About
24
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Introduction
I am an interdisciplinary scientist focusing on applying optical sensors within the scope of vegetation remote sensing. During multiple international (e.g., Australia, USA, Belgium) research periods, I used optical sensors across all scales (i.e., satellites, UAVs, and close-range). I am most interested in the interdisciplinary interfaces between plant pathology, plant functional traits, remote sensing, and data science.
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Publications
Publications (24)
The outwardly directed cell wall and associated plasma membrane of epidermal cells represent the first layers of plant defense against intruding pathogens. Cell wall modifications and the formation of defense structures at sites of attempted pathogen penetration are decisive for plant defense. A precise isolation of these stress-induced structures...
Epidermal structures (ES) of leaves are known to affect the functional properties andspectral responses. Spectral studies focused mostly on the effect of hairs or wax layers only. Westudied a wider range of different ES and their impact on spectral properties. Additionally, weidentified spectral regions that allow distinguishing different ES. We us...
In agriculture, the plant leaf angle influences light use efficiency and photosynthesis and, consequently, the overall crop performance. Leaf angle measurements are used in plant phenotyping, plant breeding, and remote sensing to study plant function and structure. Traditional manual leaf angle measurements have limited precision as they are labor-...
This study investigates the potential of high‐resolution (<0.5 cm/pixel) aerial imagery and convolutional neural networks (CNNs) for disease incidence scoring in sugar beet, focusing on two important aphid‐transmitted viruses, beet mild yellowing virus (BMYV) and beet chlorosis virus (BChV). The development of tolerant sugar beet cultivars is imper...
Optical sensors, mounted on uncrewed aerial vehicles (UAVs), are typically pointed straight downward to simplify structure-from-motion and image processing. High horizontal and vertical image overlap during UAV missions effectively leads to each object being measured from a range of different view angles, resulting in a rich multi-angular reflectan...
Remote sensing technologies provide the potential to support the breeding process of crop cultivars. The presented work suggests a new phenotyping routine for sugar beet genotypes, resistant or tolerant to beet mosaic virus (BtMV). The use of non-invasive sensors could potentially eliminate the need for time-consuming and expensive laboratory analy...
In the field of vegetation remote sensing, the concept and conversion across scales has been addressed—but not concluded—in recent years. A large array of different sensors is deployed using various platforms such as uncrewed aerial vehicles (UAVs) and satellites. In this context, while multiple concepts of scale exist, the influence of spatial sca...
Over the last two decades, UAVs have become an indispensable acquisition platform in the remote sensing community. Meanwhile, advanced lightweight sensors have been introduced in the market, including LiDAR scanners with multiple beams and hyperspectral cameras measuring reflectance using many different narrow-banded filters. To date, however, few...
Plant diseases pose a significant threat to agriculture. Precise and appropriately timed detection and identification of plant diseases is crucial for disease management, and for the selection of resistant and tolerant varieties. The detection of plant diseases by the human eye is dependent on the experience of the expert and on external influences...
Weed scoring is crucial to test the efficacy of herbicides and other weed management methods but has proven to be labor intense and variable across scoring individuals and time. This article provides a comparison of new approaches of weed scoring based on digital tools to conventional visual scoring. The first method collected aerial imagery that w...
Over the last 20 years, researchers in the field of digital plant pathology have chased the goal to implement sensors, machine learning and new technologies into knowledge-based methods for plant phenotyping and plant protection. However, the application of swiftly developing technologies has posed many challenges. Greenhouse and field applications...
This study examined the use of hyperspectral profiles for identifying three selected weed species in the alpine region of New South Wales, Australia. The targeted weeds included Orange Hawkweed, Mouse-ear Hawkweed and Ox-eye daisy, which have caused a great concern to regional biodiversity and health of the environment in Kosciuszko National Park....
Background
In arid environments, plant primary productivity is generally low and highly variable both spatially and temporally. Resources are not evenly distributed in space and time (e.g., soil nutrients, water), and depend on global (El Niño/ Southern Oscillation) and local climate parameters. The launch of the Sentinel2-satellite, part of the Eu...
Effective collaboration depends on an individual’s willingness to interact and share knowledge for generating solutions to complex theoretical or applied problems. These traits are especially important when problems are unlikely to be solved within the conceptual framework of a single discipline. The application of remote sensing methods in plant p...
Disease management in agriculture often assumes that pathogens are spread homogeneously across crops. In practice, pathogens can manifest in patches. Currently, disease detection is predominantly carried out by human assessors, which can be slow and expensive. A remote sensing approach holds promise. Current satellite sensors are not suitable to sp...
Since 2010 Australian ecosystems and managed landscapes have been severely threatened by the invasive fungal pathogen Austropuccinia psidii. Detecting and monitoring disease outbreaks is currently only possible by human assessors, which is slow and labour intensive. Over the last 25 years, spectral vegetation indices (SVIs) have been designed to as...
Hundreds of species in one of Australia's dominant plant families, the Myrtaceae, are at risk from the invasive pathogenic fungus Austropuccinia psidii. Since its arrival in Australia in 2010, native plant communities have been severely affected, with highly susceptible species likely to go extinct due to recurring infections. While severe impact o...
Hundreds of species in one of Australia’s dominant plant families, the Myrtaceae, are at risk from the invasive pathogenic fungus Austropuccinia psidii. Since its arrival in Australia in 2010, native plant communities have been severely affected, with highly susceptible species likely to go extinct due to recurring infections. While severe impact o...
Puccinia psidii is an invasive pathogen of global significance. Discovered in Brazil in 1884, it spread over Florida, Hawaii, Japan, China and finally reached Australia in 2010. Plant pathologists regard P. psidii (myrtle rust) as a threat to native Australian Myrtaceae - dominated ecosystems, and to industries depending on this plant family (e.g....
• Epidermal structures (ES) are known to affect leaf functional properties and spectral responses. • Plants with similar ES are defined as a plant functional type (PFT). • PFTs can aid in the understanding of ecological processes, such as the assembly and stability of communities and succession, at a range of scales. • It has been argued that it wo...
Questions
Questions (14)
Dear RG-Community,
I have been using Agisoft Metashape for UAV imagery processing for quite a while now. Also a while ago, I stumbled upon the Micasense GitHub repository and saw that individual radiometric correction procedures are recommended there (e.g., vignetting and row gradient removal -> https://micasense.github.io/imageprocessing/MicaSense%20Image%20Processing%20Tutorial%201.html). Now I was checking which of those radiometric corrections are also performed during processing in Agisoft Metashape. In the manual (Professional version) I could only find that vignetting is mentioned.
Did anyone of you know how to learn more about the detailed process in Agisoft Metashape..or even better..to perform a thorough radiometric image correction that removes any radiometric bias without running into the risk that this collides with the Agisoft Metashape process?
Thanks for your help,
Rene
Dear RG community,
I would like to sample (cut-off) maize leaves in the field and transport them to the lab for SLA (specific leaf area) measurements. After a brief literature review, I could not find an exact explanation on how the sampling is done without harming the leaves. As the leaves can be quite large (~60-70 cm), how would I best store 50-100 leaves in bags? Already cutting them in sections in the field might be an option and then store them in sealed bags. I am wondering if anyone ever only took a section of the leaf for SLA measurements and tested whether this causes a major error. For corn at least. Just using width and length and multiply it by a correction factor is not sufficient to get a good leaf area estimate I guess.
What would be the most practical way to do this without using huge bags and ice containers?
René
Dear RG community,
I am curious to hear your opinion. When we work with thermal imagery (UAV or ground), under what conditions are you more interested in precise temperature values (which would require a solid sensor calibration) and when is it enough for you to make a relative comparison?
I am really interested in real, and applied examples from agronomy, physiology, medicine and natural sciences in general.
Looking forward to a good discussion.
Cheers,
Rene
Dear RG Community,
I am currently working with thermal images of natural scenes. Throughout the scene I positioned large artificial targets that are painted black, grey and white.
I would like to detect those targets (position varies) in all of my images automatically. I won´t be able to work with a 3-layer RGB but only with a gray-scale one-layer image (example attached, there are 4 artificial, square targets in the example image). My initial approach was to use the SLIC algorithm to create superpixel areas. I am still working on that option.
I guess it should also be possible to find the artificial targets by filtering their similar, neighboring pixels? I am currently working in Python and my coding skills are advanced...but not amazing:-) I could also use R but would prefer a Python solution.
Any help would be much appreciated. Thanks for your time!
René
Dear RG Community,
I am currently trying to handle CDF files in R (xcms package). These files contain information that was yielded from a GCMS analysis. I start to understand that the Gaschromatography part outputs spectra (Signal Intensity vs Retention Time(Rt)). Then, the Massspectrometry seems to look at GC signals in finer detail and splits them up by their mass to charge (m/z) ratio, correct?
The xcms package seems to handle the CDF file just fine. However, I feel a bit lost when trying to understand how to handle/analyse the spectra. My first intention is to convert the Rt, m/z and the intensities into a single matrix/dataframe to apply alignment/normalization methods on it. But the total amount of spectra that come from a single CDF file is somewhat overwhelming.
Could you recommend good resources that help me to understand GCMS objects? I already did the standard google research but was not able to draw many conclusions from it.
Thanks!
Rene
