Anne-Katrin Mahlein
Plant Science · 2010–2020
A researcher in the Science corpus: 7 papers; trusts 15 researchers and is trusted by 7 researchers here, as inferred from the citation graph.
Subfields
- Plant Science — 79 citations into it; #1,653 of 8,232 by trust here, #4,762 by citations
- Ecology — 55 citations into it; #1,956 of 10,717 by trust here, #2,736 by citations
- Analytical Chemistry — 35 citations into it; #269 of 1,254 by trust here, #131 by citations
- Computer Vision and Pattern Recognition — 22 citations into it
- Genetics — 13 citations into it
- Cell Biology — 10 citations into it
Whom Anne-Katrin Mahlein trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- Anatoly A. Gitelson
- Fabio Fiorani
- Mark A. Tester
- Achim Walter
- Jose Luis Araus
- Dimitrios Moshou
- A. Knapp
- Noah Fahlgren
- George Alan Blackburn
- R. Hückelhoven
Who trusts Anne-Katrin Mahlein overall
- J. Lamichhane
- Rajendra P. Sishodia
- Ram Lakhan Ray
- Amy Lowe
- Nikola Šušić
- Sudhir Kumar Singh
- K. Golhani
- A. Berton
- Srdjan Sladojevic
- Gowtham Atluri
Papers from Anne-Katrin Mahlein's perspective
Ordered by how strongly the researchers Anne-Katrin Mahlein trusts endorse each paper. Same corpus, a different order for every researcher.
- Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
- ImageNet: A large-scale hierarchical image database
- Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
- Microsoft COCO: Common Objects in Context
- Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. (American-Eurasian journal of botany)
- Visualizing Data using t-SNE
- ImageNet classification with deep convolutional neural networks
- Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
- Towards universal broad leaf chlorophyll indices using PROSPECT simulated database and hyperspectral reflectance measurements
- Gradient-based learning applied to document recognition
Their papers
- Making deep neural networks right for the right scientific reasons by interacting with their explanations (2020) — 250 citations
- Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed! (2019) — 93 citations
- Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective (2018) — 298 citations
- Plant Disease Detection by Imaging Sensors - Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. (2016) — 1,011 citations
- Fusion of sensor data for the detection and differentiation of plant diseases in cucumber (2014) — 108 citations
- Development of spectral indices for detecting and identifying plant diseases (2013) — 595 citations
- Spectral signatures of sugar beet leaves for the detection and differentiation of diseases (2010) — 255 citations