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Uroš Žibrat

Observed names: U. Žibrat, Uros Zibrat, Uroš ŽIBRAT, Zibrat Uros, Zibrat, Uros, Žibrat, Uroš
ORCID: 0000-0002-0091-5441
Observed ORCID: 0000-0002-0091-5441
OpenAlex ID: A5031689764
Discrimination between abiotic and biotic drought stress in tomatoes using hyperspectral imaging
2018 · Nik Susič, Uroš Žibrat, et al. · Sensors and Actuators B Chemical · 129
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Hyperspectral remote sensing of grapevine drought stress
2019 · M. Zovko, U. Žibrat, et al. · Precision Agriculture · 70
Single seed near-infrared hyperspectral imaging in determining tomato (Solanum lycopersicum L.) seed quality in association with multivariate data analysis
2016 · Santosh Shrestha, Matej Knapič, et al. · Sensors and Actuators B Chemical · 70
From Genome to Field—Observation of the Multimodal Nematicidal and Plant Growth-Promoting Effects of Bacillus firmus I-1582 on Tomatoes Using Hyperspectral Remote Sensing
2020 · Nik Susič, Uroš Žibrat, et al. · Plants · 37
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Detection of Root-Knot Nematode Meloidogyne luci Infestation of Potato Tubers Using Hyperspectral Remote Sensing and Real-Time PCR Molecular Methods
2021 · Uroš Žibrat, Barbara Gerič Stare, et al. · Remote Sensing · 35
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Comparison of Selected Dimensionality Reduction Methods for Detection of Root-Knot Nematode Infestations in Potato Tubers Using Hyperspectral Imaging
2022 · Janez Lapajne, Matej Knapič, et al. · Sensors · 33
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Effects of Exposing Two Non-Target Crustacean Species, Asellus aquaticus L., and Gammarus fossarum Koch., to Atrazine and Imidacloprid
2009 · Simon Lukančič, Uroš Žibrat, et al. · Bulletin of Environmental Contamination and Toxicology · 29
Pipeline for imaging, extraction, pre-processing, and processing of time-series hyperspectral data for discriminating drought stress origin in tomatoes
2019 · Uroš Žibrat, Nik Susič, et al. · MethodsX · 27
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Field-scale UAV-based multispectral phenomics: Leveraging machine learning, explainable AI, and hybrid feature engineering for enhancements in potato phenotyping
2024 · Janez Lapajne, Andrej Vončina, et al. · Computers and Electronics in Agriculture · 23
A new method for early assessment of effects of exposing two non-target crustacean species, Asellus aquaticus and Gammarus fossarum, to pesticides, a laboratory study
2010 · Simon Lukančič, Uroš Žibrat, et al. · Toxicology and Industrial Health · 18
The role of river flow dynamics and food availability in structuring hyporheic microcrustacean assemblages: a reach scale study
2012 · Nataša Mori, Tatjana Simčič, et al. · Fundamental and Applied Limnology / Archiv für Hydrobiologie · 15
Enhancing Water-Deficient Potato Plant Identification: Assessing Realistic Performance of Attention-Based Deep Neural Networks and Hyperspectral Imaging for Agricultural Applications
2024 · Janez Lapajne, Ana Vojnović, et al. · Plants · 14
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Noninvasive detection of plant parasitic nematodes using hyperspectral and other remote sensing systems
2020 · Uroš Žibrat, Saša Širca, et al. · Elsevier eBooks · 13
Differences between groundwater fauna in shallow and in deep intergranular aquifers as an indication of different characteristics of habitats and hydraulic connections
2016 · Anton Brancelj, Uroš Žibrat, et al. · Journal of Limnology · 13
Early Detection of Wireworm (Coleoptera: Elateridae) Infestation and Drought Stress in Maize Using Hyperspectral Imaging
2023 · Eva Praprotnik, Andrej Vončina, et al. · Agronomy · 11
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Consecutive earthquakes temporarily restructured the zooplankton community in an Alpine Lake
2012 · Anton Brancelj, Uroš Žibrat, et al. · Annales de Limnologie - International Journal of Limnology · 8
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Differential diagnosis of Triaenophorus crassus and T. nodulosus experimental infection in Cyclops abyssorum praealpinus (Copepoda) from the Alpine Lake Grundlsee (Austria) using PCR–RFLP
2011 · Belgees Boufana, Uroš Žibrat, et al. · Parasitology Research · 8
Hyperspectral imagery as a supporting tool in precision irrigation of karst landscapes
2017 · M. Zovko, U. Žibrat, et al. · Advances in Animal Biosciences · 7
Plant pests and disease detection using optical sensors / Daljinsko zaznavanje rastlinskih bolezni in škodljivcev
2019 · Uroš Žibrat, Matej Knapič, et al. · Folia biologica et geologica · 6
Detecting nematodes in potato plants an explainable machine learning approach for detection of potato cyst nematode infections using hyperspectral imaging
2025 · Janez Lapajne, Nik Susič, et al. · Plant Phenomics · 3
Detection of grapevine yellows using multispectral imaging
2023 · Uroš Žibrat, Matej Knapič · Elsevier eBooks · 3
Use of Remote sensing technology to assess grapevine quality
2019 · Uros Zibrat, Matej Knapic, et al. · IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) · 3
TranslocatedEsox luciusL. (PISCES) trigger aTriaenophorus crassusForel (CESTODA) epidemic in a population ofSalvelinus umbla(L.) (PISCES)
2014 · Gishild Schaufler, Claudia Stögner, et al. · International Review of Hydrobiology · 3
NemDetect: Early detection of quarantine nematodes in potatoes using remote sensing
2023 · Uroš Žibrat, Nicole Viaene, et al. · EFSA Supporting Publications · 1
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Potato plant disease classification by using deep learning and sparse sensing
2023 · J. Lapajne, A. Vončina, et al. · Wageningen Academic Publishers eBooks · 1
Works count: 40
Citation count: 583
H-index: 13
I10-index: 15
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