mahlein Journal of Plant Diseases and Protection 127 (4), 441-451, 2020 | 59 | 2020 |
| Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale S Paulus, AK Mahlein GigaScience 9 (8), giaa090, 2020 | 54 | 2020 |
| Extending hyperspectral imaging for plant phenotyping to the UV-range A Brugger, J Behmann, S Paulus, HG Luigs, MT Kuska, P Schramowski, ... Remote Sensing 11 (12), 1401, 2019 | 45 | 2019 |
| Limits of active laser triangulation as an instrument for high precision plant imaging S Paulus, T Eichert, HE Goldbach, H Kuhlmann Sensors 14 (2), 2489-2509, 2014 | 41 | 2014 |
| Automatic UAV-based counting of seedlings in sugar-beet field and extension to maize and strawberry A Barreto, P Lottes, FRI Yamati, S Baumgarten, NA Wolf, C Stachniss, ... Computers and Electronics in Agriculture 191, 106493, 2021 | 37 | 2021 |
| Spatial referencing of hyperspectral images for tracing of plant disease symptoms J Behmann, D Bohnenkamp, S Paulus, AK Mahlein Journal of Imaging 4 (12), 143, 2018 | 28 | 2018 |
| Digital plant pathology: A foundation and guide to modern agriculture MT Kuska, RHJ Heim, I Geedicke, KM Gold, A Brugger, S Paulus Journal of Plant Diseases and Protection 129 (3), 457-468, 2022 | 27 | 2022 |
| Prediction of the kiwifruit decline syndrome in diseased orchards by remote sensing F Savian, M Martini, P Ermacora, S Paulus, AK Mahlein Remote Sensing 12 (14), 2194, 2020 | 22 | 2020 |
| A multi-resolution approach for an automated fusion of different low-cost 3D sensors J Dupuis, S Paulus, J Behmann, L Plümer, H Kuhlmann Sensors 14 (4), 7563-7579, 2014 | 21 | 2014 |
Anne-Katrin MahleinInstitute of Sugar Beet Research, GöttingenBestätigte E-Mail-Adresse bei ifz-goettingen.de
