Jan Behmann
Plant Science · 2012–2019
A researcher in the Science corpus: 4 papers; trusts 1 researcher and is trusted by 6 researchers here, as inferred from the citation graph.
Subfields
- Plant Science — 47 citations into it; #3,206 of 8,232 by trust here, #5,350 by citations
Whom Jan Behmann trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
Who trusts Jan Behmann overall
Papers from Jan Behmann's perspective
Ordered by how strongly the researchers Jan Behmann trusts endorse each paper. Same corpus, a different order for every researcher.
- Pattern Recognition and Machine Learning
- A global geometric framework for nonlinear dimensionality reduction.
- Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics
- Field high-throughput phenotyping: the new crop breeding frontier. (Trends in Plant Science)
- Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. (Current opinion in plant biology)
- Three‐band model for noninvasive estimation of chlorophyll, carotenoids, and anthocyanin contents in higher plant leaves
- Future scenarios for plant phenotyping. (Annual Review of Plant Biology)
- Machine Learning for High-Throughput Stress Phenotyping in Plants. (Trends in Plant Science)
- Plant phenotyping: from bean weighing to image analysis (Plant Methods)
- Plant pathogens and integrated defence responses to infection (Nature)
Their papers
- 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
- Detection of early plant stress responses in hyperspectral images (2014) — 300 citations
- Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. (2012) — 150 citations