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Most studies of Fusarium head blight (FHB) focused on wheat infection at anthesis. Less is known about infections at later stages. In this study, the effect of infection timing on the development of FHB and the distribution of fungal biomass and deoxynivalenol (DON) along wheat spikes was investigated. Under greenhouse conditions, two wheat varieties were point-inoculated with Fusarium graminearum starting from anthesis until 25 days after anthesis. The fungus and fungal DNA were isolated from the centers and the bases of all the spikes but not from the tips for all inoculation times and both varieties. In each variety, the amount of fungal DNA and the content of DON and deoxynivalenol-3-glucoside (DON-3-G) were higher in the center than in the base for all inoculation times. A positive correlation was found between the content of fungal DNA and DON in the centers as well as the bases of both varieties. This study showed that F. graminearum grows downward within infected wheat spikes and that the accumulation of DON is largely confined to the colonized tissue. Moreover, F. graminearum was able to infect wheat kernels and cause contamination with mycotoxins even when inoculated 25 days after anthesis.
Elias Alisaac; Anna Rathgeb; Petr Karlovsky; Anne-Katrin Mahlein. Fusarium Head Blight: Effect of Infection Timing on Spread of Fusarium graminearum and Spatial Distribution of Deoxynivalenol within Wheat Spikes. Microorganisms 2020, 9, 79 .
AMA StyleElias Alisaac, Anna Rathgeb, Petr Karlovsky, Anne-Katrin Mahlein. Fusarium Head Blight: Effect of Infection Timing on Spread of Fusarium graminearum and Spatial Distribution of Deoxynivalenol within Wheat Spikes. Microorganisms. 2020; 9 (1):79.
Chicago/Turabian StyleElias Alisaac; Anna Rathgeb; Petr Karlovsky; Anne-Katrin Mahlein. 2020. "Fusarium Head Blight: Effect of Infection Timing on Spread of Fusarium graminearum and Spatial Distribution of Deoxynivalenol within Wheat Spikes." Microorganisms 9, no. 1: 79.
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.
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 StyleElias 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 StyleElias 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.
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.
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 StyleAnne-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 StyleAnne-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.
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.
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 StyleElias 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 StyleElias 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.
The detection and identification of plant diseases is crucial for an appropriate and targeted application of plant protection measures in crop production. Recently, intensive research has been conducted to develop innovative and technology-based optical methods for plant disease detection. In contrast to common visual rating and detection methods, optical sensors are able to measure pathogen-induced changes in the plant physiology non-invasively and objectively. Several studies showed that especially hyperspectral sensors are valuable tools for disease detection, identification and quantification on different scales from the tissue to the canopy level. This review describes the basic principles of hyperspectral measurements and different types of available hyperspectral sensors. Possible applications of hyperspectral sensors on different scales for disease detection and plant protection are discussed and evaluated. The advantages and disadvantages on each particular scale, as well as the impact of external factors, such as: light, wind, viewing angle, for measurements in laboratories, greenhouses and fields, are critically assessed in order to support researchers and agriculture technicians. Additionally, a comprehensive literature review about the use of hyperspectral sensors on these different scales for plant disease detection reflects the possibilities of non-invasive measurement systems. This highlights advantages of hyperspectral sensors when investigating plant–pathogen interactions through multiple examples. By some approaches, detection before visible symptoms appear is feasible. The potential of hyperspectral sensors as a tool for disease identification and quantification, based on disease characteristic changes in the plants spectral signature, is discussed as well. The review is concluded with an overview on different data analysis methods, which are required to extract key information from gathered hyperspectral datasets.
Stefan Thomas; Matheus Thomas Kuska; David Bohnenkamp; Anna Brugger; Elias Alisaac; Mirwaes Wahabzada; Jan Behmann; Anne-Katrin Mahlein. Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective. Journal of Plant Diseases and Protection 2017, 125, 5 -20.
AMA StyleStefan Thomas, Matheus Thomas Kuska, David Bohnenkamp, Anna Brugger, Elias Alisaac, Mirwaes Wahabzada, Jan Behmann, Anne-Katrin Mahlein. Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective. Journal of Plant Diseases and Protection. 2017; 125 (1):5-20.
Chicago/Turabian StyleStefan Thomas; Matheus Thomas Kuska; David Bohnenkamp; Anna Brugger; Elias Alisaac; Mirwaes Wahabzada; Jan Behmann; Anne-Katrin Mahlein. 2017. "Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective." Journal of Plant Diseases and Protection 125, no. 1: 5-20.
The detection and identification of plant diseases is a fundamental task in sustainable crop production. An accurate estimate of disease incidence, disease severity and negative effects on yield quality and quantity is important for precision crop production, horticulture, plant breeding or fungicide screening as well as in basic and applied plant research. Particularly hyperspectral imaging of diseased plants offers insight into processes during pathogenesis. By hyperspectral imaging and subsequent data analysis routines, it was possible to realize an early detection, identification and quantification of different relevant plant diseases. Depending on the measuring scale, even subtle processes of defence and resistance mechanism of plants could be evaluated. Within this scope, recent results from studies in barley, wheat and sugar beet and their relevant foliar diseases will be presented.
A.K. Mahlein; M. T. Kuska; S. Thomas; D. Bohnenkamp; Elias Alisaac; J. Behmann; M. Wahabzada; K. Kersting. Plant disease detection by hyperspectral imaging: from the lab to the field. Advances in Animal Biosciences 2017, 8, 238 -243.
AMA StyleA.K. Mahlein, M. T. Kuska, S. Thomas, D. Bohnenkamp, Elias Alisaac, J. Behmann, M. Wahabzada, K. Kersting. Plant disease detection by hyperspectral imaging: from the lab to the field. Advances in Animal Biosciences. 2017; 8 (2):238-243.
Chicago/Turabian StyleA.K. Mahlein; M. T. Kuska; S. Thomas; D. Bohnenkamp; Elias Alisaac; J. Behmann; M. Wahabzada; K. Kersting. 2017. "Plant disease detection by hyperspectral imaging: from the lab to the field." Advances in Animal Biosciences 8, no. 2: 238-243.