Hyperspectral and Thermal Imaging of Oilseed Rape (Brassica napus) Response to Fungal Species of the Genus Alternaria

Baranowski, Piotr; Jędryczka, M.; Mazurek, Walentyna; Babula-Skowrońska, Danuta; Siedliska, Anna; Kaczmarek, Joanna

doi:10.1371/journal.pone.0122913

Cited by 98 publications

(59 citation statements)

References 44 publications

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“…A relatively small number of works have addressed the study of biotic stress, most of them focused on fungal infections. For example, Baranowski et al (2015) reported the analysis of Alternaria infections in oilseed rape; Calderón et al (2013, 2015) analyzed olive trees infected by Verticillium dahliae ; the infection of almond trees with red leaf blotch was studied by López-López et al (2016); and powdery mildew on tomato plants was investigated by Raza et al (2015). However, other techniques on their own, or combined, could also be of great help in precision agriculture and plant phenotyping, as reviewed by Tremblay et al (2012).…”

Section: Discussionmentioning

confidence: 99%

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

et al. 2016

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The negative impact of conventional farming on environment and human health make improvements on farming management mandatory. Imaging techniques are implemented in remote sensing for monitoring crop fields and plant phenotyping programs. The increasingly large size and complexity of the data obtained by these techniques, makes the implementation of powerful mathematical tools necessary in order to identify informative parameters and to apply them in precision agriculture. Multicolor fluorescence imaging is a useful approach for the study of plant defense responses to stress factors at bench scale. However, it has not been fully applied to plant phenotyping. This work evaluates the possible application of multicolor fluorescence imaging in combination with thermography for the particular case of zucchini plants affected by soft-rot, caused by Dickeya dadantii. Several statistical models -based on logistic regression analysis (LRA) and artificial neural networks (ANN)- were obtained for the experimental system zucchini-D. dadantii, which classify new samples as “healthy” or “infected.” The LRA worked best in identifying high dose-infiltrated leaves (in infiltrated and non-infiltrated areas) whereas ANN offered a higher accuracy at identifying low dose-infiltrated areas. To assess the applicability of these results to cucurbits in a more general way, these models were validated for melon infected by the same pathogen, achieving accurate predictions for the infiltrated areas. The values of accuracy achieved are comparable to those found in the literature for classifiers identifying other infections based on data obtained by different techniques. Thus, MCFI in combination with thermography prove useful at providing data at lab scale that can be analyzed by machine learning. This approach could be scaled up to be applied in plant phenotyping.

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Section: Discussionmentioning

confidence: 99%

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

et al. 2016

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“…Among all the imaging techniques employed in this study, the results obtained by thermography revealed significant changes in infected leaves. Differences in temperature, which depended on the Alternaria species, were observed in various stages of the infection [96]. Hellebrand et al [97] studied the possibility of detecting wheat plants infected by powdery mildew (Blumeria graminis) using thermal and NIR imaging under laboratory and field conditions.…”

Section: Thermal Imaging To Monitor Plants Infected With Fungimentioning

confidence: 99%

Picturing pathogen infection in plants

Barón

Pineda

Pérez-Bueno

2016

Zeitschrift Für Naturforschung C

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Several imaging techniques have provided valuable tools to evaluate the impact of biotic stress on host plants. The use of these techniques enables the study of plant-pathogen interactions by analysing the spatial and temporal heterogeneity of foliar metabolism during pathogenesis. In this work we review the use of imaging techniques based on chlorophyll fluorescence, multicolour fluorescence and thermography for the study of virus, bacteria and fungi-infected plants. These studies have revealed the impact of pathogen challenge on photosynthetic performance, secondary metabolism, as well as leaf transpiration as a promising tool for field and greenhouse management of diseases. Images of standard chlorophyll fluorescence (Chl-F) parameters obtained during Chl-F induction kinetics related to photochemical processes and those involved in energy dissipation, could be good stress indicators to monitor pathogenesis. Changes on UV-induced blue (F440) and green fluorescence (F520) measured by multicolour fluorescence imaging in pathogen-challenged plants seem to be related with the up-regulation of the plant secondary metabolism and with an increase in phenolic compounds involved in plant defence, such as scopoletin, chlorogenic or ferulic acids. Thermal imaging visualizes the leaf transpiration map during pathogenesis and emphasizes the key role of stomata on innate plant immunity. Using several imaging techniques in parallel could allow obtaining disease signatures for a specific pathogen. These techniques have also turned out to be very useful for presymptomatic pathogen detection, and powerful non-destructive tools for precision agriculture. Their applicability at lab-scale, in the field by remote sensing, and in high-throughput plant phenotyping, makes them particularly useful. Thermal sensors are widely used in crop fields to detect early changes in leaf transpiration induced by both air-borne and soil-borne pathogens. The limitations of measuring photosynthesis by Chl-F at the canopy level are being solved, while the use of multispectral fluorescence imaging is very challenging due to the type of light excitation that is used.Keywords: biotic stress; chlorophyll fluorescence imaging; multicolor fluorescence imaging; plant pathogens; thermography. Dedication: This work is dedicated to the memory of Professor Peter Böger. He will always bring to my heart (M. B.) the best memories of my postdoc at Lake Constance.

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“…Sacc., and A. brassicicola (Schwein.) Wiltshire infected rapeseed kernels in the NIR region of 1000-2500 nm (Baranowski et al, 2015). The classification models differentiated healthy samples from fungal infected samples with classification accuracy more than 80% at early stage (2 days) and more than 90 at later stages of infection (Baranowski et al,2015).…”

Section: Push-broom Systemmentioning

confidence: 99%

Detection of fungal infection and Ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

Senthilkumar

Jayas

White

et al. 2016

Journal of Stored Products Research

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Fungal infection and mycotoxin contamination in agricultural products are a serious food safety issue. The detection of fungal infection and mycotoxin contamination in food products should be in a rapid way. A Near-Infrared (NIR) hyperspectral imaging system was used to detect fungal infection in 2013 crop year canola, wheat, and barley at different periods after inoculation and different concentration levels of ochratoxin A in wheat and barley. Artificially fungal infected (Fungi: Aspergillus glaucus, Penicillium spp.) kernels of canola, wheat and barley, were subjected to single kernel imaging after 2, 4, 6, 8, and 10 weeks post inoculation in the NIR region from 1000 to 1600 nm at 61 evenly distributed wavelengths at 10 nm intervals.The acquired image data were in the three-dimensional hypercube forms, and these were transformed into two-dimensional data. The two-dimensional data were subjected to principal component analysis to identify significant wavelengths based on the highest principal component factor loadings. Wavelengths 1100, 1130, 1250, and 1300 nm were identified as significant for detection of fungal infection in canola kernels, wavelengths 1280, 1300, and 1350 nm were identified as significant for detection of fungal infection in wheat kernels, and wavelengths 1260, 1310, and 1360 nm were identified as significant for detection of fungal infection in barley kernels. The linear, quadratic and Mahalanobis statistical discriminant classifiers differentiated healthy canola kernels with > 95% and fungal infected canola kernels with > 90% classification accuracy. All the three classifiers discriminated healthy wheat and barley kernels with > 90% and fungal infected wheat and barley kernels with > 80% classification accuracy.ii The wavelengths 1300, 1350, and 1480 nm were identified as significant for detection of ochratoxin A contaminated wheat kernels, and wavelengths 1310, 1360, 1480 nm were identified as significant for detection of ochratoxin A contaminated barley kernels. All the three statistical classifiers differentiated healthy wheat and barley kernels and ochratoxin A contaminated wheat and barley kernels with a classification accuracy of 100%. The classifiers were able to discriminate between different durations of fungal infections in canola, wheat, and barley kernels with classification accuracy of more than 80% at initial periods (2 weeks) of fungal infection and 100% at the later periods of fungal infection. Different concentration levels of ochratoxin A contamination in wheat and barley kernels were discriminated with a classification accuracy of > 98% at ochratoxin A concentration level of ≤ 72 ppb in wheat kernels and ≤ 140 ppb in barley kernels and with 100% classification accuracy at higher concentration levels.iii

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Hyperspectral and Thermal Imaging of Oilseed Rape (Brassica napus) Response to Fungal Species of the Genus Alternaria

Cited by 98 publications

References 44 publications

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

Multicolor Fluorescence Imaging as a Candidate for Disease Detection in Plant Phenotyping

Picturing pathogen infection in plants

Detection of fungal infection and Ochratoxin A contamination in stored wheat using near-infrared hyperspectral imaging

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