Observation of plant–pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements
Stefan Thomas A B , Mirwaes Wahabzada A , Matheus Thomas Kuska A , Uwe Rascher B and Anne-Katrin Mahlein A C
+ Author Affiliations
- Author Affiliations
A INRES-Phytomedizin, University Bonn, Nussallee 9, 53115 Bonn, Germany.
B IBG2: Plant Sciences, Forschungszentrum Jülich GMBH, Wilhelm-Johnen-Straße, 52428 Jülich, Germany.
C Corresponding author. Email: amahlein@uni-bonn.de
Functional Plant Biology 44(1) 23-34 https://doi.org/10.1071/FP16127
Submitted: 1 April 2016 Accepted: 27 September 2016 Published: 20 October 2016
Abstract
Hyperspectral imaging sensors are valuable tools for plant disease detection and plant phenotyping. Reflectance properties are influenced by plant pathogens and resistance responses, but changes of transmission characteristics of plants are less described. In this study we used simultaneously recorded reflectance and transmittance imaging data of resistant and susceptible barley genotypes that were inoculated with Blumeria graminis f. sp. hordei to evaluate the added value of imaging transmission, reflection and absorption for characterisation of disease development. These datasets were statistically analysed using principal component analysis, and compared with visual and molecular disease estimation. Reflection measurement performed significantly better for early detection of powdery mildew infection, colonies could be detected 2 days before symptoms became visible in RGB images. Transmission data could be used to detect powdery mildew 2 days after symptoms becoming visible in reflection based RGB images. Additionally distinct transmission changes occurred at 580–650 nm for pixels containing disease symptoms. It could be shown that the additional information of the transmission data allows for a clearer spatial differentiation and localisation between powdery mildew symptoms and necrotic tissue on the leaf then purely reflectance based data. Thus the information of both measurement approaches are complementary: reflectance based measurements facilitate an early detection, and transmission measurements provide additional information to better understand and quantify the complex spatio-temporal dynamics of plant-pathogen interactions.
Additional keywords: barley, disease detection, hyperspectral imaging, imaging spectroscopy, phenotyping, principal component analysis, powdery mildew.
References
Bauckhage C, Kersting K (2013) Data mining and pattern recognition in agriculture. KI - Künstliche Intelligenz 27, 313–324.| Data mining and pattern recognition in agriculture.Crossref | GoogleScholarGoogle Scholar |
Bauriegel E, Giebel A, Geyer M, Schmidt U, Herpich WB (2011) Early detection of Fusarium infection in wheat using hyper-spectral imaging. Computers and Electronics in Agriculture 75, 304–312.
| Early detection of Fusarium infection in wheat using hyper-spectral imaging.Crossref | GoogleScholarGoogle Scholar |
Behmann J, Mahlein A-K, Rumpf T, Römer C, Plümer L (2015) A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precision Agriculture 16, 239–260.
| A review of advanced machine learning methods for the detection of biotic stress in precision crop protection.Crossref | GoogleScholarGoogle Scholar |
Berdugo CA, Zito R, Paulus S, Mahlein A-K (2014) Fusion of sensor data for the detection and differentiation of plant diseases in cucumber. Plant Pathology 63, 1344–1356.
| Fusion of sensor data for the detection and differentiation of plant diseases in cucumber.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2cXhvFShs73M&md5=db167e78c8c64bc2cec3dec53492bc38CAS |
Bergsträsser S, Fanourakis D, Schmittgen S, Cendrero-Mateo MP, Jansen M, Scharr H, Rascher U (2015) HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging. Plant Methods 11, 1–17.
| HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging.Crossref | GoogleScholarGoogle Scholar | 25649124PubMed |
Bhat R, Miklis M, Schmelzer E, Schulze-Lefert P, Panstruga R (2005) Recruitment and interaction dynamics of plant penetration resistance components in a plasma membrane microdomain. Proceedings of the National Academy of Sciences of the United States of America 102, 3135–3140.
| Recruitment and interaction dynamics of plant penetration resistance components in a plasma membrane microdomain.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2MXitVSkur4%3D&md5=758f95eeff7927d85822c1443e896d69CAS | 15703292PubMed |
Blackburn GA (2007) Hyperspectral remote sensing of plant pigments. Journal of Experimental Botany 58, 855–867.
| Hyperspectral remote sensing of plant pigments.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD2sXislCrtrc%3D&md5=d08278508041096a0f9d440b051fe851CAS | 16990372PubMed |
Bock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Critical Reviews in Plant Sciences 29, 59–107.
| Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging.Crossref | GoogleScholarGoogle Scholar |
Brakke TW (1994) Specular and diffuse components of radiation scattered by leaves. Agricultural and Forest Meteorology 71, 283–295.
| Specular and diffuse components of radiation scattered by leaves.Crossref | GoogleScholarGoogle Scholar |
Carter G, Knapp A (2001) Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. American Journal of Botany 88, 677–684.
| Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3MXjsVWgs7Y%3D&md5=ed6fd7d8c02f3c4d2c9436051e6fe9daCAS | 11302854PubMed |
Christiansen SK, Justesen AF, Giese H (1997) Disparate sequence characteristics of the Erysiphe graminis f. sp. hordei glyceraldehyde-3-phospahte dehydrogenase gene. Current Genetics 31, 525–529.
| Disparate sequence characteristics of the Erysiphe graminis f. sp. hordei glyceraldehyde-3-phospahte dehydrogenase gene.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK2sXkslGgtbc%3D&md5=8940392c3425dd5339fc72eadd792cc5CAS | 9211797PubMed |
Dean R, Van Kan JAL, Pretorius ZA, Hammond-Kosack KE, Pietro AD, Spanu PD, Rudd JJ, Dickman M, Kahmann R, Ellis J, Foster GD (2012) The top 10 fungal pathogens in molecular plant pathology. Molecular Plant Pathology 13, 414–430.
| The top 10 fungal pathogens in molecular plant pathology.Crossref | GoogleScholarGoogle Scholar | 22471698PubMed |
Dhillon IS, Modha DS (2001) Concept decompositions for large sparse text data using clustering. Machine Learning 42, 143–175.
| Concept decompositions for large sparse text data using clustering.Crossref | GoogleScholarGoogle Scholar |
Fiorani F, Rascher U, Jahnke S, Schurr U (2012) Imaging plants dynamics in heterogenic environments. Current Opinion in Biotechnology 23, 227–235.
| Imaging plants dynamics in heterogenic environments.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XltVKnt7Y%3D&md5=3dd2abc95da7992e1285d3f802677039CAS | 22257752PubMed |
Furbank RT, Tester M (2011) Phenomics – technologies to relieve the phenotyping bottleneck. Trends in Plant Science 16, 635–644.
| Phenomics – technologies to relieve the phenotyping bottleneck.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhsFOhu7%2FJ&md5=b61a1e3f534159863d82ad6b44ce19fdCAS | 22074787PubMed |
Hack H, Bleiholder H, Buhr L, Meier U, Schnock-Fricke U, Weber E, Witzenberger A (1992) Einheitliche Codierung der phänologischen Entwicklungsstadien mono- und dikotyler Pflanzen – erweiterte BBCH- Skala. Allgemeines Nachrichtenblatt des deutschen Pflanzenschutzdienstes 44, 265–270.
Hillnhütter C, Mahlein A-K, Sikora RA, Oerke E-C (2012) Use of imaging spectroscopy to discriminate symptoms caused by Heterodera schachtii and Rhizoctonia solani on sugar beet. Precision Agriculture 13, 17–32.
| Use of imaging spectroscopy to discriminate symptoms caused by Heterodera schachtii and Rhizoctonia solani on sugar beet.Crossref | GoogleScholarGoogle Scholar |
Hinze K, Thompson R, Ritter E, Salamini F, Schulze-Lefert P (1991) Restriction fragment length polymorphism-mediated targeting of the ml-o resistance locus in barley (Hordeum vulgare). Proceedings of the National Academy of Sciences of the United States of America 88, 3691–3695.
| Restriction fragment length polymorphism-mediated targeting of the ml-o resistance locus in barley (Hordeum vulgare).Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK3MXktVahtbc%3D&md5=e6892c44a3c87322a8885c4368875fd2CAS | 11607179PubMed |
Hückelhoven R, Panstruga R (2011) Cell biology of the plant-powdery mildew interaction. Current Opinion in Plant Biology 14, 738–746.
| Cell biology of the plant-powdery mildew interaction.Crossref | GoogleScholarGoogle Scholar | 21924669PubMed |
Jørgensen JH (1977) Spectrum of resistance conferred by ml-o powdery mildew resistance genes in barley. Euphytica 26, 55–62.
| Spectrum of resistance conferred by ml-o powdery mildew resistance genes in barley.Crossref | GoogleScholarGoogle Scholar |
Jørgensen JH, Wolfe M (1994) Genetics of powdery mildew resistance in barley. Critical Reviews in Plant Sciences 13, 97–119.
| Genetics of powdery mildew resistance in barley.Crossref | GoogleScholarGoogle Scholar |
Kersting K, Wahabzada M, Römer C, Thurau C, Ballvora A, Rascher U, Léon J, Bauckhage C, Plümer L (2012) Simplex distributions for embedding data matrices over time. In ‘Proceedings of the 2012 SIAM international conference on data mining, Anaheim’. (Eds J Ghosh, H Liu, I Davidson, C Domeniconi, C Kamath) pp. 295–306. (American Statistical Association: Alexandria, VA, USA)
Kim DM, Zhang H, Zhou H, Du T, Wu Q, Mockler TC, Berezin MY (2015) Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis. Scientific Reports 5, 15919
| Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXhslOntrzJ&md5=a12a275a081fb2cba0158f9ec245b596CAS | 26531782PubMed |
Knipling EB (1970) Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation. Remote Sensing of Environment 1, 155–159.
| Physical and physiological basis for the reflectance of visible and near-infrared radiation from vegetation.Crossref | GoogleScholarGoogle Scholar |
Kuska M, Wahabzada M, Leucker M, Dehne H-W, Kersting K, Oerke EC, Steiner U, Mahlein A-K (2015) Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions. Plant Methods 11, 28
| Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions.Crossref | GoogleScholarGoogle Scholar | 25937826PubMed |
Leucker M, Mahlein A-K, Steiner U, Oerke E-C (2016a) Improvement of lesion phenotyping in Cercospora beticola-sugar beet interaction by hyperspectral imaging. Phytopathology 1, 1–30.
| Improvement of lesion phenotyping in Cercospora beticola-sugar beet interaction by hyperspectral imaging.Crossref | GoogleScholarGoogle Scholar |
Leucker M, Wahabzada M, Kersting K, Peter M, Beyer W, Steiner U, Mahlein A-K, Oerke E-C (2016b) Hyperspectral imaging reveals the effects of sugar beet QTLs on Cercospora leaf spot resistance. Functional Plant Biology 44, 1–9.
| Hyperspectral imaging reveals the effects of sugar beet QTLs on Cercospora leaf spot resistance.Crossref | GoogleScholarGoogle Scholar |
Mahlein A-K (2016) Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease 100, 241–251.
| Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping.Crossref | GoogleScholarGoogle Scholar |
Mahlein A-K, Steiner U, Dehne H-W, Oerke E-C (2010) Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agriculture 11, 413–431.
| Spectral signatures of sugar beet leaves for the detection and differentiation of diseases.Crossref | GoogleScholarGoogle Scholar |
Mahlein A-K, Steiner U, Hillnhütter C, Dehne H-W, Oerke E-C (2012) Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods 8, 3
| Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases.Crossref | GoogleScholarGoogle Scholar | 22273513PubMed |
Mahlein A-K, Rumpf T, Welke P, Dehne H-W, Plümer L, Steiner U, Oerke E-C (2013) Development of spectral indices for detecting and identifying plant diseases. Remote Sensing of Environment 128, 21–30.
| Development of spectral indices for detecting and identifying plant diseases.Crossref | GoogleScholarGoogle Scholar |
Hamid Muhammed H, Larsolle A (2003) Feature vector based analysis of hyperspectral crop reflectance data for discrimination of fungal disease severity in wheat. Biosystems Engineering 86, 125–134.
| Feature vector based analysis of hyperspectral crop reflectance data for discrimination of fungal disease severity in wheat.Crossref | GoogleScholarGoogle Scholar |
Rascher U, Nichol CJ, Small C, Hendricks L (2007) Monitoring spatio-temporal dynamics of photosynthesis with a portable hyperspectral imaging system. Photogrammetric Engineering and Remote Sensing 73, 45–56.
| Monitoring spatio-temporal dynamics of photosynthesis with a portable hyperspectral imaging system.Crossref | GoogleScholarGoogle Scholar |
Rascher U, Blossfeld S, Fiorani F, Jahnke S, Jansen M, Kuhn AJ, Matsubara S, Märtin LLA, Merchant A, Metzner R, Müller-Linof M, Nagel KA, Pieruschka R, Pinto F, Schreiber CM, Temperton VM, Thorpe MR, Dusschoten D, Volkenburgh E, Windt CW, Schurr U (2011) Non-invasive approaches for phenotyping of enhanced performance traits in bean. Functional Plant Biology 38, 968–983.
| Non-invasive approaches for phenotyping of enhanced performance traits in bean.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhsFKktLvP&md5=c2f321d5c90b65e2b2b8f10d5cfb6f23CAS |
Römer C, Wahabzada M, Ballvora C, Pinto F, Rossini M, Panigada C, Behmann J, Léon J, Thurau C, Bauckhage C, Kersting K, Rascher U, Plümer L (2012) Early drought stress detection in cereals: simplex volume maximisation for hyperspectral image analysis. Functional Plant Biology 39, 878–890.
Sankaran S, Mishra A, Ehsani R, Davis C (2010) A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture 72, 1–13.
| A review of advanced techniques for detecting plant diseases.Crossref | GoogleScholarGoogle Scholar |
Sankaran S, Eshani R, Inch SA, Ploetz RC (2012) Evaluation of visible-near infrared reflectance spectra of avocado leaves as non-destructive sensing tool for detection of laurel wilt. Plant Disease 96, 1683–1689.
| Evaluation of visible-near infrared reflectance spectra of avocado leaves as non-destructive sensing tool for detection of laurel wilt.Crossref | GoogleScholarGoogle Scholar |
Savitzky A, Golay M (1964) Smoothing and differentiation of data by simplified least squares procedures. Analytical Chemistry 36, 1627–1639.
| Smoothing and differentiation of data by simplified least squares procedures.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaF2cXksVCjur8%3D&md5=dd35a090eeecd6851bf331ba07bb7a21CAS |
Singh A, Ganapathysubramanian B, Singh AK, Sarkar S (2016) Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science 21, 110–124.
| Machine learning for high-throughput stress phenotyping in plants.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC2MXhvVykurvJ&md5=cec62cd601630162f444dc509dd574d5CAS | 26651918PubMed |
Suzuki Y, Hiroshi O, Takashi K (2008) Image segmentation between crop and weed using hyperspectral imaging for weed detection in soybean field. Environment Control in Biology 46, 163–173.
| Image segmentation between crop and weed using hyperspectral imaging for weed detection in soybean field.Crossref | GoogleScholarGoogle Scholar |
Thurau C, Kersting K, Wahabzada M, Bauckhage C (2012) Descriptive matrix factorization for sustainability adopting the principle of opposites. Data Mining and Knowledge Discovery 24, 325–354.
| Descriptive matrix factorization for sustainability adopting the principle of opposites.Crossref | GoogleScholarGoogle Scholar |
Vogelmann TC (1989) Penetration of light into plants. Photochemistry and Photobiology 50, 895–902.
| Penetration of light into plants.Crossref | GoogleScholarGoogle Scholar |
Vogelmann TC (1993) Plant tissue optics. Plant Molecular Biology 44, 231–251.
Vogelmann TC, Gorton HL (2014) Leaf: light capture in the photosynthetic organ. In ‘The structural basis of biological energy generation’. (Ed. MF Hohmann-Marriott) pp. 363–377. (Springer: Dordrecht, The Netherlands)
Wahabzada M, Mahlein A-K, Bauckhage C, Steiner U, Oerke EC, Kersting K (2015) Metro maps of plant disease dynamics – automated mining of differences using hyperspectral images. PLoS One 10, e0116902
| Metro maps of plant disease dynamics – automated mining of differences using hyperspectral images.Crossref | GoogleScholarGoogle Scholar | 25621489PubMed |
Wahabzada M, Mahlein A-K, Bauckhage C, Steiner U, Oerke EC, Kersting K (2016) Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants. Scientific Reports 6, 22482
| Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC28XktVaitbo%3D&md5=e3d6461cd1aec883a05fd0516460925dCAS | 26957018PubMed |
Wei F, Wing RA, Wise RP (2002) Genome dynamics and evolution of the Mla (powdery mildew) resistance locus in barley. The Plant Cell 14, 1903–1917.
| Genome dynamics and evolution of the Mla (powdery mildew) resistance locus in barley.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD38XmslCgsrw%3D&md5=d43da55ca34f2e3d5767341f0c4b70d7CAS | 12172030PubMed |
West J, Bravo C, Oberti R, Lemaire D, Moshou D, McCartney HA (2003) The potential of optical canopy measurement for targeted control of field crop diseases. Phytopathology 41, 593–614.
| The potential of optical canopy measurement for targeted control of field crop diseases.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXptFWltr8%3D&md5=502db084ef98d4d8c7db8d8c24ba6e58CAS |
Wold S, Esbensen K, Geladi P (1987) Principal component analysis. Chemometrics and Intelligent Laboratory Systems 2, 37–52.
| Principal component analysis.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaL1cXjtVyjsw%3D%3D&md5=c06ba5925c4b45390ecd6d848205f99bCAS |
Woolley J (1971) Reflectance and transmittance of light by leaves. Plant Physiology 47, 656–662.
| Reflectance and transmittance of light by leaves.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC3cngvFylsA%3D%3D&md5=dda7963fb53efb8d991277681d7c8035CAS | 16657679PubMed |
Yuan L, Zhang J, Shi Y, Nie C, Wie L, Wang J (2014) Damage mapping of powdery mildew in winter wheat with high-resolution satellite image. Remote Sensing 6, 3611–3623.
| Damage mapping of powdery mildew in winter wheat with high-resolution satellite image.Crossref | GoogleScholarGoogle Scholar |
Zhang M, Liu X, O’Neill M (2002) Spectral discrimination of Phytophthora infestans infection on tomatoes based on principal component and cluster analyses. International Journal of Remote Sensing 23, 1095–1107.
| Spectral discrimination of Phytophthora infestans infection on tomatoes based on principal component and cluster analyses.Crossref | GoogleScholarGoogle Scholar |
