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Achim Walter

Showing results (61-70 of 142) with videos related to

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Plant Phenomics (Washington, D.C.)|June 26, 2023
Combining High-Resolution Imaging, Deep Learning, and Dynamic Modeling to Separate Disease and Senescence in Wheat CanopiesJonas Anderegg, Radek Zenkl, Achim Walter, et al.
BMC Genetics|October 9, 2015
Evidence and consequences of self-fertilisation in the predominantly outbreeding forage legume Onobrychis viciifoliaKatharina Kempf, Christoph Grieder, Achim Walter, et al.
Frontiers in Plant Science|February 13, 2020
Spectral Vegetation Indices to Track Senescence Dynamics in Diverse Wheat GermplasmJonas Anderegg, Kang Yu, Helge Aasen, et al.
Remote Sensing|September 9, 2022
Crop Nitrogen Retrieval Methods for Simulated Sentinel-2 Data Using In-Field Spectrometer DataGregor Perich, Helge Aasen, Jochem Verrelst, et al.
Plant Methods|February 19, 2019
A spatio temporal spectral framework for plant stress phenotypingRaghav Khanna, Lukas Schmid, Achim Walter, et al.
ACS Combinatorial Science|February 28, 2019
Electrochemical Screening of Tungsten Trioxide-Nickel Oxide Thin Film Combinatorial Library at Low Nickel ConcentrationsJun-Seob Lee, Cezarina Cela Mardare, Andrei Ionut Mardare, et al.
Plant Methods|December 20, 2024
Temporal resolution trumps spectral resolution in UAV-based monitoring of cereal senescence dynamicsFlavian Tschurr, Lukas Roth, Nicola Storni, et al.
Journal of Experimental Botany|June 27, 2006
Anticipated yield loss in field-grown soybean under elevated ozone can be avoided at the expense of leaf growth during early reproductive growth stages in favourable environmental conditionsMaja M Christ, Elizabeth A Ainsworth, Randall Nelson, et al.
Plant Methods|February 3, 2016
Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditionsMichael Friedli, Norbert Kirchgessner, Christoph Grieder, et al.
Ecology and Evolution|January 14, 2026
Automated Detection and Monitoring of Ground-Nesting Bee Nests Using Drone Imagery and Deep LearningPhilippe Tschanz, Thomas Renggli, Jonas Winizki, et al.
Pageof 15

Showing results (61-70 of 142) with videos related to

Sort By:
Pageof 15
Plant Phenomics (Washington, D.C.)|June 26, 2023
Combining High-Resolution Imaging, Deep Learning, and Dynamic Modeling to Separate Disease and Senescence in Wheat CanopiesJonas Anderegg, Radek Zenkl, Achim Walter, et al.
BMC Genetics|October 9, 2015
Evidence and consequences of self-fertilisation in the predominantly outbreeding forage legume Onobrychis viciifoliaKatharina Kempf, Christoph Grieder, Achim Walter, et al.
Frontiers in Plant Science|February 13, 2020
Spectral Vegetation Indices to Track Senescence Dynamics in Diverse Wheat GermplasmJonas Anderegg, Kang Yu, Helge Aasen, et al.
Remote Sensing|September 9, 2022
Crop Nitrogen Retrieval Methods for Simulated Sentinel-2 Data Using In-Field Spectrometer DataGregor Perich, Helge Aasen, Jochem Verrelst, et al.
Plant Methods|February 19, 2019
A spatio temporal spectral framework for plant stress phenotypingRaghav Khanna, Lukas Schmid, Achim Walter, et al.
ACS Combinatorial Science|February 28, 2019
Electrochemical Screening of Tungsten Trioxide-Nickel Oxide Thin Film Combinatorial Library at Low Nickel ConcentrationsJun-Seob Lee, Cezarina Cela Mardare, Andrei Ionut Mardare, et al.
Plant Methods|December 20, 2024
Temporal resolution trumps spectral resolution in UAV-based monitoring of cereal senescence dynamicsFlavian Tschurr, Lukas Roth, Nicola Storni, et al.
Journal of Experimental Botany|June 27, 2006
Anticipated yield loss in field-grown soybean under elevated ozone can be avoided at the expense of leaf growth during early reproductive growth stages in favourable environmental conditionsMaja M Christ, Elizabeth A Ainsworth, Randall Nelson, et al.
Plant Methods|February 3, 2016
Terrestrial 3D laser scanning to track the increase in canopy height of both monocot and dicot crop species under field conditionsMichael Friedli, Norbert Kirchgessner, Christoph Grieder, et al.
Ecology and Evolution|January 14, 2026
Automated Detection and Monitoring of Ground-Nesting Bee Nests Using Drone Imagery and Deep LearningPhilippe Tschanz, Thomas Renggli, Jonas Winizki, et al.
Pageof 15