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H. W. Dehne

Bio: H. W. Dehne is an academic researcher from University of Bonn. The author has contributed to research in topics: Fusarium & Venturia inaequalis. The author has an hindex of 11, co-authored 21 publications receiving 903 citations.

Papers
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Journal ArticleDOI
TL;DR: A procedure for the early detection and differentiation of sugar beet diseases based on Support Vector Machines and spectral vegetation indices to discriminate diseased from non-diseased sugar beet leaves and to identify diseases even before specific symptoms became visible.

666 citations

Journal ArticleDOI
TL;DR: The results demonstrate a significant mycotoxin contamination associated with maize ear rots in Germany and indicate, with regard to anticipated climate change, that fumonisins-producing species already present in German maize production may become more important.
Abstract: High year-to-year variability in the incidence of Fusarium spp. and mycotoxin contamination was observed in a two-year survey investigating the impact of maize ear rot in 84 field samples from Germany. Fusarium verticillioides, F. graminearum, and F. proliferatum were the predominant species infecting maize kernels in 2006, whereas in 2007 the most frequently isolated species were F. graminearum, F. cerealis and F. subglutinans. Fourteen Fusarium-related mycotoxins were detected as contaminants of maize kernels analyzed by a multi-mycotoxin determination method. In 2006, a growth season characterized by high temperature and low rainfall during anthesis and early grain filling, 75% of the maize samples were contaminated with deoxynivalenol, 34% with fumonisins and 27% with zearalenone. In 2007, characterized by moderate temperatures and frequent rainfall during the entire growth season, none of the 40 maize samples had quantifiable levels of fumonisins while deoxynivalenol and zearalenone were detected in 90% and 93% of the fields, respectively. In addition, 3-acetyldeoxynivalenol, 15-acetyldeoxnivalenol, moniliformin, beauvericin, nivalenol and enniatin B were detected as common contaminants produced in both growing seasons. The results demonstrate a significant mycotoxin contamination associated with maize ear rots in Germany and indicate, with regard to anticipated climate change, that fumonisins-producing species already present in German maize production may become more important.

104 citations

Journal ArticleDOI
TL;DR: EPSPS gene amplification is the main mechanism contributing to glyphosate resistance in the A. tuberculatus populations analyzed, and EPSPS Vmax and Kcat values were more than doubled in resistant plants, indicating higher levels of catalytically active expressed EPSPS protein.
Abstract: The evolution of glyphosate-resistant weeds has recently increased dramatically. Six suspected glyphosate-resistant Amaranthus tuberculatus populations were studied to confirm resistance and determine the resistance mechanism. Resistance was confirmed in greenhouse for all six populations with glyphosate resistance factors (R/S) between 5.2 and 7.5. No difference in glyphosate absorption or translocation was observed between resistant and susceptible individuals. No mutation at amino acid positions G101, T102, or P106 was detected in the EPSPS gene coding sequence, the target enzyme of glyphosate. Analysis of EPSPS gene copy number revealed that all glyphosate-resistant populations possessed increased EPSPS gene copy number, and this correlated with increased expression at both RNA and protein levels. EPSPS Vmax and Kcat values were more than doubled in resistant plants, indicating higher levels of catalytically active expressed EPSPS protein. EPSPS gene amplification is the main mechanism contributing to glyphosate resistance in the A. tuberculatus populations analyzed.

75 citations

Journal ArticleDOI
TL;DR: The spatial pattern of Fusarium-infected kernels and their mycotoxin contamination was studied in four wheat fields in Germany using geo-referenced sampling grids (12-15 x 20-30 m, 28-30 samples per field) at harvest as discussed by the authors.
Abstract: The spatial pattern of Fusarium-infected kernels and their mycotoxin contamination was studied in four wheat fields in Germany using geo-referenced sampling grids (12-15 x 20-30 m, 28-30 samples per field) at harvest. For each sample, frequency of Fusarium-infected kernels and spectrum of species were assessed microbiologically; mycotoxin contents were determined by HPLC-MS/MS analysis. Spatial variability of pathogens and mycotoxins was analysed using various parameters including Spatial Analysis by Distance IndicEs ( sadie® ). Microdochium majus, the most frequent head blight pathogen in 1998, was less frequent in 1999 and could not be detected in kernels from two fields in 2004. Fusarium avenaceum, F. graminearum and F. poae were the most frequent Fusarium species, with 7-8 species per field. The frequency of Fusarium-infected kernels was 3-15% and the incidence of species showed considerable within-field variability. Spatial patterns varied among Fusarium species as well as from field to field. Although pathogens and mycotoxin were often distributed randomly in the field, F. avenaceum, F. graminearum, F. poae, F. sporotrichioides, F. tricinctum and the mycotoxin moniliformin had an aggregated pattern in at least one field. Patterns are discussed in relation to spread of Fusarium species depending on inoculum sources, spore type, kind of dispersal, availability of susceptible host tissue and micro-climate. Sampling of wheat fields for representative assessment of mycotoxins is complicated by random patterns of Fusarium-infected kernels, especially where the frequency of infection is small.

58 citations

Journal ArticleDOI
TL;DR: The NMDS approach was able to reproduce accurately the variety ranking and outlines the potential of hyperspectral imaging to phenotype the variety susceptibility for improved breeding processes.
Abstract: Interactions of Fusarium species with different wheat varieties differ in their temporal dynamics and symptom appearance. Reliable and objective approaches for monitoring processes during infection are demanded for plant phenotyping and disease rating. This study presents an automated method to phenotype wheat varieties to Fusarium head blight (FHB) using hyperspectral sensors. In time-series experiments, the optical properties of spikes infected with F. graminearum or F. culmorum were recorded. Two hyperspectral cameras, in visible and near-infrared (VIS-NIR, 400–1000 nm) and shortwave-infrared (SWIR, 1000–2500 nm) captured the most relevant bands for pigments, cell structure, water and further compounds. Correlations between disease severity (DS), spike weight, spectral bands and vegetation indices were investigated. Following, the detectability of infections was assessed by Support Vector Machine (SVM) classifier. A variety ranking based on AUDPC was performed and compared to a fully-automated approach using Non-metric Multi-Dimensional Scaling (NMDS). High correlation was found between the spectral signature and DS in 430–525 nm, 560–710 nm and 1115–2500 nm. All indices from the VIS-NIR showed high correlation with DS and, for the first time, this was also confirmed for three indices from the SWIR: NDNI, CAI and MSI. Using SVM, differentiation between healthy and infected spikes was possible (acc. > 0.76). Furthermore, the possibility to differentiate between F. graminearum and F. culmorum infected spikes has been verified. The NMDS approach was able to reproduce accurately the variety ranking and outlines the potential of hyperspectral imaging to phenotype the variety susceptibility for improved breeding processes.

48 citations


Cited by
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Journal ArticleDOI
TL;DR: A new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks, which is able to recognize 13 different types of plant diseases out of healthy leaves.
Abstract: The latest generation of convolutional neural networks (CNNs) has achieved impressive results in the field of image classification. This paper is concerned with a new approach to the development of plant disease recognition model, based on leaf image classification, by the use of deep convolutional networks. Novel way of training and the methodology used facilitate a quick and easy system implementation in practice. The developed model is able to recognize 13 different types of plant diseases out of healthy leaves, with the ability to distinguish plant leaves from their surroundings. According to our knowledge, this method for plant disease recognition has been proposed for the first time. All essential steps required for implementing this disease recognition model are fully described throughout the paper, starting from gathering images in order to create a database, assessed by agricultural experts. Caffe, a deep learning framework developed by Berkley Vision and Learning Centre, was used to perform the deep CNN training. The experimental results on the developed model achieved precision between 91% and 98%, for separate class tests, on average 96.3%.

1,135 citations

Journal ArticleDOI
TL;DR: The most relevant areas of application of sensor-based analyses are precision agriculture and plant phenotyping as discussed by the authors, which is facilitated by highly sophisticated and innovative methods of data analysis that lead to new insights derived from sensor data for complex plant-pathogen systems.
Abstract: Early and accurate detection and diagnosis of plant diseases are key factors in plant production and the reduction of both qualitative and quantitative losses in crop yield. Optical techniques, such as RGB imaging, multi- and hyperspectral sensors, thermography, or chlorophyll fluorescence, have proven their potential in automated, objective, and reproducible detection systems for the identification and quantification of plant diseases at early time points in epidemics. Recently, 3D scanning has also been added as an optical analysis that supplies additional information on crop plant vitality. Different platforms from proximal to remote sensing are available for multiscale monitoring of single crop organs or entire fields. Accurate and reliable detection of diseases is facilitated by highly sophisticated and innovative methods of data analysis that lead to new insights derived from sensor data for complex plant-pathogen systems. Nondestructive, sensor-based methods support and expand upon visual and/or molecular approaches to plant disease assessment. The most relevant areas of application of sensor-based analyses are precision agriculture and plant phenotyping.

680 citations

Journal ArticleDOI
TL;DR: This work provides a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.

633 citations

Journal ArticleDOI
TL;DR: Modern methods based on nucleic acid and protein analysis are described, which represent unprecedented tools to render agriculture more sustainable and safe, avoiding expensive use of pesticides in crop protection.
Abstract: Plant diseases are responsible for major economic losses in the agricultural industry worldwide. Monitoring plant health and detecting pathogen early are essential to reduce disease spread and facilitate effective management practices. DNA-based and serological methods now provide essential tools for accurate plant disease diagnosis, in addition to the traditional visual scouting for symptoms. Although DNA-based and serological methods have revolutionized plant disease detection, they are not very reliable at asymptomatic stage, especially in case of pathogen with systemic diffusion. They need at least 1–2 days for sample harvest, processing, and analysis. Here, we describe modern methods based on nucleic acid and protein analysis. Then, we review innovative approaches currently under development. Our main findings are the following: (1) novel sensors based on the analysis of host responses, e.g., differential mobility spectrometer and lateral flow devices, deliver instantaneous results and can effectively detect early infections directly in the field; (2) biosensors based on phage display and biophotonics can also detect instantaneously infections although they can be integrated with other systems; and (3) remote sensing techniques coupled with spectroscopy-based methods allow high spatialization of results, these techniques may be very useful as a rapid preliminary identification of primary infections. We explain how these tools will help plant disease management and complement serological and DNA-based methods. While serological and PCR-based methods are the most available and effective to confirm disease diagnosis, volatile and biophotonic sensors provide instantaneous results and may be used to identify infections at asymptomatic stages. Remote sensing technologies will be extremely helpful to greatly spatialize diagnostic results. These innovative techniques represent unprecedented tools to render agriculture more sustainable and safe, avoiding expensive use of pesticides in crop protection.

553 citations

Journal ArticleDOI
01 Oct 2012-Toxins
TL;DR: The data published since 2004 concerning the contamination of animal feed with single or combinations of mycotoxins and the occurrence of these co-contaminations are reviewed and highlighted.
Abstract: Mycotoxins are secondary metabolites produced by fungi especially those belonging to the genus Aspergillus, Penicillum and Fusarium. Mycotoxin contamination can occur in all agricultural commodities in the field and/or during storage, if conditions are favourable to fungal growth. Regarding animal feed, five mycotoxins (aflatoxins, deoxynivalenol, zearalenone, fumonisins and ochratoxin A) are covered by EU legislation (regulation or recommendation). Transgressions of these limits are rarely observed in official monitoring programs. However, low level contamination by Fusarium toxins is very common (e.g., deoxynivalenol (DON) is typically found in more than 50% of the samples) and co-contamination is frequently observed. Multi-mycotoxin studies reported 75%–100% of the samples to contain more than one mycotoxin which could impact animal health at already low doses. Co-occurrence of mycotoxins is likely to arise for at least three different reasons (i) most fungi are able to simultaneously produce a number of mycotoxins, (ii) commodities can be contaminated by several fungi, and (iii) completed feed is made from various commodities. In the present paper, we reviewed the data published since 2004 concerning the contamination of animal feed with single or combinations of mycotoxins and highlighted the occurrence of these co-contaminations.

503 citations