20,791 research outputs found
In-Field Detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale
No full textThe application of hyperspectral imaging technology for plant disease detection in the field is still challenging. Existing equipment and analysis algorithms are adapted to highly controlled environmental conditions in the laboratory. However, only real time information from the field scale is able to guide plant protection measures and to optimize the use of resources. At the field scale, many parameters such as the optimal measurement distance, informative feature sets, and suitable algorithms have not been investigated. In this study, the hyperspectral detection and quantification of yellow rust in wheat was evaluated using two measurement platforms: a ground-based vehicle and an unmanned aerial vehicle (UAV). Different disease development stages and disease severities were provided in a plot-based field experiment. Measurements were performed weekly during the vegetation period. Data analysis was performed by three prediction algorithms with a focus on the selection of optimal feature sets. In this context, the across-scale application of optimized feature sets, an approach of information transfer between scales, was also evaluated. Relevant aspects for an on-line disease assessment in the field integrating affordable sensor technology, sensor spatial resolution, compact analysis models, and fast evaluation have been outlined and reflected upon. For the first time, a hyperspectral imaging observation experiment of a plant disease was comparatively performed at two scales, ground canopy and UAV
Potential of hyperspectral imaging to detect and identify the impact of chemical warfare compounds on plant tissue
No full textThe OPCW Member states cover 98% of the global population and landmass. Regrettably, unanticipated chemical warfare agent assaults are reported during the last decades. In addition to the frequent threat situation, the sampling of bio-medical samples from these areas is critical and mainly depends on investigation opportunities of victims. Non-contact sensor technologies are desirable to enable a fast and secure estimation of a situation. Plants react on pollution because of their direct interaction with gases and it is assumed that chemical warfare agents influence plants, respectively. This impact can be analyzed for the detection and characterization of chemical warfare assaults. Nowadays technological progress in digital technologies provides new innovations in detectors, data analysis approaches and software availability which could improve the screening, monitoring and analysis of chemical warfare. Within this context hyperspectral imaging (HSI) is a promising method. Different applications from remote to close range sensing in medicine, food production, military, geography and agriculture do exist already. During the last years HSI showed high potential to determine and assess different plant parameters, e.g. abiotic and biotic stresses by recording the spectral reflectance of plants. Within the present manuscript, the basics principle of HSI as an innovative technique, aspects of recording and analyzing HSI data is presented using wild growing apple leaves which are treated with sulfuric acid, fire or heat. Resulting spectral signatures showed significant changes among the treatments. Especially the shortwave infrared was sensitive to changes due to the different treatments. Furthermore, the calculation of common spectral indices revealed differences due to the treatments which are not visible to the human eye. The results support HSI applications for the detection of chemical warfare agents and elucidate the impact of chemical warfare on plants
Assessment of Fusarium Infection and Mycotoxin Contamination of Wheat Kernels and Flour Using Hyperspectral Imaging
No full textFusarium head blight (FHB) epidemics in wheat and contamination with Fusarium mycotoxins has become an increasing problem over the last decades. This prompted the need for non-invasive and non-destructive techniques to screen cereal grains for Fusarium infection, which is usually accompanied by mycotoxin contamination. This study tested the potential of hyperspectral imaging to monitor the infection of wheat kernels and flour with three Fusarium species. Kernels of two wheat varieties inoculated at anthesis with F. graminearum, F. culmorum, and F. poae were investigated. Hyperspectral images of kernels and flour were taken in the visible-near infrared (VIS-NIR) (400–1000 nm) and short-wave infrared (SWIR) (1000–2500 nm) ranges. The fungal DNA and mycotoxin contents were quantified. Spectral reflectance of Fusarium-damaged kernels (FDK) was significantly higher than non-inoculated ones. In contrast, spectral reflectance of flour from non-inoculated kernels was higher than that of FDK in the VIS and lower in the NIR and SWIR ranges. Spectral reflectance of kernels was positively correlated with fungal DNA and deoxynivalenol (DON) contents. In the case of the flour, this correlation exceeded r = −0.80 in the VIS range. Remarkable peaks of correlation appeared at 1193, 1231, 1446 to 1465, and 1742 to 2500 nm in the SWIR range
Sektion 43 Digitalisierung
No full text43-1 - Hyperspektrale Anaylse von frühen Wirt-Parasit-Interaktionen im UVBereichAnna Brugger, Jan Behmann, Matheus Thomas Kuska, Ulrike Steiner, Anne-Katrin Mahlein
43-2 - Potential of hyperspectral imaging to quantify Fusarium mycotoxins in wheat kernels and flourElias Alisaac, Jan Behmann, Matheus Thomas Kuska, Heinz-Wilhelm Dehne, Anne-Katrin Mahlein
43-3 - Nicht-invasive Charakterisierung spektraler Dynamiken von Pilzerkrankungen im Weizen: Erstellung einer spektralen BibliothekDavid Bohnenkamp, Jan Behmann, Ulrike Steiner, Anne-Katrin Mahlein
43-4 - Deep Learning für die Identifikation und Charakterisierung von pilzlichen Blattkrankheiten des Weizens in hyperspektralen BildernJan Behmann, David Bohnenkamp, Anne-Katrin Mahlein
43-5 - Erweiterte Diagnosen im Satellitenbild zur Automatisierung von Behandlungs-empfehlungen im AckerbauKatrin Kohler, Peter Baumann, Vlad Merticariu, Ali Al Masri, Ismoil Isroilov, Abidur Khan, Layth Sahib
43-6 - Identifizierung von Schaderregern im Ackerbau mittels UAVBernd Hoffmann, Antje Augstein, Martin von Kameke, Oliver Martinez, Nikolaus Schackmann, Christian Wolff, Benno Kleinhenz
43-7 - Sensorbasierte, teilflächenspezifische Unkrautbekämpfung im Mais: Ergebnisse eines 3-jährigen GroßflächenversuchesHermann Leithold, Hubert Schmeer, Peer Leithold, Steffen Müller
43-8 - Untersuchung der Wirkung verschiedener Wachstumsregler auf die Physiologie von Weizen und Gerste mit der Hochdurchsatz Feldphänotypisierungsplattform Phenotrac IVMichael Heß, Gero Barmeier, Tobias Erven43-1 - Hyperspectrale analysis of early host-pathogen interaction in UV-rangeAnna Brugger, Jan Behmann, Matheus Thomas Kuska, Ulrike Steiner, Anne-Katrin Mahlein
43-2 - Potential of hyperspectral imaging to quantify Fusarium mycotoxins in wheat kernels and flourElias Alisaac, Jan Behmann, Matheus Thomas Kuska, Heinz-Wilhelm Dehne, Anne-Katrin Mahlein
43-3 - Non-invasive characterization of spectral dynamics of fungal diseases in wheat: Generation of a spectral libraryDavid Bohnenkamp, Jan Behmann, Ulrike Steiner, Anne-Katrin Mahlein
43-4 - Deep Learning for the identification and characterization of fungal leaf diseases of wheat in hyperspectral imagesJan Behmann, David Bohnenkamp, Anne-Katrin Mahlein
43-5 - Extended diagnostics in satellite images for the automation of treatment recommendations in agricultureKatrin Kohler, Peter Baumann, Vlad Merticariu, Ali Al Masri, Ismoil Isroilov, Abidur Khan, Layth Sahib
43-6 - UAV-based pest identification in AgricultureBernd Hoffmann, Antje Augstein, Martin von Kameke, Oliver Martinez, Nikolaus Schackmann, Christian Wolff, Benno Kleinhenz
43-7 - Site-specific application of herbicides in maize based on H-Sensor: Results from 3 years of On-Farm-Research trialsHermann Leithold, Hubert Schmeer, Peer Leithold, Steffen Müller
43-8 - Investigation of the effects of different growth regulators on the physiology of barley and wheat with high troughput field phenotyping platform Phenotrac IVMichael Heß, Gero Barmeier, Tobias Erve
Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed!
No full textDetermination and characterization of resistance reactions of crops against fungal pathogens are essential to select resistant genotypes. In plant breeding, phenotyping of genotypes is realized by time consuming and expensive visual plant ratings. During resistance reactions and during pathogenesis plants initiate different structural and biochemical defence mechanisms, which partly affect the optical properties of plant organs. Recently, intensive research has been conducted to develop innovative optical methods for an assessment of compatible and incompatible plant pathogen interaction. These approaches, combining classical phytopathology or microbiology with technology driven methods — such as sensors, robotics, machine learning, and artificial intelligence — are summarized by the term digital phenotyping. In contrast to common visual rating, detection and assessment methods, optical sensors in combination with advanced data analysis methods are able to retrieve pathogen induced changes in the physiology of susceptible or resistant plants non-invasively and objectively. Phenotyping disease resistance aims different tasks. In an early breeding step, a qualitative assessment and characterization of specific resistance action is aimed to link it, for example, to a genetic marker. Later, during greenhouse and field screening, the assessment of the level of susceptibility of different genotypes is relevant. Within this review, recent advances of digital phenotyping technologies for the detection of subtle resistance reactions and resistance breeding are highlighted and methodological requirements are critically discusse
A Hyperspectral Library of Foliar Diseases of Wheat
No full textThis work established a hyperspectral library of important foliar diseases of wheat in time series to detect spectral changes from infection to symptom appearance induced by different pathogens. The data was generated under controlled conditions at the leaf-scale. The transition from healthy to diseased leaf tissue was assessed, spectral shifts were identified and used in combination with histological investigations to define developmental stages in pathogenesis for each disease. The spectral signatures of each plant disease that are indicative of a certain developmental stage during pathogenesis - defined as turning points - were combined into a spectral library. Different machine learning analysis methods were applied and compared to test the potential of this library for the detection and quantification of foliar diseases in hyperspectral images. All evaluated classifiers provided a high accuracy for the detection and identification for both the biotrophic fungi and the necrotrophic fungi of up to 99%. The potential of applying spectral analysis methods, in combination with a spectral library for the detection and identification of plant diseases is demonstrated. Further evaluation and development of these algorithms should contribute to a robust detection and identification system for plant diseases at different developmental stages and the promotion and development of site-specific management techniques of plant diseases under field conditions
Jan Kapr's contribution to contemporary music : an essay about a composer and teacher
No full textThis creative project is a treatise on a leading personality of Czechoslovakian musical life, the composer, Jan Kapr. The author discusses the following:1. The complicated development of Kapr's career and work, 2. Kapr's method of organization of musical material in a composition, as described in his book Constants,3. His former and current style which is demonstrated in two of his compositions, Concert Variations, for flute and string orchestra and Testimonies for four solo instruments,4. Two of his recent works, Exercises for Gydli and the Symphony No. 7, Country of Childhood.Thesis (M.A.
Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring <i>Fusarium</i> Head Blight of Wheat on Spikelet Scale
No full textOptical sensors have shown high capabilities to improve the detection and monitoring of plant disease development. This study was designed to compare the feasibility of different sensors to characterize Fusarium head blight (FHB) caused by Fusarium graminearum and Fusarium culmorum. Under controlled conditions, time-series measurements were performed with infrared thermography (IRT), chlorophyll fluorescence imaging (CFI), and hyperspectral imaging (HSI) starting 3 days after inoculation (dai). IRT allowed the visualization of temperature differences within the infected spikelets beginning 5 dai. At the same time, a disorder of the photosynthetic activity was confirmed by CFI via maximal fluorescence yields of spikelets (Fm) 5 dai. Pigment-specific simple ratio PSSRa and PSSRb derived from HSI allowed discrimination between Fusarium-infected and non-inoculated spikelets 3 dai. This effect on assimilation started earlier and was more pronounced with F. graminearum. Except the maximum temperature difference (MTD), all parameters derived from different sensors were significantly correlated with each other and with disease severity (DS). A support vector machine (SVM) classification of parameters derived from IRT, CFI, or HSI allowed the differentiation between non-inoculated and infected spikelets 3 dai with an accuracy of 78, 56 and 78%, respectively. Combining the IRT-HSI or CFI-HSI parameters improved the accuracy to 89% 30 dai
Going Beyond Counting First Authors in Author Co-citation Analysis
No full textThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
ELEVEN FACES OF JAN GOGOL, JR.
No full textAuthor Jan Rendl in his thesis attempts to look at the world of ideas and educator Jan
Gogola ml. through the eleven chapters in which each chapter somehow characterizes itself by Jan Gogola ml. and each of them somehow determines its creative ideas of it through the metaphor of a football match when Jan Gogola, with its characters, movies himself a teammate, as well as defensively. It gives goals with their situations as well as occasionally digging his opponents ankles.
Jan Gogola ml. thus embodies one stage of the Department of Documentary Film at FAMU, which often stands at the intersection between teaching activities and Karel Vachek among students who applied by them during their seminars psychological methods that work must be peculiarly associated with the author of the film
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