Hyperspectral and Chlorophyll Fluorescence Imaging for Early Detection of Plant Diseases, with Special Reference to Fusarium spec. Infections on Wheat

Agriculture (Switzerland)

Volumn 4, Issue 1, 2014, Pages 32-57

  Bauriegel, Elke ^a   Herppich, Werner B ^a  

^a LEIBNIZ INSTITUTE FOR AGRICULTURAL ENGINEERING AND BIOECONOMY ATB   (Germany)

Author keywords

Differentiated spatial analysis; Head blight; Imaging; Quality and safety of cereals; Triticum spp

Indexed keywords

EID: 85126730728     PISSN: None     EISSN: 20770472     Source Type: Journal    
DOI: 10.3390/agriculture4010032     Document Type: Review

References (107)

1. Shaner, G.E. Epidemiology of Fusarium Head Blight of Small Grain Cereals in North America. In Fusarium Head Blight of Wheat and Barley; Leonard, K.J., Bushnell, W.R., Eds.; APS Press: St. Paul, MN, USA, 2005; pp. 84–119.
2. Delwiche, S.R. Classification of scab-and other mold-damaged wheat kernels by near-infrared reflectance spectroscopy. Trans. ASAE 2003, 46, 731–738.
3. Ragab, W.; Drusch, S.; Kuhlmann, A.; Beyer, M. Characterizing effects of fermentation and baking on the deoxynivalenol content of rolls. J. Appl. Bot. Food Qual. 2005, 79, 197–201.
4. DLG-Arbeitsgruppe “Mykotoxineˮ. Mykotoxine vermeiden statt “bekämpfenˮ. Sonderdr. DLG-Mitt. 8. Available onlline: http://www.dlg.org/uploads/media/myko.pdf (accessed on 1 March 2010).
5. Gelderblom, W.C.A.; Jaskiewicz, K.; Marasas, W.F.O.; Thiel, P.G.; Horak, R.M.; Vleggaar, R.; Kriek, N.P.J. Fumonisins—Novel mycotoxins with cancer-promoting activity produced by Fusarium-moniliforme. Appl. Environ. Microbiol. 1988, 54, 1806–1811.
6. Kuiper-Goodman, T. Mycotoxins: Risk assessment and legislation. Toxicol. Lett. 1995, 82/83, 853–859.
7. Massart, F.; Saggese, G. Oestrogenic mycotoxin exposures and precocious pubertal development. Int. J. Androl. 2010, 33, 369–376.
8. Ministerium für Infrastruktur und Landwirtschaft des Landes Brandenburg (MIL). Mykotoxine: Vorkommen und Bekämpfungsstrategien in Brandenburg; Ministerium für Ländl. Entwicklung, Umwelt und Verbraucherschutz des Landes Brandenburg: Brandenburg, Germany, 2004; p. 80.
9. Pestka, J.J.; Smolinski, A.T. Deoxynivalenol: Toxicology and potential effects on humans. J. Toxicol. Environ. Health 2005, 8, 39–69.
10. Thate, A.; Schumann, S.; Hanschmann, G.; Urban, Y. Auftreten von Ährenfusariosen und Mykotoxinen in Sachsen und Ergebnisse der Vor-und Nachernteuntersuchungen von Winterweizen 2007 (Appearance of ear fusariosis and mycotoxins in Saxonia and results of pre-and postharvest investigations on winter wheat 2007). Nachrichtenblatt Dtsch. Pflanzenschutzd. 2008, 60, 117.
11. Laguette, S.; Hollingsworth, C.; Motteberg, C.; MacRae, I. Potentials and Limits of Remote Sensing Data for Detection of Fusarium Head Blight on Hard Red Spring Wheat in Minnesota. In Proceedings of the 7th International Conference on Precision Agriculture and Other Precision Resources Management, Hyatt Regency, Minneapolis, MN, USA, 25–28 July 2004; Mulla, D.J., Ed.; Precision Agriculture Center, University of Minnesota: St. Paul, MN, USA, 2004; pp. 1196–1203.
12. Dammer, K.H.; Moeller, B.; Rodemann, B.; Heppner, D. Detection of head blight (Fusarium ssp.) in winter wheat by color and multispectral image analyses. Crop Protect. 2011, 30, 420–428.
13. Von Willert, D.; Matyssek, R.; Herppich, W. Experimental Plant Ecology: Fundamentals and Applications. In Experimentelle Pflanzenökologie: Grundlagen und Anwendungen; Georg Thieme Verlag: Stuttgart, Germany, 1995; p. 344.
14. Maxwell, K.; Johnson, G.N. Chlorophyll fluorescence—A practical guide. J. Exp. Bot. 2000, 51, 659–668.
15. Nedbal, L.; Soukupova, J.; Whitmarsh, J.; Trtilek, M. Postharvest imaging of chlorophyll fluorescence from lemons can be used to predict fruit quality. Photosynthetica 2000, 38, 571–579.
16. Herppich, W.B.; Hetz, E.; Dammer, K.-H.; Langner, H.-R.; Beuche, H.; Hellebrand, H.-J. Application of chlorophyll fluorescence imaging analysis in fungal pathogen detection. In Bornimer Agrartechnische Berichte; Institut für Agrartechnik Bornim e.V.: Potsdam, Germany, 2005; Volume 40, pp. 95–108.
17. Daley, P.F. Chlorophyll fluorescence analysis and imaging in plant stress and disease. Can. J. Plant Pathol. 1995, 17, 167–173.
18. Nedbal, L.; Whitmarsh, J. Chlorophyll Fluorescence Imaging of Leaves and Fruits. In Advances in Photosynthesis and Respiration; Papageorgiou, G.C., Govindjee, X., Eds.; Springer: Dordrecht, The Netherlands, 2004; Volume 19, pp. 389–407.
19. Kuckenberg, J.; Tartachnyk, I.; Noga, G. Temporal and spatial changes of chlorophyll fluorescence as a basis for early and precise detection of leaf rust and powdery mildew infections in wheat leaves. Precis. Agric. 2009, 10, 34–44.
20. Rolfe, S.A.; Scholes, J.D. Chlorophyll fluorescence imaging of plant-pathogen interactions. Protoplasma 2010, 247, 163–175.
21. Bonfig, K.B.; Schreiber, U.; Gabler, A.; Roitsch, T.; Berger, S. Infection with virulent and avirulent P-syringae strains differentially affects photosynthesis and sink metabolism in Arabidopsis leaves. Planta 2006, 225, 1–12.
22. Buerling, K.; Hunsche, M.; Noga, G. Quantum yield of non-regulated energy dissipation in PSII (Y(NO)) for early detection of leaf rust (Puccinia triticina) infection in susceptible and resistant wheat (Triticum aestivum L.) cultivars. Precis. Agric. 2010, 11, 703–716.
23. Chaerle, L.; Hagenbeek, D.; de Bruyne, E.; Valcke, R.; Van Der Straeten, D. Thermal and chlorophyll-fluorescence imaging distinguish plant-pathogen interactions at an early stage. Plant Cell Physiol. 2004, 45, 887–896.
24. Chaerle, L.; Hagenbeek, D.; de Bruyne, E.; Van Der Straeten, D. Chlorophyll fluorescence imaging for disease-resistance screening of sugar beet. Plant Cell Tissue Organ Cult. 2007, 91, 97–106.
25. Prokopova, J.; Spundova, M.; Sedlarova, M.; Husickova, A.; Novotny, R.; Dolezal, K.; Naus, J.; Lebeda, A. Photosynthetic responses of lettuce to downy mildew infection and cytokinin treatment. Plant Physiol. Biochem. 2010, 48, 716–723.
26. Martinez, D.E.; Luquez, V.M.; Bartoli, C.G.; Guiamet, J.J. Persistence of photosynthetic components and photochemical efficiency in ears of water-stressed wheat (Triticum aestivum). Physiol. Plant 2003, 119, 519–525.
27. Wang, X.; Cai, J.; Jiang, D.; Liu, F.; Dai, T.; Cao, W. Pre-anthesis high-temperature acclimation alleviates damage to the flag leaf caused by post-anthesis heat stress in wheat. J. Plant Physiol. 2011, 168, 585–593.
28. Tartachnyk, I.I.; Rademacher, I.; Kuehbauch, W. Distinguishing nitrogen deficiency and fungal infection of winter wheat by laser-induced fluorescence. Precis. Agric. 2006, 7, 281–293.
29. Kuckenberg, J.; Tartachnyk, I.; Schmitz-Eiberger, M.; Noga, G. Early detection of leaf rust and powdery mildew infections on wheat leaves by PAM fluorescence imaging. Precis. Agric. 2007, 8, 515–521.
30. Robert, C.; Bancal, M.O.; Lannou, C.; Ney, B. Quantification of the effects of Septoria tritici blotch on wheat leaf gas exchange with respect to lesion age, leaf number, and leaf nitrogen status. J. Exp. Bot. 2006, 57, 225–234.
31. Buerling, K.; Hunsche, M.; Noga, G. Use of blue-green and chlorophyll fluorescence measurements for differentiation between nitrogen deficiency and pathogen infection in winter wheat. J. Plant Physiol. 2011, 168, 1641–1648.
32. Matous, K.; Benediktyova, Z.; Berger, S.; Roitsch, T.; Nedbal, L. Case study of combinatorial imaging: What protocol and what chlorophyll fluorescence image to use when visualizing infection of Arabidopsis thaliana by Pseudomonas syringae? Photosynth. Res. 2006, 90, 243–253.
33. Buschmann, C.; Bilke, S.; Butterer, A.; Enk, A.; Längle, T.; Wenzel, O.; Lichtenthaler, H.K. Einfluß von Trockenstreß auf die Photosyntheseaktivität verschiedener Ökotypen der Buche (Fagus sylvatica L.)—Unterschiede in der Trockenstressempfindlichkeit. In Berichtsreihe zum Statusseminar der Baden-Württemberg-Projektträgerschaft “Lebensgrundlage Umwelt und ihre Sicherungˮ (BW-PLUS), FZKA-BWPLUS 15, Diskussionskreis; Botanisches Institut II, Universität Karlsruhe: Leopoldshafen, Germany, 1999; pp. 1–12.
34. Kitajima, M.; Butler, W.L. Excitation spectra for photosystem-I and photosystem-II in chloroplasts and spectral characteristics of distribution of quanta between 2 photosystems. Biochem. Biophys. Acta 1975, 408, 297–305.
35. Pinto, L.S.R.C.; Azevedo, J.L.; Pereira, J.O.; Vieira, M.L.C.; Labate, C.A. Symptomless infection of banana and maize by endophytic fungi impairs photosynthetic efficiency. New Phytol. 2000, 147, 609–615.
36. Murchie, E.; Lawson, T. Chlorophyll fluorescence analysis: A guide to good practice and understanding some new applications. J. Exp. Bot. 2013, 64, 3983–3998.
37. Polder, G.; van der Heijden, G.W.A.M.; van Doom, J.; van der Schoor, R.; Baltissen, A.H.M.C. Detection of the Tulip Breaking Virus (TBV) in Tulip Using Spectral and Vision Sensors. In Proceedings of the 7th European Conference on Precision Agriculture (Precision Agriculture’09); Wageningen, NL, 6–8 July 2009; van Henten, E.J., Goense, D., Lokhorst, C., Eds.; Wageningen Academic Publishers: Wageningen, The Netherlands; pp. 219–227.
38. Bauriegel, E.; Giebel, A.; Herppich, W.B. Rapid Fusarium head blight detection on winter wheat ears using chlorophyll fluorescence imaging. J. Appl. Bot. Food Qual. 2010, 83, 196–203.
39. Häuser-Hahn, I. Focus on Fusarium at BCS Research & Development. Presentation at Fusariumsymposium Monheim. Available online: http://www.press.bayercropscience.com/bcsweb/cropprotection.nsf/id/FS05_FoconFusatBCSRD_EN (accessed on 20 January 2010).
40. Bauriegel, E.; Giebel, A.; Herppich, W.B. Hyperspectral and chlorophyll fluorescence imaging to analyse the impact of Fusarium culmorum on the photosynthetic integrity of infected wheat ears. Sensors 2011, 11, 3765–3779.
41. Kang, Z.S.; Buchenauer, H. Cytology and ultrastructure of the infection of wheat spikes by Fusarium culmorum. Mycol. Res. 2000, 104, 1083–1093.
42. Brown, N.A.; Urban, M.; van de Meene, A.M.; Hammond-Kosack, K.E. The infection biology of Fusarium graminearum: Defining the pathways of spikelet to spikelet colonisation in wheat ears. Fungal Biol. 2010, 114, 555–571.
43. Förschler, A.; Portz, C.; Steiner, U.; Schmitz-Eiberger, M.; Noga, A. Minderung von pilzlichem Pathogenbefall im organischen Obstbau durch Zufuhr von Antioxidantien, Schriftenreihe des Lehr-und Forschungsschwerpunktes USL; Landwirtschaftliche Fakultät der Universität Bonn: Bonn, Germany, 2003; Volume 113, pp. 1–41.
44. Bauer, H.; Plattner, K.; Volgger, W. Photosynthesis in Norway spruce seedlings infected by the needle rust Chrysomyxa rhododendri. Tree Physiol. 2000, 20, 211–216.
45. Bauriegel, E.; Giebel, A.; Geyer, M.; Schmidt, U.; Herppich, W. Early detection of Fusarium infection in wheat using hyper-spectral imaging. Comput. Electron. Agric. 2011, 75, 304–312.
46. Bravo, C.; Moshou, D.; Oberti, R.; West, J.; McCartney, A.; Bodria, L.; Ramon, H. Foliar disease detection in the field using optical sensor fusion. Agric. Eng. Int. 2004, 6, 1–14.
47. Polder, G.; Heijden, G.; Doorn, J.; Baltissen, T. Automatic detection of tulip breaking virus (TBV) in tulip fields using machine vision. Biosyst. Eng. 2014, 117, 35–42.
48. Polder, G.; van der Heijden, G.; Jalink, H.; Snel, J. Correcting and matching time sequence images of plant leaves using penalized likelihood warping and Robust Point Matching. Comput. Electron. Agric. 2007, 55, 1–15.
49. Jalink, H.; van der Schoor, R.; Schapendonk, A.H.C.M. A Method and a Device for Making Images of the Quantum Efficiency of the Photosynthetic System with the Purpose of Determining the Quality of Plant Material and a Method and a Device for Measuring, Classifying and Sorting Plant Material. Patent WO2004040274, The Netherlands, 13 May 2004.
50. Herppich, W.B.; Foerster, J.; Zeymer, J.; Geyer, M.; Schlüter, O. Chlorophyll fluorescence image analysis for non-destructive monitoring of physiological changes in fresh and fresh-cut produce. In Environmentally Friendly and Safe Technologies for Quality of Fruit and Vegetables; Nunes, C., Ed.; Universidade do Algarve: Faro, Portugal, 2012; pp. 45–51.
51. Petkova, V.; Denev, I.D.; Cholakov, D.; Porjazov, I. Field screening for heat tolerant common bean cultivars (Phaseolus vulgaris L.) by measuring of chlorophyll fluorescence induction parameters. Sci. Hortic. 2007, 111, 101–106.
52. Firtha, F.; Fekete, A.; Kaszab, T.; Gillay, B.; Nogula-Nagy, M.; Kovacs, Z.; Kantor, D.B. Methods for improving image quality and reducing data load of NIR hyperspectral images. Sensors 2008, 8, 3287–3298.
53. Chen, Y.R.; Chao, K.L.; Kim, M.S. Machine vision technology for agricultural applications. Comput. Electron. Agric. 2002, 36, 173–191.
54. Mahlein, A.K.; Steiner, U.; Dehne, H.W.; Oerke, E.C. Spectral signatures of diseased sugar beet leaves. In Proceedings of the 7th European Conference on Precision Agriculture (Precision Agriculture’09); van Henten, E.J., Goense, D., Lokhorst, C., Eds.; Wageningen, NL, 6–8 July 2009; Wageningen Academic Publishers: Wageningen, The Netherlands; pp. 271–278.
55. Nieuwenhuizen, A.; Hofstee, J.; van de Zande, J.; Meuleman, J.; van Henten, E. Classification of sugar beet and volunteer potato reflection spectra with a neural network and statistical discriminant analysis to select discriminative wavelengths. Comput. Electron. Agric. 2010, 73, 146–153.
56. Devadas, R.; Lamb, D.W.; Simpfendorfer, S.; Backhouse, D. Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precis. Agric. 2009, 10, 459–470.
57. Shahin, M.A.; Symons, S.J. Detection of Fusarium damaged kernels in Canada Western Red Spring wheat using visible/near-infrared hyperspectral imaging and principal component analysis. Comput. Electron. Agric. 2011, 75, 107–112.
58. Williams, P.; Manley, M.; Fox, G.; Geladi, P. Indirect detection of Fusarium verticillioides in maize (Zea mays L.) kernels by near infrared hyperspectral imaging. J. Near Infrared Spectrosc. 2010, 18, 49–58.
59. Peiris, K.; Pumphrey, M.; Dowell, F. NIR absorbance characteristics of deoxynivalenol and of sound and Fusarium-damaged wheat kernels. J. Near Infrared Spectrosc. 2009, 17, 213–221.
60. Makkai, G.; Erostyak, J.; Mesterhazy, A. Identification of Fusarium graminearum infection severity of wheat grains by digitally aided spectroscopy. Opt. Eng. 2013, 52, doi:10.1117/1.OE.52.5.057004.
61. Wiwart, M.; Koczowska, I.; Borusiewicz, A. Estimation of Fusarium head blight of Triticale using digital image analysis of gain. In Computer Analysis of Images and Patterns; Skarbek, W., Ed.; Publisher: Warsaw, Poland, 2001; Volume 2124, pp. 563–569.
62. Dowell, F.E.; Ram, M.S.; Seitz, L.M. Predicting scab, vomitoxin, and ergosterol in single wheat kernels using near-infrared spectroscopy. Cereal Chem. 1999, 76, 573–576.
63. Singh, C.B.; Jayas, D.S.; Paliwal, J.; White, N.D.G. Fungal detection in wheat using near-infrared hyperspectral imaging. Trans. ASABE 2007, 50, 2171–2176.
64. Polder, G.; van der Heijden, G.W.M.; Waalwijk, C.; Young, I.T. Detection of Fusarium in single wheat kernels using spectral imaging. Seed Sci. Technol. 2005, 33, 655–668.
65. Nilsson, H.E. Remote-sensing and image-analysis in plant pathology. Annu. Rev. Phytopathol. 1995, 33, 489–527.
66. Zarco-Tejada, P.J.; Ustin, S.L.; Whiting, M.L. Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agron. J. 2005, 97, 641–653.
67. Peñuelas, J.; Baret, F.; Filella, I. Semiempirical indexes to assess carotenoids chlorophyll-A ratio from leaf spectral reflectance. Photosynthetica 1995, 31, 221–230.
68. Peñuelas, J.; Gamon, J.A.; Fredeen, A.L.; Merino, J.; Field, C.B. Reflectance indexes associated with physiological-changes in nitrogen-limited and water-limited sunflower leaves. Remote Sens. Environ. 1994, 48, 135–146.
69. Lichtenthaler, H.K.; Lang, M.; Sowinska, M.; Heisel, F.; Mieh, J.A. Detection of vegetation stress via a new high resolution fluorescence imaging system. J. Plant Physiol. 1996, 148, 599–612.
70. Lichtenthaler, H.K.; Gitelson, A.; Lang, M. Non-destructive determination of chlorophyll content of leaves of a green and an aurea mutant of tobacco by reflectance measurements. J. Plant Physiol. 1996, 148, 483–493.
71. Baret, F.; Guyot, G.; Major, D. Coupled Fluorescence and Reflectance Measurements to Improve Crop Productivity Evaluation; Lichtenthaler, H.K., Ed.; Kluwer Academic Publisher: Dordrecht, The Netherlands, 1988; pp. 319–324.
72. Muhammed, H.H.; Larsolle, A. Feature vector based analysis of hyperspectral crop reflectance data for discrimination and quantification of fungal disease severity in wheat. Biosyst. Eng. 2003, 86, 125–134.
73. Walther, U.; Flath, K.; Moll, E.; Prochnow, J.; Sachs, E. Methodische Anleitung zur Bewertung der partiellen Resistenz von Sorten bzw. Linien unter Berücksichtigung epidemiologischer Aspekte. Mitteilungen der Biologischen Bundesanstalt für Land und Forstwirtschaft 2000, 374, 9–25.
74. Hillnhuetter, C.; Mahlein, A.K.; Sikora, R.A.; Oerke, E.C. Use of imaging spectroscopy to discriminate symptoms caused by Heterodera schachtii and Rhizoctonia solani on sugar beet. Precis. Agric. 2012, 13, 17–32.
75. Hehmke, M.; Dammer, K.H.; Herppich, W.B.; Hellebrand, J.; Beuche, H.; Rodemann, B. Digital image analysis for detection of head blight (Fusarium spp.) in winter wheat. In Proceedings of the International Symposium. Agricultural Field Trials—Today and Tomorrow, Stuttgart-Hohenheim, Germany, 8–10 October 2007; Bleiholder, H., Ed.; Grauer: Stuttgart, Germany, 2007; pp. 56–61.
76. Miedaner, T.; Schneider, B. Züchtungsstrategien zur Verringerung von Ährenfusariosen und Mykotoxingehalten bei Getreide; Universität Bonn: Bonn, Germany, 2002; pp. 55–67.
77. Castillejo-Gonzalez, L.I.; Lopez-Granados, F.; Garcia-Ferrer, A.; Manuel Pena-Barragan, J.; Jurado-Exposito, M.; Sanchez de la Orden, M.; Gonzalez-Audicana, M. Object-and pixel-based analysis for mapping crops and their agro-environmental associated measures using QuickBird imagery. Comput. Electron. Agric. 2009, 68, 207–215.
78. Yang, F.; Li, J.L.; Gan, X.Y.; Qian, Y.R.; Wu, X.L.; Yang, Q. Assessing nutritional status of Festuca arundinacea by monitoring photosynthetic pigments from hyperspectral data. Comput. Electron. Agric. 2010, 70, 52–59.
79. South, S.; Qi, J.G.; Lusch, D.P. Optimal classification methods for mapping agricultural tillage practices. Remote Sens. Environ. 2004, 91, 90–97.
80. Becker, B.L.; Lusch, D.P.; Qi, J.G. A classification-based assessment of the optimal spectral and spatial resolutions for Great Lakes coastal wetland imagery. Remote Sens. Environ. 2007, 108, 111–120.
81. Richards, J.A.; Jia, X. Supervised Cassification Techniques. In Remote Sensing Digital Image, Analysis; Springer-Verlag GmbH: Heidelberg, Germany, 1999.
82. ENVI.Tutorial: Selected Hyperspectral Methods. Available online: http://www.creaso.com (assessed on 1 July 2010).
83. Yuhas, R.H.; Goetz, F.H.A.; Boardmann, J.W. Discrimination among Semiarid Landscape Endmembers Using the Spectral Angle Mapper (SAM) Algorithm. In Summaries of the Third Annual JPL Airborne Geoscience Workshop; Jet Propulsion Laboratory Publication 92: Pasadena, CA, USA, 1992; pp. 147–149.
84. Kruse, F.A.; Lefkoff, A.B.; Boardman, J.W.; Heidebrecht, K.B.; Shapiro, A.T.; Barloon, P.J.; Goetz, A.F.H. The spectral image-processing system (Sips)—Interactive visualization and analysis of imaging spectrometer data. Remote Sens. Environ. 1993, 44, 145–163.
85. Mahlein, A.K.; Steiner, U.; Hillnhuetter, C.; Dehne, H.W.; Oerke, E.C. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods 2012, 8, doi:10.1186/1746-4811-8-3.
86. Mewes, T.; Franke, J.; Menz, G. Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection. Precis. Agric. 2011, 12, 795–812.
87. Menesatti, P.; Antonucci, F.; Pallottino, F.; Giorgi, S.; Matere, A.; Nocente, F.; Pasquini, M.; D’Egidio, M.G.; Costa, C. Laboratory vs. in-field spectral proximal sensing for early detection of Fusarium head blight infection in durum wheat. Biosyst. Eng. 2013, 114, 289–293.
88. Heitefuss, R.; König, K.; Obst, A.; Reschke, M. Pflanzenkrankheiten und Schädlinge im Ackerbau; DLG-Verlag: München, Germany, 1993; p. 151.
89. Gitelson, A.A.; Gritz, Y.; Merzlyak, M.N. Relationships between leaf chlorophyll content and spectral reflectance and algorithms for non-destructive chlorophyll assessment in higher plant leaves. J. Plant Physiol. 2003, 160, 271–282.
90. Lichtenthaler, H.K. Spektroskopische Eigenschaften von Pflanzen und ihre Nutzung zur Fernerkundung der Vegetation. Fridericiana 1994, 49, 25–45.
91. Nicotra, A.B.; Hofmann, M.; Siebke, K.; Ball, M.C. Spatial patterning of pigmentation in evergreen leaves in response to freezing stress. Plant Cell Environ. 2003, 26, 1893–1904.
92. Polder, G.; van der Heijden, G.W.A.M.; Young, I.T. Spectral image analysis for measuring ripeness of tomatoes. Trans. ASAE 2002, 45, 1155–1161.
93. Risius, H.; Hahn, J.; Korte, H. Die Guten ins Töpfchen—Trennung des Erntegutes nach Qualitätseigenschaften während des Mähdruschs. Neue Landwirtsch. 2008, 11, 52–54.
94. Risius, H.; Hahn, J.; Korte, H. Implementation of Process Analytical Technology (PAT) for the Determination of Product Quality on a Combine Harvester. In Proceedings of the 67 International Conference on Agricultural Engineering LAND.TECHNIK Ag Eng, Hannover, Germany, 6–7 November 2009; pp. 375–380.
95. Forstreuter, M.; Schröder, R.; Herppich, W.B. Chlorophyllfluoreszenzbildanalyse zur raschen Indikation des Befalls von Laub-und Nadelbäumen durch herbivore Insekten (Chlorophyll fluorescence imaging to analyse insect herbivory on broad-leaved and coniferous trees). Comput. Bildanal. Landwirtsch. Workshop 2006, 2006, 26–33.
96. Csefalvay, L.; di Gaspero, G.; Matous, K.; Bellin, D.; Ruperti, B.; Olejnickova, J. Pre-symptomatic detection of Plasmopara viticola infection in grapevine leaves using chlorophyll fluorescence imaging. Eur. J. Plant Pathol. 2009, 125, 291–302.
97. Aldea, M.; Frank, T.D.; DeLucia, E.H. A method for quantitative analysis of spatially variable physiological processes across leaf surfaces. Photosyn. Res. 2006, 90, 161–172.
98. Oerke, E.; Steiner, U.; Dehne, H.; Lindenthal, M. Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions. J. Exp. Bot. 2006, 57, 2121–2132.
99. Bravo, C. Automatic Foliar Disease Detection in Winter Wheat. Ph.D. Thesis, Katholieke Universiteit Leuven, Leuven, Belgium, February 2006; p. 258.
100. Kim, D.; Cho, B.; Lee, Y. Multispectral Imaging Technique for Detection of Fungi-Infected Seed Potatoes Based on Hyperspectral Imagery. In Papers Book; Valencia Conference Centre: Valencia, Spain, 8–12 July 2012; p. C-1183.
101. Davies, A.M.C.; Fearn, T. Back to basics: The principles of principal component analysis. Spectrosc. Eur. 2005, 16, 20–23.
102. Gamon, J.A.; Penuelas, J.; Field, C.B. A narrow-waveband spectral index that tracks diurnal changes in photosynthetic efficiency. Remote Sens. Environ. 1992, 41, 35–44.
103. Weis, M.; Rumpf, T.; Gerhards, R.; Plümer, L. Comparison of Different Classification Algorithms for Weed Detection from Images Based on Shape Parameters. In Bornimer Agrartechnische Berichte; ATB: Potsdam, Germany, 2009; Volume 69, pp. 53–64.
104. Bauer, S.D.; Korc, F.; Foerstner, W. The potential of automatic methods of classification to identify leaf diseases from multispectral images. Precis. Agric. 2011, 12, 361–377.
105. Polder, G.; van der Heijden, G.; van Doorn, J.; Clevers, J.; van der Schoor, R.; Baltissen, A. Detection of the tulip breaking virus (TBV) in tulips using optical sensors. Precis. Agric. 2010, 11, 397–412.
106. Moshou, D.; Bravo, C.; West, J.; Wahlen, T.; McCartney, A.; Ramon, H. Automatic detection of “yellow rust” in wheat using reflectance measurements and neural networks. Comput. Electron. Agric. 2004, 44, 173–188.
107. Pflanzenschutzdienste der Länder. Brandenburg, Sachsen und Thüringen. Available online: www.tll.de/ainfo/pdf/psab0210.pdf (accessed on 9 July 2011).