Detection of Fungal Infection of Plants by Laser-induced Fluorescence: An Attempt to Use Remote Sensing

Lüdeker, W.; Dahn, H.-G.; Günther, Kurt P.

doi:10.1016/s0176-1617(96)80078-2

Cited by 35 publications

(18 citation statements)

References 9 publications

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“…Fungi infections increased non-photochemical quenching (NPQ) of ChlF and it was possible to locate the infected area before the appearance of visible symptoms (Scholes and Rolfe 1996;Chou et al 2000). It has been noted also that some fungi can themselves emit BGF (Lüdeker et al 1996). Furthermore, phenolic compounds are usually synthesized in response to infection in order to prevent infestation spreading to neighbor cells (Benhamou and Bélanger 1998).…”

Section: Field Applicability Considerationsmentioning

confidence: 99%

Discrimination of corn, grasses and dicot weeds by their UV-induced fluorescence spectral signature

et al. 2009

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Real-time spot spraying of weed patches requires the development of sensors for the automatic detection of weeds within a crop. In this context, the potential of UVinduced fluorescence of green plants for corn-weed discrimination was evaluated. A total of 1 440 spectral signatures of fluorescence were recorded in a greenhouse from three plant groups (four corn hybrids, four dicotyledonous weed species and four monocotyledonous weed species) grown in a growth chamber. With multi-variate analysis, the full information contained in each spectrum was first reduced to the scores calculated from five principal components. Then, a linear discriminant analysis was applied on these scores to classify spectra on a species/hybrids basis and, subsequently, the resulting classes were aggregated according to the three plant groups. This two-step process minimized the error generated by heterogeneous groups such as dicotyledonous weeds. The output of this classification shows the significant potential of UV-induced fluorescence for plant group discrimination as the success rate reached 91.8%. No error was observed between corn and dicot weeds and most of the errors between corn and grasses came from confusion between the hybrid Pioneer 39Y85 and Setaria glauca L. (Beauv.). Analysis also determined that the position of the fluorescence sensor on the leaf and the plant age had negligible effects on the efficiency of fluorescence to discriminate plant groups. The factors to consider for transferring the results about UV-induced fluoro-sensing from laboratory to the field are discussed.

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Section: Field Applicability Considerationsmentioning

confidence: 99%

Discrimination of corn, grasses and dicot weeds by their UV-induced fluorescence spectral signature

et al. 2009

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“…As reported by Lüdeker et al [4], fungal infection led to a stronger increase in the green (F520) than in the blue fluorescence (F440), that can be either caused by the fungi or due to accumulation of intercellular substances. According to our results, the inoculum of Blumeria graminis f.sp.…”

Section: Discussionmentioning

confidence: 65%

“…In breeding programs new and promising lines are classified and rated to their susceptibility to diseases after a visual monitoring done by trained specialists. However, precise classifications on the susceptibility to pathogens are often difficult, amongst others due to interpretations of the operators [4]. …”

Section: Introductionmentioning

confidence: 99%

Proximal Sensing of Plant-Pathogen Interactions in Spring Barley with Three Fluorescence Techniques

Leufen

Noga

Hunsche

2014

Sensors

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In the last years fluorescence spectroscopy has come to be viewed as an essential approach in key research fields of applied plant sciences. However, the quantity and particularly the quality of information produced by different equipment might vary considerably. In this study we investigate the potential of three optical devices for the proximal sensing of plant-pathogen interactions in four genotypes of spring barley. For this purpose, the fluorescence lifetime, the image-resolved multispectral fluorescence and selected indices of a portable multiparametric fluorescence device were recorded at 3, 6, and 9 days after inoculation (dai) from healthy leaves as well as from leaves inoculated with powdery mildew (Blumeria graminis) or leaf rust (Puccinia hordei). Genotype-specific responses to pathogen infections were revealed already at 3 dai by higher fluorescence mean lifetimes in the spectral range from 410 to 560 nm in the less susceptible varieties. Noticeable pathogen-induced modifications were also revealed by the ‘Blue-to-Far-Red Fluorescence Ratio’ and the ‘Simple Fluorescence Ratio’. Particularly in the susceptible varieties the differences became more evident in the time-course of the experiment i.e., following the pathogen development. The relevance of the blue and green fluorescence to exploit the plant-pathogen interaction was demonstrated by the multispectral fluorescence imaging system. As shown, mildewed leaves were characterized by exceptionally high blue fluorescence, contrasting the values observed in rust inoculated leaves. Further, we confirm that the intensity of green fluorescence depends on the pathogen infection and the stage of disease development; this information might allow a differentiation of both diseases. Moreover, our results demonstrate that the detection area might influence the quality of the information, although it had a minor impact only in the current study. Finally, we highlight the relevance of different excitation-emission channels to better understand and evaluate plant-physiological alterations due to pathogen infections.

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“…Red fluorescence is only emitted by chlorophyll a which is an indicator for photosynthetic efficiency. BRR_FRF from Multiplex is blue to red emission ratio, it indicates water stress [3] and [4] or the stress from pathogens [5]. FLAV is the logarithm of FRF_R (far red fluorescence under red excitation) and FRF_UV.…”

Section: Methodsmentioning

confidence: 99%

Early Detection of Fungal Diseases in Winter Wheat by Multi-optical Sensors

et al. 2014

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Biotic factors such as pests and pathogens cause a substantial damage to wheat crops which leads to reduction in yield in a range from 10% to 50%. Application of fungicides and pesticides on one hand protects the crop but it also increases the crop production cost. Pathogens affect photosynthesis, respiration, translocation of water and nutrients of the crop and mostly the visual symptoms are detected too late to protect the crop. The objective of this study was to detect the plant fungal diseases by non-invasive sensor technologies and to determine the early outbreak of the disease. The experiment was conducted in the greenhouse where the two wheat cultivars namely; Monopol and Kalahari were infected with three fungal diseases viz. Fusarium culmorum, Septoria tritici and Blumeria graminis f.sp. tritic. Throughout the experiment four spectral sensors were used namely, Isaria, Handyspec, Multiplex and Infrared thermal camera. The results showed that as early as 2 days after inoculation (DAI), an increase in the average canopy temperature and maximum temperature difference within the canopy (MTD) was observed. Similarly, the REIP calculated from Handyspec showed significant difference between the infested and the control plants before the visual symptoms appeared. Multiplex measured chlorophyll content which is related to the photosynthesis process allowed to detect the early symptoms in contrast to the Isaria which, does not show a significant difference between control and infected plants.

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Detection of Fungal Infection of Plants by Laser-induced Fluorescence: An Attempt to Use Remote Sensing

Cited by 35 publications

References 9 publications

Discrimination of corn, grasses and dicot weeds by their UV-induced fluorescence spectral signature

Discrimination of corn, grasses and dicot weeds by their UV-induced fluorescence spectral signature

Proximal Sensing of Plant-Pathogen Interactions in Spring Barley with Three Fluorescence Techniques

Early Detection of Fungal Diseases in Winter Wheat by Multi-optical Sensors

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