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Dr. Jan Behmann
University of Bonn, Institute of Plant Sciences and Resource Conservation, Nussallee 9, 53115 Bonn, Germany

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0 Camera Calibration
0 Computer Vision
0 Hyperspectral Imaging
0 Machine Learning
0 Plant Phenotyping

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Hyperspectral Imaging
Plant Phenotyping
Machine Learning
Computer Vision

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Research article
Published: 15 February 2021 in Phytopathology®
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This 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.

ACS Style

David Bohnenkamp; Jan Behmann; Stefan Paulus; Ulrike Steiner; Anne-Katrin Mahlein. A Hyperspectral Library of Foliar Diseases of Wheat. Phytopathology® 2021, 1 .

AMA Style

David Bohnenkamp, Jan Behmann, Stefan Paulus, Ulrike Steiner, Anne-Katrin Mahlein. A Hyperspectral Library of Foliar Diseases of Wheat. Phytopathology®. 2021; ():1.

Chicago/Turabian Style

David Bohnenkamp; Jan Behmann; Stefan Paulus; Ulrike Steiner; Anne-Katrin Mahlein. 2021. "A Hyperspectral Library of Foliar Diseases of Wheat." Phytopathology® , no. : 1.

Journal article
Published: 25 October 2019 in Remote Sensing
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The 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.

ACS Style

David Bohnenkamp; Jan Behmann; Anne-Katrin Mahlein. In-Field Detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale. Remote Sensing 2019, 11, 2495 .

AMA Style

David Bohnenkamp, Jan Behmann, Anne-Katrin Mahlein. In-Field Detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale. Remote Sensing. 2019; 11 (21):2495.

Chicago/Turabian Style

David Bohnenkamp; Jan Behmann; Anne-Katrin Mahlein. 2019. "In-Field Detection of Yellow Rust in Wheat on the Ground Canopy and UAV Scale." Remote Sensing 11, no. 21: 2495.

Journal article
Published: 21 September 2019 in Toxins
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Fusarium 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.

ACS Style

Elias Alisaac; Jan Behmann; Anna Rathgeb; Petr Karlovsky; Heinz-Wilhelm Dehne; Anne-Katrin Mahlein. Assessment of Fusarium Infection and Mycotoxin Contamination of Wheat Kernels and Flour Using Hyperspectral Imaging. Toxins 2019, 11, 556 .

AMA Style

Elias Alisaac, Jan Behmann, Anna Rathgeb, Petr Karlovsky, Heinz-Wilhelm Dehne, Anne-Katrin Mahlein. Assessment of Fusarium Infection and Mycotoxin Contamination of Wheat Kernels and Flour Using Hyperspectral Imaging. Toxins. 2019; 11 (10):556.

Chicago/Turabian Style

Elias Alisaac; Jan Behmann; Anna Rathgeb; Petr Karlovsky; Heinz-Wilhelm Dehne; Anne-Katrin Mahlein. 2019. "Assessment of Fusarium Infection and Mycotoxin Contamination of Wheat Kernels and Flour Using Hyperspectral Imaging." Toxins 11, no. 10: 556.

Conference paper
Published: 08 July 2019 in Precision agriculture ’19
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ACS Style

J. Behmann; D. Bohnenkamp; A.-K. Mahlein. Image-based assessment of hyperspectral reflectance characteristics of plants in the field: Lessons Learned. Precision agriculture ’19 2019, 1 .

AMA Style

J. Behmann, D. Bohnenkamp, A.-K. Mahlein. Image-based assessment of hyperspectral reflectance characteristics of plants in the field: Lessons Learned. Precision agriculture ’19. 2019; ():1.

Chicago/Turabian Style

J. Behmann; D. Bohnenkamp; A.-K. Mahlein. 2019. "Image-based assessment of hyperspectral reflectance characteristics of plants in the field: Lessons Learned." Precision agriculture ’19 , no. : 1.

Journal article
Published: 12 June 2019 in Remote Sensing
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Previous plant phenotyping studies have focused on the visible (VIS, 400–700 nm), near-infrared (NIR, 700–1000 nm) and short-wave infrared (SWIR, 1000–2500 nm) range. The ultraviolet range (UV, 200–380 nm) has not yet been used in plant phenotyping even though a number of plant molecules like flavones and phenol feature absorption maxima in this range. In this study an imaging UV line scanner in the range of 250–430 nm is introduced to investigate crop plants for plant phenotyping. Observing plants in the UV-range can provide information about important changes of plant substances. To record reliable and reproducible time series results, measurement conditions were defined that exclude phototoxic effects of UV-illumination in the plant tissue. The measurement quality of the UV-camera has been assessed by comparing it to a non-imaging UV-spectrometer by measuring six different plant-based substances. Given the findings of these preliminary studies, an experiment has been defined and performed monitoring the stress response of barley leaves to salt stress. The aim was to visualize the effects of abiotic stress within the UV-range to provide new insights into the stress response of plants. Our study demonstrated the first use of a hyperspectral sensor in the UV-range for stress detection in plant phenotyping.

ACS Style

Anna Brugger; Jan Behmann; Stefan Paulus; Hans-Georg Luigs; Matheus Thomas Kuska; Patrick Schramowski; Kristian Kersting; Ulrike Steiner; Anne-Katrin Mahlein. Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range. Remote Sensing 2019, 11, 1401 .

AMA Style

Anna Brugger, Jan Behmann, Stefan Paulus, Hans-Georg Luigs, Matheus Thomas Kuska, Patrick Schramowski, Kristian Kersting, Ulrike Steiner, Anne-Katrin Mahlein. Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range. Remote Sensing. 2019; 11 (12):1401.

Chicago/Turabian Style

Anna Brugger; Jan Behmann; Stefan Paulus; Hans-Georg Luigs; Matheus Thomas Kuska; Patrick Schramowski; Kristian Kersting; Ulrike Steiner; Anne-Katrin Mahlein. 2019. "Extending Hyperspectral Imaging for Plant Phenotyping to the UV-Range." Remote Sensing 11, no. 12: 1401.

Journal article
Published: 17 May 2019 in Sensors
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Optical 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.

ACS Style

Anne-Katrin Mahlein; Elias Alisaac; Ali Al Masri; Jan Behmann; Heinz-Wilhelm Dehne; Erich-Christian Oerke. Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Fusarium Head Blight of Wheat on Spikelet Scale. Sensors 2019, 19, 2281 .

AMA Style

Anne-Katrin Mahlein, Elias Alisaac, Ali Al Masri, Jan Behmann, Heinz-Wilhelm Dehne, Erich-Christian Oerke. Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Fusarium Head Blight of Wheat on Spikelet Scale. Sensors. 2019; 19 (10):2281.

Chicago/Turabian Style

Anne-Katrin Mahlein; Elias Alisaac; Ali Al Masri; Jan Behmann; Heinz-Wilhelm Dehne; Erich-Christian Oerke. 2019. "Comparison and Combination of Thermal, Fluorescence, and Hyperspectral Imaging for Monitoring Fusarium Head Blight of Wheat on Spikelet Scale." Sensors 19, no. 10: 2281.

Research article
Published: 19 March 2019 in PLOS ONE
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Hyperspectral imaging has proved its potential for evaluating complex plant-pathogen interactions. However, a closer link of the spectral signatures and genotypic characteristics remains elusive. Here, we show relation between gene expression profiles and specific wavebands from reflectance during three barley—powdery mildew interactions. Significant synergistic effects between the hyperspectral signal and the corresponding gene activities has been shown using the linear discriminant analysis (LDA). Combining the data sets of hyperspectral signatures and gene expression profiles allowed a more precise differentiation of the three investigated barley-Bgh interactions independent from the time after inoculation. This shows significant synergistic effects between the hyperspectral signal and the corresponding gene activities. To analyze this coherency between spectral reflectance and seven different gene expression profiles, relevant wavelength bands and reflectance intensities for each gene were computed using the Relief algorithm. Instancing, xylanase activity was indicated by relevant wavelengths around 710 nm, which are characterized by leaf and cell structures. HvRuBisCO activity underlines relevant wavebands in the green and red range, elucidating the coherency of RuBisCO to the photosynthesis apparatus and in the NIR range due to the influence of RuBisCO on barley leaf cell development. These findings provide the first insights to links between gene expression and spectral reflectance that can be used for an efficient non-invasive phenotyping of plant resistance and enables new insights into plant-pathogen interactions.

ACS Style

Matheus Thomas Kuska; Jan Behmann; Mahsa Namini; Erich-Christian Oerke; Ulrike Steiner; Anne-Katrin Mahlein. Discovering coherency of specific gene expression and optical reflectance properties of barley genotypes differing for resistance reactions against powdery mildew. PLOS ONE 2019, 14, e0213291 .

AMA Style

Matheus Thomas Kuska, Jan Behmann, Mahsa Namini, Erich-Christian Oerke, Ulrike Steiner, Anne-Katrin Mahlein. Discovering coherency of specific gene expression and optical reflectance properties of barley genotypes differing for resistance reactions against powdery mildew. PLOS ONE. 2019; 14 (3):e0213291.

Chicago/Turabian Style

Matheus Thomas Kuska; Jan Behmann; Mahsa Namini; Erich-Christian Oerke; Ulrike Steiner; Anne-Katrin Mahlein. 2019. "Discovering coherency of specific gene expression and optical reflectance properties of barley genotypes differing for resistance reactions against powdery mildew." PLOS ONE 14, no. 3: e0213291.

Journal article
Published: 04 December 2018 in Journal of Imaging
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The characterization of plant disease symptoms by hyperspectral imaging is often limited by the missing ability to investigate early, still invisible states. Automatically tracing the symptom position on the leaf back in time could be a promising approach to overcome this limitation. Therefore we present a method to spatially reference time series of close range hyperspectral images. Based on reference points, a robust method is presented to derive a suitable transformation model for each observation within a time series experiment. A non-linear 2D polynomial transformation model has been selected to cope with the specific structure and growth processes of wheat leaves. The potential of the method is outlined by an improved labeling procedure for very early symptoms and by extracting spectral characteristics of single symptoms represented by Vegetation Indices over time. The characteristics are extracted for brown rust and septoria tritici blotch on wheat, based on time series observations using a VISNIR (400–1000 nm) hyperspectral camera.

ACS Style

Jan Behmann; David Bohnenkamp; Stefan Paulus; Anne-Katrin Mahlein. Spatial Referencing of Hyperspectral Images for Tracing of Plant Disease Symptoms. Journal of Imaging 2018, 4, 143 .

AMA Style

Jan Behmann, David Bohnenkamp, Stefan Paulus, Anne-Katrin Mahlein. Spatial Referencing of Hyperspectral Images for Tracing of Plant Disease Symptoms. Journal of Imaging. 2018; 4 (12):143.

Chicago/Turabian Style

Jan Behmann; David Bohnenkamp; Stefan Paulus; Anne-Katrin Mahlein. 2018. "Spatial Referencing of Hyperspectral Images for Tracing of Plant Disease Symptoms." Journal of Imaging 4, no. 12: 143.

Original research article
Published: 23 July 2018 in Frontiers in Plant Science
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Molecular marker analysis allow for a rapid and advanced pre-selection and resistance screenings in plant breeding processes. During the phenotyping process, optical sensors have proved their potential to determine and assess the function of the genotype of the breeding material. Thereby, biomarkers for specific disease resistance traits provide valuable information for calibrating optical sensor approaches during early plant-pathogen interactions. In this context, the combination of physiological, metabolic phenotyping and phenomic profiles could establish efficient identification and quantification of relevant genotypes within breeding processes. Experiments were conducted with near-isogenic lines of H. vulgare (susceptible, mildew locus o (mlo) and Mildew locus a (Mla) resistant). Multispectral imaging of barley plants was daily conducted 0–8 days after inoculation (dai) in a high-throughput facility with 10 wavelength bands from 400 to 1,000 nm. In parallel, the temporal dynamics of the activities of invertase isoenzymes, as key sink specific enzymes that irreversibly cleave the transport sugar sucrose into the hexose monomers, were profiled in a semi high-throughput approach. The activities of cell wall, cytosolic and vacuole invertase revealed specific dynamics of the activity signatures for susceptible genotypes and genotypes with mlo and Mla based resistances 0–120 hours after inoculation (hai). These patterns could be used to differentiate between interaction types and revealed an early influence of Blumeria graminis f.sp. hordei (Bgh) conidia on the specific invertase activity already 0.5 hai. During this early powdery mildew pathogenesis, the reflectance intensity increased in the blue bands and at 690 nm. The Mla resistant plants showed an increased reflectance at 680 and 710 nm and a decreased reflectance in the near infrared bands from 3 dai. Applying a Support Vector Machine classification as a supervised machine learning approach, the pixelwise identification and quantification of powdery mildew diseased barley tissue and hypersensitive response spots were established. This enables an automatic identification of the barley-powdery mildew interaction. The study established a proof-of-concept for plant resistance phenotyping with multispectral imaging in high-throughput. The combination of invertase analysis and multispectral imaging showed to be a complementing validation system. This will provide a deeper understanding of optical data and its implementation into disease resistance screening.

ACS Style

Matheus T. Kuska; Jan Behmann; Dominik K. Großkinsky; Thomas Roitsch; Anne-Katrin Mahlein. Screening of Barley Resistance Against Powdery Mildew by Simultaneous High-Throughput Enzyme Activity Signature Profiling and Multispectral Imaging. Frontiers in Plant Science 2018, 9, 1074 .

AMA Style

Matheus T. Kuska, Jan Behmann, Dominik K. Großkinsky, Thomas Roitsch, Anne-Katrin Mahlein. Screening of Barley Resistance Against Powdery Mildew by Simultaneous High-Throughput Enzyme Activity Signature Profiling and Multispectral Imaging. Frontiers in Plant Science. 2018; 9 ():1074.

Chicago/Turabian Style

Matheus T. Kuska; Jan Behmann; Dominik K. Großkinsky; Thomas Roitsch; Anne-Katrin Mahlein. 2018. "Screening of Barley Resistance Against Powdery Mildew by Simultaneous High-Throughput Enzyme Activity Signature Profiling and Multispectral Imaging." Frontiers in Plant Science 9, no. : 1074.

Journal article
Published: 08 June 2018 in Plant Methods
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Phenotyping is a bottleneck for the development of new plant cultivars. This study introduces a new hyperspectral phenotyping system, which combines the high throughput of canopy scale measurements with the advantages of high spatial resolution and a controlled measurement environment. Furthermore, the measured barley canopies were grown in large containers (called Mini-Plots), which allow plants to develop field-like phenotypes in greenhouse experiments, without being hindered by pot size. Six barley cultivars have been investigated via hyperspectral imaging up to 30 days after inoculation with powdery mildew. With a high spatial resolution and stable measurement conditions, it was possible to automatically quantify powdery mildew symptoms through a combination of Simplex Volume Maximization and Support Vector Machines. Detection was feasible as soon as the first symptoms were visible for the human eye during manual rating. An accurate assessment of the disease severity for all cultivars at each measurement day over the course of the experiment was realized. Furthermore, powdery mildew resistance based necrosis of one cultivar was detected as well. The hyperspectral phenotyping system combines the advantages of field based canopy level measurement systems (high throughput, automatization, low manual workload) with those of laboratory based leaf level measurement systems (high spatial resolution, controlled environment, stable conditions for time series measurements). This allows an accurate and objective disease severity assessment without the need for trained experts, who perform visual rating, as well as detection of disease symptoms in early stages. Therefore, it is a promising tool for plant resistance breeding.

ACS Style

Stefan Thomas; Jan Behmann; Angelina Steier; Thorsten Kraska; Onno Muller; Uwe Rascher; Anne-Katrin Mahlein. Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform. Plant Methods 2018, 14, 1 -12.

AMA Style

Stefan Thomas, Jan Behmann, Angelina Steier, Thorsten Kraska, Onno Muller, Uwe Rascher, Anne-Katrin Mahlein. Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform. Plant Methods. 2018; 14 (1):1-12.

Chicago/Turabian Style

Stefan Thomas; Jan Behmann; Angelina Steier; Thorsten Kraska; Onno Muller; Uwe Rascher; Anne-Katrin Mahlein. 2018. "Quantitative assessment of disease severity and rating of barley cultivars based on hyperspectral imaging in a non-invasive, automated phenotyping platform." Plant Methods 14, no. 1: 1-12.

Article
Published: 19 May 2018 in European Journal of Plant Pathology
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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.

ACS Style

Elias Alisaac; J. Behmann; M. T. Kuska; H.-W. Dehne; A.-K. Mahlein. Hyperspectral quantification of wheat resistance to Fusarium head blight: comparison of two Fusarium species. European Journal of Plant Pathology 2018, 152, 869 -884.

AMA Style

Elias Alisaac, J. Behmann, M. T. Kuska, H.-W. Dehne, A.-K. Mahlein. Hyperspectral quantification of wheat resistance to Fusarium head blight: comparison of two Fusarium species. European Journal of Plant Pathology. 2018; 152 (4):869-884.

Chicago/Turabian Style

Elias Alisaac; J. Behmann; M. T. Kuska; H.-W. Dehne; A.-K. Mahlein. 2018. "Hyperspectral quantification of wheat resistance to Fusarium head blight: comparison of two Fusarium species." European Journal of Plant Pathology 152, no. 4: 869-884.

Journal article
Published: 02 February 2018 in Sensors
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Hyperspectral imaging sensors are promising tools for monitoring crop plants or vegetation in different environments. Information on physiology, architecture or biochemistry of plants can be assessed non-invasively and on different scales. For instance, hyperspectral sensors are implemented for stress detection in plant phenotyping processes or in precision agriculture. Up to date, a variety of non-imaging and imaging hyperspectral sensors is available. The measuring process and the handling of most of these sensors is rather complex. Thus, during the last years the demand for sensors with easy user operability arose. The present study introduces the novel hyperspectral camera Specim IQ from Specim (Oulu, Finland). The Specim IQ is a handheld push broom system with integrated operating system and controls. Basic data handling and data analysis processes, such as pre-processing and classification routines are implemented within the camera software. This study provides an introduction into the measurement pipeline of the Specim IQ as well as a radiometric performance comparison with a well-established hyperspectral imager. Case studies for the detection of powdery mildew on barley at the canopy scale and the spectral characterization of Arabidopsis thaliana mutants grown under stressed and non-stressed conditions are presented.

ACS Style

Jan Behmann; Kelvin Acebron; Dzhaner Emin; Simon Bennertz; Shizue Matsubara; Stefan Thomas; David Bohnenkamp; Matheus T. Kuska; Jouni Jussila; Harri Salo; Anne-Katrin Mahlein; Uwe Rascher. Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection. Sensors 2018, 18, 441 .

AMA Style

Jan Behmann, Kelvin Acebron, Dzhaner Emin, Simon Bennertz, Shizue Matsubara, Stefan Thomas, David Bohnenkamp, Matheus T. Kuska, Jouni Jussila, Harri Salo, Anne-Katrin Mahlein, Uwe Rascher. Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection. Sensors. 2018; 18 (2):441.

Chicago/Turabian Style

Jan Behmann; Kelvin Acebron; Dzhaner Emin; Simon Bennertz; Shizue Matsubara; Stefan Thomas; David Bohnenkamp; Matheus T. Kuska; Jouni Jussila; Harri Salo; Anne-Katrin Mahlein; Uwe Rascher. 2018. "Specim IQ: Evaluation of a New, Miniaturized Handheld Hyperspectral Camera and Its Application for Plant Phenotyping and Disease Detection." Sensors 18, no. 2: 441.

Journal article
Published: 18 May 2016 in GeoInformatica
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Tracking the spatio-temporal activity is highly relevant for domains like security, health, and quality management. Since animal welfare became a topic in politics and legislation locomotion patterns of livestock have received increasing interest. In contrast to the monitoring of pedestrians cattle activity tracking poses special challenges to both sensors and data analysis. Interesting states are not directly observable by a single sensor. In addition, sensors must be accepted by cattle and need to be robust enough to cope with a rough environment. In this article, we introduce the novel combination of heart rate and positioning sensors. Attached to neck and chest they are less interfering than accelerometers at the ankles. Exploiting the potential of such combined sensor system that records locomotion and non-spatial information from the heart rate sensor however is challenging. We introduce a novel two level method for the activity tracking focused on the duration and sequence of activity states. We combine Support Vector Machine (SVM) with Conditional Random Field (CRF) and extend Conditional Random fields by an explicit representation of duration. The SVM characterizes local activity states, whereas the CRF addresses sequences of local states to sequences incorporating spatial and non-spatial contextual knowledge. This combination provides a reliable and comprehensive identification of defined activity patterns, as well as their chronology and durations, suitable for the integration in an activity data base. This data base is used to extract physiological parameters and promises insights into internal states such as fitness, well-being and stress. Interestingly we were able to demonstrate a significant correlation between resting pulse rate and the day of pregnancy.

ACS Style

Jan Behmann; Kathrin Hendriksen; Ute Müller; Wolfgang Büscher; Lutz Plümer. Support Vector machine and duration-aware conditional random field for identification of spatio-temporal activity patterns by combined indoor positioning and heart rate sensors. GeoInformatica 2016, 20, 693 -714.

AMA Style

Jan Behmann, Kathrin Hendriksen, Ute Müller, Wolfgang Büscher, Lutz Plümer. Support Vector machine and duration-aware conditional random field for identification of spatio-temporal activity patterns by combined indoor positioning and heart rate sensors. GeoInformatica. 2016; 20 (4):693-714.

Chicago/Turabian Style

Jan Behmann; Kathrin Hendriksen; Ute Müller; Wolfgang Büscher; Lutz Plümer. 2016. "Support Vector machine and duration-aware conditional random field for identification of spatio-temporal activity patterns by combined indoor positioning and heart rate sensors." GeoInformatica 20, no. 4: 693-714.

Journal article
Published: 03 October 2015 in Machine Vision and Applications
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Hyperspectral imaging sensors have been introduced for measuring the health status of plants. Recently, they also have been used for close-range sensing of plant canopies with a highly complex architecture. However, the complex geometry of plants and their interaction with the illumination setting severely affect the spectral information obtained. Furthermore, the spatial component of analysis results gain in importance as higher plants are represented by multiple plant organs as leaves, stems and seed pods. The combination of hyperspectral images and 3D point clouds is a promising approach to face these problems. We present the generation and application of hyperspectral 3D plant models as a new, interesting application field for computer vision with a variety of challenging tasks. We sum up a geometric calibration method for hyperspectral pushbroom cameras using a reference object for the combination of spectral and spatial information. Furthermore, we show exemplarily new calibration and analysis methods enabled by the hyperspectral 3D models in an experiment with sugar beet plants. An improved normalization, a comparison of image and 3D analysis and the density estimation of infected surface points underline some of the new capabilities gained using this new data type. Based on such hyperspectral 3D models the effects of plant geometry and sensor configuration can be quantified and modeled. In future, reflectance models can be used to remove or weaken the geometry-related effects in hyperspectral images and, therefore, have the potential to improve automated plant phenotyping significantly.

ACS Style

Jan Behmann; Anne-Katrin Mahlein; Stefan Paulus; Jan Dupuis; Heiner Kuhlmann; Erich-Christian Oerke; Lutz Plümer. Generation and application of hyperspectral 3D plant models: methods and challenges. Machine Vision and Applications 2015, 27, 611 -624.

AMA Style

Jan Behmann, Anne-Katrin Mahlein, Stefan Paulus, Jan Dupuis, Heiner Kuhlmann, Erich-Christian Oerke, Lutz Plümer. Generation and application of hyperspectral 3D plant models: methods and challenges. Machine Vision and Applications. 2015; 27 (5):611-624.

Chicago/Turabian Style

Jan Behmann; Anne-Katrin Mahlein; Stefan Paulus; Jan Dupuis; Heiner Kuhlmann; Erich-Christian Oerke; Lutz Plümer. 2015. "Generation and application of hyperspectral 3D plant models: methods and challenges." Machine Vision and Applications 27, no. 5: 611-624.

Conference paper
Published: 19 March 2015 in Transactions on Petri Nets and Other Models of Concurrency XV
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Hyperspectral imaging sensors have been introduced for measuring the health status of plants. Recently, they have been also used for close-range sensing of plant canopies with a more complex architecture. The complex geometry of plants and their interaction with the illumination scenario severely affect the spectral information obtained. The combination of hyperspectral images and 3D point clouds are a promising approach to face this problem. Based on such hyperspectral 3D models the effects of plant geometry and sensor configuration can be quantified an modeled. Reflectance models can be used to remove or weaken the geometry-related effects in hyperspectral images and, therefore, have the potential potential to improve automated phenotyping significantly. We present the generation and application of hyperspectral 3D plant models as a new, interesting application field for computer vision with a variety of challenging tasks. The reliable and accurate generation requires the adaptation of methods designed for man-made scenes. The adaption requires new types of point descriptors and 3D matching technologies. Also the application and analysis of 3D plant models creates new challenges as the light scattering at plant tissue is highly complex and so far not fully described. New approaches for measuring, simulating, and visualizing light fluxes are required for improved sensing and new insights into stress reactions of plants.

ACS Style

Jan Behmann; Anne-Katrin Mahlein; Stefan Paulus; Heiner Kuhlmann; Erich-Christian Oerke; Lutz Plümer. Generation and Application of Hyperspectral 3D Plant Models. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 8928, 117 -130.

AMA Style

Jan Behmann, Anne-Katrin Mahlein, Stefan Paulus, Heiner Kuhlmann, Erich-Christian Oerke, Lutz Plümer. Generation and Application of Hyperspectral 3D Plant Models. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; 8928 ():117-130.

Chicago/Turabian Style

Jan Behmann; Anne-Katrin Mahlein; Stefan Paulus; Heiner Kuhlmann; Erich-Christian Oerke; Lutz Plümer. 2015. "Generation and Application of Hyperspectral 3D Plant Models." Transactions on Petri Nets and Other Models of Concurrency XV 8928, no. : 117-130.

Review
Published: 31 August 2014 in Precision Agriculture
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Effective crop protection requires early and accurate detection of biotic stress. In recent years, remarkable results have been achieved in the early detection of weeds, plant diseases and insect pests in crops. These achievements are related both to the development of non-invasive, high resolution optical sensors and data analysis methods that are able to cope with the resolution, size and complexity of the signals from these sensors. Several methods of machine learning have been utilized for precision agriculture such as support vector machines and neural networks for classification (supervised learning); k-means and self-organizing maps for clustering (unsupervised learning). These methods are able to calculate both linear and non-linear models, require few statistical assumptions and adapt flexibly to a wide range of data characteristics. Successful applications include the early detection of plant diseases based on spectral features and weed detection based on shape descriptors with supervised or unsupervised learning methods. This review gives a short introduction into machine learning, analyses its potential for precision crop protection and provides an overview of instructive examples from different fields of precision agriculture.

ACS Style

Jan Behmann; Anne-Katrin Mahlein; Till Rumpf; Christoph Römer; Lutz Plümer. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agriculture 2014, 16, 239 -260.

AMA Style

Jan Behmann, Anne-Katrin Mahlein, Till Rumpf, Christoph Römer, Lutz Plümer. A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agriculture. 2014; 16 (3):239-260.

Chicago/Turabian Style

Jan Behmann; Anne-Katrin Mahlein; Till Rumpf; Christoph Römer; Lutz Plümer. 2014. "A review of advanced machine learning methods for the detection of biotic stress in precision crop protection." Precision Agriculture 16, no. 3: 239-260.

Journal article
Published: 24 April 2014 in Sensors
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The 3D acquisition of object structures has become a common technique in many fields of work, e.g., industrial quality management, cultural heritage or crime scene documentation. The requirements on the measuring devices are versatile, because spacious scenes have to be imaged with a high level of detail for selected objects. Thus, the used measuring systems are expensive and require an experienced operator. With the rise of low-cost 3D imaging systems, their integration into the digital documentation process is possible. However, common low-cost sensors have the limitation of a trade-off between range and accuracy, providing either a low resolution of single objects or a limited imaging field. Therefore, the use of multiple sensors is desirable. We show the combined use of two low-cost sensors, the Microsoft Kinect and the David laserscanning system, to achieve low-resolved scans of the whole scene and a high level of detail for selected objects, respectively. Afterwards, the high-resolved David objects are automatically assigned to their corresponding Kinect object by the use of surface feature histograms and SVM-classification. The corresponding objects are fitted using an ICP-implementation to produce a multi-resolution map. The applicability is shown for a fictional crime scene and the reconstruction of a ballistic trajectory.

ACS Style

Jan Dupuis; Stefan Paulus; Jan Behmann; Lutz Plümer; Heiner Kuhlmann. A Multi-Resolution Approach for an Automated Fusion of Different Low-Cost 3D Sensors. Sensors 2014, 14, 7563 -7579.

AMA Style

Jan Dupuis, Stefan Paulus, Jan Behmann, Lutz Plümer, Heiner Kuhlmann. A Multi-Resolution Approach for an Automated Fusion of Different Low-Cost 3D Sensors. Sensors. 2014; 14 (4):7563-7579.

Chicago/Turabian Style

Jan Dupuis; Stefan Paulus; Jan Behmann; Lutz Plümer; Heiner Kuhlmann. 2014. "A Multi-Resolution Approach for an Automated Fusion of Different Low-Cost 3D Sensors." Sensors 14, no. 4: 7563-7579.

Journal article
Published: 14 February 2014 in Sensors
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Over the last few years, 3D imaging of plant geometry has become of significant importance for phenotyping and plant breeding. Several sensing techniques, like 3D reconstruction from multiple images and laser scanning, are the methods of choice in different research projects. The use of RGBcameras for 3D reconstruction requires a significant amount of post-processing, whereas in this context, laser scanning needs huge investment costs. The aim of the present study is a comparison between two current 3D imaging low-cost systems and a high precision close-up laser scanner as a reference method. As low-cost systems, the David laser scanning system and the Microsoft Kinect Device were used. The 3D measuring accuracy of both low-cost sensors was estimated based on the deviations of test specimens. Parameters extracted from the volumetric shape of sugar beet taproots, the leaves of sugar beets and the shape of wheat ears were evaluated. These parameters are compared regarding accuracy and correlation to reference measurements. The evaluation scenarios were chosen with respect to recorded plant parameters in current phenotyping projects. In the present study, low-cost 3D imaging devices have been shown to be highly reliable for the demands of plant phenotyping, with the potential to be implemented in automated application procedures, while saving acquisition costs. Our study confirms that a carefully selected low-cost sensor

ACS Style

Stefan Paulus; Jan Behmann; Anne-Katrin Mahlein; Lutz Plümer; Heiner Kuhlmann. Low-Cost 3D Systems: Suitable Tools for Plant Phenotyping. Sensors 2014, 14, 3001 -3018.

AMA Style

Stefan Paulus, Jan Behmann, Anne-Katrin Mahlein, Lutz Plümer, Heiner Kuhlmann. Low-Cost 3D Systems: Suitable Tools for Plant Phenotyping. Sensors. 2014; 14 (2):3001-3018.

Chicago/Turabian Style

Stefan Paulus; Jan Behmann; Anne-Katrin Mahlein; Lutz Plümer; Heiner Kuhlmann. 2014. "Low-Cost 3D Systems: Suitable Tools for Plant Phenotyping." Sensors 14, no. 2: 3001-3018.