Publications

Global retrieval of canopy chlorophyll content from Sentinel-3 OLCI TOA data using a two-step upscaling method integrating physical and machine learning models
Canopy chlorophyll content per ground area (CCC, g·m−2) is tightly related to vegetation photosynthesis and is a promising indicator of …
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Weed instance segmentation from UAV Orthomosaic Images based on Deep Learning
Weeds significantly impact agricultural production, and traditional weed control methods often harm soil health and environment. This …
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Self-attention and frequency-augmentation for unsupervised domain adaptation in satellite image-based time series classification
With the increasing availability of Earth observation data in recent years, the development of deep learning algorithms for the …
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Planting contexts affect urban tree species classification using airborne hyperspectral and LiDAR imagery
Different urban planting contexts, such as streets and parks, can lead to significant intraspecific biochemical and structural …
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Optimising Root and Grain Yield Through Variety Selection in Winter Wheat Across a European Climate Gradient
Ensuring food security through sustainable practices while reducing greenhouse gas emissions are key challenges in modern agriculture. …
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NTRI: A novel spectral index for developing a precise nitrogen diagnosis model across pre- and post-anthesis stages of maize plants
Context Accurate and real-time diagnosis of crop nitrogen (N) status is essential for effective precision N management. Integrating the …
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Improving winter wheat plant nitrogen concentration prediction by combining proximal hyperspectral sensing and weather information with machine learning
Timely and accurate prediction of nitrogen (N) status in winter wheat is crucial for guiding precision N management. This study aimed …
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Improving the transferability of potato nitrogen concentration estimation models based on hybrid feature selection and Gaussian process regression
Feature selection methods are widely used to improve the performance of plant nitrogen concentration (PNC) estimation models. However, …
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Improving the performance of plant nitrogen assessment in drip-irrigated potatoes using optimized spectral indices-based machine learning
Timely and accurate monitoring of plant nitrogen concentration (PNC) is vital for optimizing field N management. Hyperspectral indices …
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Improving potato leaf chlorophyll content prediction using a machine learning model with a hybrid dataset

Haibo Yang, Yuncai Hu, Hang Yin, Qingyu Jin, Fei Li, & Kang Yu (2025). Improving potato leaf chlorophyll content prediction using a machine learning model with a hybrid dataset. International Journal of Remote Sensing: 1–25.

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Drone RGB Imagery Color and Texture Information Have Varied Importance in Predicting Potato Aboveground Biomass in Different Growth Stages
Accurately monitoring the crop aboveground biomass (AGB) is critical for yield forecasting. Despite the advances in high-resolution RGB …
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Drone multispectral imaging captures the effects of soil mineral nitrogen on canopy structure and nitrogen use efficiency in wheat
Drone remote sensing offers a powerful tool for monitoring vegetation and agricultural systems. However, its effectiveness in assessing …
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Characterization of N variations in different organs of winter wheat and mapping NUE using low altitude UAV-based remote sensing
Although unmanned aerial vehicle (UAV) remote sensing is widely used for high-throughput crop monitoring, few attempts have been made …
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Biodiversity Monitoring in Urban Community Gardens Using Proximal Sensing and Drone Remote Sensing
In urban community gardens, artificially managed ground cover types, including vegetative components and non-vegetative ground …
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Wheat varieties show consistent differences in root colonization by mycorrhiza across a European pedoclimatic gradient
Arbuscular mycorrhizal (AM) fungi form mutualistic relationships with the majority of land plants and are an important part of the soil …
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Weed Instance Segmentation from UAV ortho-mosaic Images based on Deep Learning
Weeds significantly impact agricultural production, and traditional weed control methods often harm soil health and the environment. …
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UAV-based assessment of the impact of aboveground biomass and photosynthesis parameters on winter wheat yield

Jingcheng Zhang, Vladyslav Pitsyk, & Kang Yu (2024). UAV-based assessment of the impact of aboveground biomass and photosynthesis parameters on winter wheat yield. {UAV}-based assessment of the impact of aboveground biomass and photosynthesis parameters on winter wheat yield: 326–333.

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Substrate pH mediates growth promotion and resilience to water stress of Tilia tomentosa seedlings after Ectomycorrhizal inoculation
Urban trees in urban environments are commonly exposed to stresses, contributing to their health decline;
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Multi temporal multispectral UAV remote sensing allows for yield assessment across European wheat varieties already before flowering
High throughput field phenotyping techniques employing multispectral cameras allow extracting a variety of variables and features to …
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Meta-Analysis Assessing Potential of Drone Remote Sensing in Estimating Plant Traits Related to Nitrogen Use Efficiency
Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health …
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Mapping Plant Nitrogen Concentration and Aboveground Biomass of Potato Crops from Sentinel-2 Data Using Ensemble Learning Models
Excessive nitrogen (N) fertilization poses environmental risks at regional and global levels. Satellite remote sensing provides a novel …
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Maize plant detection using UAV-based RGB imaging and YOLOv5
In recent years, computer vision (CV) has made enormous progress and is providing great possibilities in analyzing images for object …
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Exploring the optimal wavelet function and wavelet feature for estimating maize leaf chlorophyll content
Ensuring global food security depends heavily on attaining and sustaining high maize yields. However, effective N fertilisation …
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Drone Multispectral Imaging Captures the Effects of Soil Nmin on Canopy Structure and Nitrogen Use Efficiency in wheat
Drone remote sensing has been increasingly demonstrated its unique advantage of monitoring vegetation and agricultural systems, yet the …
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Approaching holistic crop type mapping in Europe through winter vegetation classification and the Hierarchical Crop and Agriculture Taxonomy
The process of crop type mapping generates land use maps, which serve as critical tools for efficient evaluation of production factors …
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Application of unmanned aerial vehicle optical remote sensing in crop nitrogen diagnosis: A systematic literature review
Nitrogen (N) is an essential nutrient that profoundly impacts crop growth, yield, and overall quality. Misapplication of N fertilizers …
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UAS-based multispectral imaging and feature selection for yield prediction
Yield prediction of wheat using unmanned aerial systems requires the extraction of a few meaningful features from the images. To …
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Tracking wheat senescence based on UAV multispectral imaging
Crop senescence phenotyping usually relies on visual assessment, which, however, is time-consuming and cannot reflect the dynamic …
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Stay-green monitoring for maize drought tolerance under field environments using leaf hyperspectral data

H. El Sharawy, Y.c. Lin, C.-C. Schön, & K. Yu (2023). Stay-green monitoring for maize drought tolerance under field environments using leaf hyperspectral data. Precision agriculture ?23: 887–893.

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OPTRAM-ET: A novel approach to remote sensing of actual evapotranspiration applied to Sentinel-2 and Landsat-8 observations
Satellite remote sensing technology provides a promising means for near real-time monitoring of crop water status and requirements in …
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Multi-temporal UAV Imaging-Based Mapping of Chlorophyll Content in Potato Crop
Spectral indices based on unmanned aerial vehicle (UAV) multispectral images combined with machine learning algorithms can more …
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Meta-analysis assessing potential of drone remote sensing in estimating plant traits related to nitrogen use efficiency
Unmanned Aerial Systems (UASs) are increasingly vital in precision agriculture, offering detailed, real-time insights into plant health …
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Machine learning models fed with optimized spectral indices to advance crop nitrogen monitoring
Context Remotely estimating plant nitrogen concentration (PNC) at the vegetative growth stage plays a crucial role in the precision N …
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Improving the generalization ability of random forest for potato chlorophyll estimation through integrating experimental and simulated canopy spectral data
Machine learning (ML) combined with hyperspectral reflectance is a hot research topic in the field of vegetation traits monitoring. …
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Detection of combined frost and drought stress in wheat using hyperspectral and chlorophyll fluorescence imaging
Immediate detection and prediction tools for interactive stresses are essential to avoid yield losses. This study investigated the …
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Detecting Cassava Plants under Different Field Conditions Using UAV-Based RGB Images and Deep Learning Models
A significant number of object detection models have been researched for use in plant detection. However, deployment and evaluation of …
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Cassava detection under real field conditions using YOLOv5
Plant detection is a critical step in many farm-management tasks. Many object detection models lack robustness for plant detection in …
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An optical trapezoid model for actual evapotranspiration and winter wheat yield estimation
In this study, a satellite-based actual evapotranspiration (ETa) estimation model, OPTRAM-ET, based on the optical trapezoid model …
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Using multispectral drone data to monitor maize’s response to various irrigation modes
[Objectives]Monitoring crop moisture conditions in real-time is critical for adopting water-saving irrigation and reducing China’s …
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UAV-Based Multi-Temporal Thermal Imaging to Evaluate Wheat Drought Resistance in Different Deficit Irrigation Regimes
Deficit irrigation is a common approach in water-scarce regions to balance productivity and water use, whereas drought stress still …
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Multi-angular Multispectral Sensing of Barley Awn Coloring
Barley awn coloring is a descriptive trait for barley (Hordeum vulgare) testing by the European Union. Rating the coloring visually is …
Machine Learning on the estimation of Leaf Area Index
The Leaf Area Index (LAI) is an important indicator in agriculture that can be considered a reliable plant growth parameter. The …
Hyperspectral Vegetation Indices to Assess Water and Nitrogen Status of Sweet Maize Crop
The deployment of novel technologies in the field of precision farming has risen to the top of global agendas in response to the impact …
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Foliar optical traits capture physiological and phenological leaf plasticity in Tilia×euchlora in the urban environment
Knowledge on the response of trees to the urban heat island (UHI) effect and soil sealing is currently limited, yet of vital importance …
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Estimation of Above-Ground Biomass of Winter Wheat Based on Consumer-Grade Multi-Spectral UAV
One of the problems of optical remote sensing of crop above-ground biomass (AGB) is that vegetation indices (VIs) often saturate from …
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Characterization of N distribution in different organs of winter wheat using UAV-based remote sensing
Although unmanned aerial vehicle (UAV) remote sensing is widely used for high-throughput crop monitoring, few attempts have been made …
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Cassava Detection from UAV Images Using YOLOv5 Object Detection Model: Towards Weed Control in a Cassava Farm
Most deep learning-based weed detection methods either yield high accuracy, but are slow for real-time applications or too …
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Spike growth affects spike fertility through the number of florets with green anthers before floret abortion in wheat
The dynamics of floret initiation/abortion has been explored widely; however, the mechanisms of increasing floret fertility at anthesis …
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Spectroscopic estimation of N concentration in wheat organs for assessing N remobilization under different irrigation regimes
Nitrogen (N) remobilization is a critical process that provides substantial N to winter wheat grains for improving yield productivity. …
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Sorghum Grains Grading for Food, Feed, and Fuel Using NIR Spectroscopy
Near-infrared spectroscopy (NIR) is a non-destructive, fast, and low-cost method to measure the grain quality of different cereals. …
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Predicting micronutrients of wheat using hyperspectral imaging
Micronutrients are the key factors to evaluate the nutritional quality of wheat. However, measuring micronutrients is time-consuming …
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Hyperspectral indices optimization algorithms for estimating canopy nitrogen concentration in potato (Solanum tuberosum L.)
Many empirical models based on hyperspectral indices (HIs) have been developed to estimate nitrogen (N) status of crops. However, most …
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Estimating Above-Ground Biomass of Potato Using Random Forest and Optimized Hyperspectral Indices
Spectral indices rarely show consistency in estimating crop traits across growth stages; thus, it is critical to simultaneously …
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‪Vitaliteit van stadsbomen. Het belang van omgeving en bodemschimmels‬

M Van Geel, K Yu, B Somers, & O Honnay (2020). ‪Vitaliteit van stadsbomen. Het belang van omgeving en bodemschimmels‬. Groencontact, 46(2): 16–19.

Urban Tree Health Classification Across Tree Species by Combining Airborne Laser Scanning and Imaging Spectroscopy
Declining urban tree health can affect critical ecosystem services, such as air quality improvement, temperature moderation, carbon …
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Spectral Vegetation Indices to Track Senescence Dynamics in Diverse Wheat Germplasm
The ability of a genotype to stay green affects the primary target traits grain yield (GY) and grain protein concentration (GPC) in …
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