Kang Yu is a professor of Precison Agriculture at the Technical University of Munich. His research interests include crop field phenotyping, precision crop nutrient mangement, and vegetation remote sensing. He leads the Precision Agriculture group, which develops sensing and computational methods to phenotype plant traits and to monitor crop stress, nutrition response, and yield across scales using UAV and satellite remote sensing data.
Interests
- Vegetation remote sensing
- Crop field phenotyping
- Precision crop nutrient management
Education
PhD in Physical Geography, 2014
University of Cologne
MSc in Plant Nutrition, 2009
China Agricultural University
BSc in Agricultural Resource and Environmental Sciences, 2007
China Agricultural University
Latest
- AI Meets Drones Summer Hackathon 2025
- Global Canopy Chlorophyll Content (CCC) Datasets (2016–2024)
- Global retrieval of canopy chlorophyll content from Sentinel-3 OLCI TOA data using a two-step upscaling method integrating physical and machine learning models
- [Code & Data] UAV weed detection
- Satellite farming boosting Nitrogen Use Efficiency from Space
- Biodiversity Monitoring in Urban Community Gardens Using Proximal Sensing and Drone Remote Sensing
- Characterization of N variations in different organs of winter wheat and mapping NUE using low altitude UAV-based remote sensing
- Drone multispectral imaging captures the effects of soil mineral nitrogen on canopy structure and nitrogen use efficiency in wheat
- Drone RGB Imagery Color and Texture Information Have Varied Importance in Predicting Potato Aboveground Biomass in Different Growth Stages
- Improving potato leaf chlorophyll content prediction using a machine learning model with a hybrid dataset
- Improving the performance of plant nitrogen assessment in drip-irrigated potatoes using optimized spectral indices-based machine learning
- Improving the transferability of potato nitrogen concentration estimation models based on hybrid feature selection and Gaussian process regression
- Improving winter wheat plant nitrogen concentration prediction by combining proximal hyperspectral sensing and weather information with machine learning
- NTRI: A novel spectral index for developing a precise nitrogen diagnosis model across pre- and post-anthesis stages of maize plants
- Planting contexts affect urban tree species classification using airborne hyperspectral and LiDAR imagery
- Self-attention and frequency-augmentation for unsupervised domain adaptation in satellite image-based time series classification
- Weed instance segmentation from UAV Orthomosaic Images based on Deep Learning
- [Dataset] UAV multispectral vegetation indices and texture features for predicting plant organ-specific Nitrogen content
- PRECISION: Advanced Machine Learning for Drone-Based Precision Agriculture
- Special Issue: Advanced Technologies for Precision Nutrient Management
- Application of unmanned aerial vehicle optical remote sensing in crop nitrogen diagnosis: A systematic literature review
- Approaching holistic crop type mapping in Europe through winter vegetation classification and the Hierarchical Crop and Agriculture Taxonomy
- Drone Multispectral Imaging Captures the Effects of Soil Nmin on Canopy Structure and Nitrogen Use Efficiency in wheat
- Exploring the optimal wavelet function and wavelet feature for estimating maize leaf chlorophyll content
- Maize plant detection using UAV-based RGB imaging and YOLOv5
- Mapping Plant Nitrogen Concentration and Aboveground Biomass of Potato Crops from Sentinel-2 Data Using Ensemble Learning Models
- Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency
- Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering
- Substrate pH mediates growth promotion and resilience to water stress of Tilia tomentosa seedlings after Ectomycorrhizal inoculation
- UAV-based assessment of the impact of aboveground biomass and photosynthesis parameters on winter wheat yield
- Weed Instance Segmentation from UAV ortho-mosaic Images based on Deep Learning
- Wheat varieties show consistent differences in root colonization by mycorrhiza across a European pedoclimatic gradient
- AmAIzed: AI-Assisted Multi-Tasking Robot for Sustainable Agriculture
- LiveSen-MAP: Real-Time Nutrient Sensing for Precision Agriculture
- Special Issue: Remote Sensing for Crop Nutrients and Related Traits
- Detecting Cassava Plants under Different Field Conditions Using UAV-Based RGB Images and Deep Learning Models
- Detection of combined frost and drought stress in wheat using hyperspectral and chlorophyll fluorescence imaging
- Machine learning models fed with optimized spectral indices to advance crop nitrogen monitoring
- Meta-analysis assessing potential of drone remote sensing in estimating plant traits related to nitrogen use efficiency
- Multi-temporal UAV Imaging-Based Mapping of Chlorophyll Content in Potato Crop
- OPTRAM-ET: A novel approach to remote sensing of actual evapotranspiration applied to Sentinel-2 and Landsat-8 observations
- Special Issue: Intelligent Agricultural Applications with Sensing and Vision System
- Cassava Detection from UAV Images Using YOLOv5 Object Detection Model: Towards Weed Control in a Cassava Farm
- Characterization of N distribution in different organs of winter wheat using UAV-based remote sensing
- Estimation of Above-Ground Biomass of Winter Wheat Based on Consumer-Grade Multi-Spectral UAV
- Foliar optical traits capture physiological and phenological leaf plasticity in Tilia×euchlora in the urban environment
- Hyperspectral Vegetation Indices to Assess Water and Nitrogen Status of Sweet Maize Crop
- Machine Learning on the estimation of Leaf Area Index
- Multi-angular Multispectral Sensing of Barley Awn Coloring
- UAV-Based Multi-Temporal Thermal Imaging to Evaluate Wheat Drought Resistance in Different Deficit Irrigation Regimes
- RAISEAgriculture: EXIST Start-up Project
- Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices
- Hyperspectral indices optimization algorithms for estimating canopy nitrogen concentration in potato (Solanum tuberosum L.)
- Predicting micronutrients of wheat using hyperspectral imaging
- Sorghum Grains Grading for Food, Feed, and Fuel Using NIR Spectroscopy
- Spectroscopic estimation of N concentration in wheat organs for assessing N remobilization under different irrigation regimes
- Spike growth affects spike fertility through the number of florets with green anthers before floret abortion in wheat
- Spectral Vegetation Indices to Track Senescence Dynamics in Diverse Wheat Germplasm
- Urban Tree Health Classification Across Tree Species by Combining Airborne Laser Scanning and Imaging Spectroscopy