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Proximal Sensing of Plant Diseases

Chapter
Part of the Plant Pathology in the 21st Century book series (ICPP, volume 5)

Abstract

Proximal sensing techniques have a large potential in surveying crops for the occurrence of diseases varying in spatial and temporal distribution within crops. Incidence of plant diseases results from crop status, the presence of inoculum, and suitable abiotic environmental factors, and often is heterogeneous in the field. Various technical sensors may be suitable for the detection, identification and quantification of plant diseases on different scales. Thermography, fluorescence and spectral sensors are very promising, but other techniques like electronic nose may be also useful. The full potential of these advanced detector technologies may be exploited only in combination with innovative methods of data processing for the extraction of relevant information. These technologies may support further Integrated Pest Management programs for sustainable crop production.

Keywords

Proximal sensing Disease symptoms Thermography Fluorescence imaging Spectral imaging Image processing 

References

  1. Bauriegel E, Giebel A, Geyer M, Schmidt U, Herppich WB (2011) Early detection of Fusarium infection in wheat using hyperspectral imaging. Comput Electron Agric 75:304–312CrossRefGoogle Scholar
  2. Bellow S, Latouche G, Brown SC, Poutaraud A, Cerovic ZG (2013) Optical detection of downy mildew in grapevine leaves: daily kinetics of autofluorescence upon infection. J Exp Bot 64:333–341PubMedCrossRefPubMedCentralGoogle Scholar
  3. Blackburn GA (2007) Hyperspectral remote sensing of plant pigments. J Exp Bot 58:844–867Google Scholar
  4. Bock CH, Poole GH, Parker PE, Gottwald TR (2010) Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Crit Rev Plant Sci 29:59–107CrossRefGoogle Scholar
  5. Boquete L, Ortega S, Miguel-Jienez JM, Rodriguez- Ascariz JM, Blanco R (2010) Automated detection of breast cancer in thermal infrared images, based on independent component analysis. J Med Syst 36:103–111PubMedCrossRefGoogle Scholar
  6. Bravo C, Moushou D, West J, McCartney A, Ramon H (2003) Early disease detection in wheat fields using spectral reflectance. Biosyst Eng 84:137–145CrossRefGoogle Scholar
  7. Buerling K, Hunsche M, Noga G (2011) Use of blue-green and chlorophyll fluorescence measurements for differentiation between nitrogen deficiency and pathogen infection in wheat. J Plant Physiol 168:1641–1648CrossRefGoogle Scholar
  8. Buerling K, Hunsche M, Noga G (2012) Presymptomatic detection of powdery mildew infection in winter wheat cultivars by laser-induced fluorescence. Appl Spectrosc 66:1411–1419CrossRefGoogle Scholar
  9. Cao X, Luo Y, Zhou Y, Duan X, Cheng D (2013) Detection of powdery mildew in two winter wheat cultivars using canopy hyperspectral reflectance. Crop Prot 45:124–131CrossRefGoogle Scholar
  10. Carrol MW, Glaser JA, Hellmich RL, Hunt TE, Sappington TW, Calvin D et al (2008) Use of spectral vegetation indices derived from airborne hyperspectral imagery for detection of European corn borer infestation in Iowa corn plots. J Econ Entomol 101:1614–1623CrossRefGoogle Scholar
  11. Carter GA, Knapp AK (2001) Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am J Bot 88:677–684PubMedCrossRefGoogle Scholar
  12. Chaerle L, Van der Straeten D (2000) Imaging techniques and the early detection of plant stress. Trends Plant Sci 5:495–501PubMedCrossRefGoogle Scholar
  13. Chaerle L, Leinonen I, Jones HG, Van der Straeten D (2007) Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. J Exp Bot 58:773–784PubMedCrossRefGoogle Scholar
  14. Chaerle L, Lenk S, Leinonen I, Jones HG, Van der Straeten D, Buschmann C (2009) Multi-sensor plant imaging: towards the development of a stress catalogue. Biotechnol J 4:1152–1167PubMedCrossRefGoogle Scholar
  15. Csefalvay L, Di Gaspero G, Matous K, Bellin D, Ruperti B, Olejnickova J (2009) Pre-symptomatic detection of Plasmopara viticola infection in grapevine leaves using chlorophyll fluorescence imaging. Eur J Plant Pathol 125:291–302CrossRefGoogle Scholar
  16. Delalieux S, van Aardt J, Keulemans W, Coppin P (2007) Detection of biotic stress (Venturia inaequalis) in apple trees using hyperspectral data: non-parametric statistical approaches and physiological implications. Eur J Agronomy 27:130–143CrossRefGoogle Scholar
  17. Dudareva N, Negre F, Nagegowda DA, Orlova I (2006) Plant volatiles: recent advances and future perspectives. Crit Rev Plant Sci 25:417–440CrossRefGoogle Scholar
  18. Franke J, Menz G (2007) Multi-temporal wheat disease detection by multi-spectral remote sensing. Precison Agric 8:161–172CrossRefGoogle Scholar
  19. Gebbers R, Adamchuk VI (2010) Precision agriculture and food security. Science 327:828–831PubMedCrossRefGoogle Scholar
  20. Hadjiloucas S, Walker GC, Bowen JW, Zafiropoulos A (2009) Propagation of errors from a null balance terahertz reflectometer to a sample’s relative water content. J Phys: Conf Ser Sens Appl XV(178):012012, 1–5Google Scholar
  21. Hillnhuetter C, Mahlein A-K, Sikora RA, Oerke E-C (2011a) Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields. Field Crop Res 122:70–77CrossRefGoogle Scholar
  22. Hillnhuetter C, Mahlein A-K, Sikora RA, Oerke E-C (2011b) Use of imaging spectroscopy to discriminate symptoms caused by Heterodera schachtii and Rhizoctonia solani on sugar beet. Precision Agric 13:17–32CrossRefGoogle Scholar
  23. Hillnhuetter C, Sikora RA, Oerke E-C, van Dusschoten D (2012) Nuclear magnetic resonance: a tool for imaging belowground damage caused by Heterodera schachtii and Rhizoctonia solani on sugar beet. J Exp Bot 63:319–327CrossRefGoogle Scholar
  24. Huang LS, Zhao JL, Zhang DY, Dong YY, Zhang JC (2012) Identifying and mapping stripe rust in winter wheat using multi-temporal airborne hyperspectral images. Int J Agric Biol 14:697–704Google Scholar
  25. Jacquemoud, S, Ustin SL (2001) Leaf optical properties: a state of the art. In Proceedings 8th international symposium physical measurements & signatures in remote sensing, CNES, Aussois, France, 8–12 Jan 2001, pp 223–232Google Scholar
  26. Jones HG, Schofield P (2008) Thermal and other remote sensing of plant stress. Gen Appl Plant Physiol 34:19–32Google Scholar
  27. Kobayashi T, Kanda E, Kitada K, Ishiguro K, Torigoe Y (2001) Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. Phytopathology 91:316–323PubMedCrossRefGoogle Scholar
  28. Kuckenberg J, Tartachnyk I, Noga G (2009) 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. Precision Agric 10:34–44CrossRefGoogle Scholar
  29. Kushalappa AC, Lui LH, Chen CR, Lee B (2002) Volatile fingerprinting (SPMEGCFID) to detect and discriminate diseases of potato tubers. Plant Dis 86:131–137CrossRefGoogle Scholar
  30. Laothawornkitkul J, Moore JP, Taylor JE, Possell M, Gibson TD, Hewitt CN, Paul ND (2008) Discrimination of plant volatile signatures by an electronic nose: a potential technology for plant pest and disease monitoring. Environ Sci Tech 42:8433–8439CrossRefGoogle Scholar
  31. Lenthe J-H (2006) Erfassung befallsrelevanter Klimafaktoren in Weizenbeständen mit Hilfe digitaler Infrarot-Thermographie. PhD thesis, University of BonnGoogle Scholar
  32. Lenthe J-H, Oerke E-C, Dehne H-W (2007) Digital thermography for monitoring canopy health of wheat. Precision Agric 8:15–26CrossRefGoogle Scholar
  33. Li C, KrewerG, Kays SJ (2009) Blueberry postharvest disease detection using an electronic nose. ASABE paper no. 096783, ASABE annual international meeting, Reno, NV, June 21–June 24, 2009Google Scholar
  34. Lindenthal M (2005) Visualisierung der Krankheitsentwicklung von Falschem Mehltau an Gurken durch Pseudoperonospora cubensis mittels Thermography. PhD thesis, University of BonnGoogle Scholar
  35. Lindenthal M, Steiner U, Dehne HW, Oerke EC (2005) Effect of downy mildew development on transpiration of cucumber leaves visualized by digital thermography. Phytopathology 95:233–240PubMedCrossRefGoogle Scholar
  36. Mahlein AK, Steiner U, Dehne HW, Oerke EC (2010) Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agric 11:413–431CrossRefGoogle Scholar
  37. Mahlein AK, Oerke EC, Steiner U, Dehne HW (2012a) Recent advances in sensing plant diseases for precision crop protection. Eur J Plant Pathol 133:197–203CrossRefGoogle Scholar
  38. Mahlein AK, Steiner U, Hillnhuetter C, Dehne HW, Oerke EC (2012b) Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet diseases. Plant Methods 8:3. doi: 10.1186/1746-4811-8-3 PubMedCrossRefPubMedCentralGoogle Scholar
  39. Mahlein AK, Rumpf T, Welke P, Dehne HW, Pluemer L, Steiner U, Oerke EC (2013) Development of spectral indices for detecting and identifying plant diseases. Remote Sens Environ 128:21–30CrossRefGoogle Scholar
  40. Markom MA, Shakaff AYM, Adom AH, Ahmad MN, Hidayat W, Abdullah AH, Fikri NA (2009) Intelligent electronic nose system for basal stem rot disease detection. Comput Electron Agric 66:140–146CrossRefGoogle Scholar
  41. Montes JM, Melchinger AE, Reif JC (2007) Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci 12:433–436PubMedCrossRefGoogle Scholar
  42. Moshou D, Bravo C, West J, Wahlen S, McCartney A, Ramon H (2004) Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks. Comput Electron Agric 44:173–188CrossRefGoogle Scholar
  43. Naidu RA, Perry EM, Pierce FJ, Mekuria T (2009) The potential of spectral reflectance technique for the detection of grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Comput Electron Agric 66:38–45CrossRefGoogle Scholar
  44. Narvankar DS, Singh CB, Jayas DS, White NDG (2009) Assessment of soft X-ray imaging for detection of fungal infection in wheat. Biosyst Eng 103:49–56CrossRefGoogle Scholar
  45. Nutter F, van Rij N, Eggenberger SK, Holah N (2010) Spatial and temporal dynamics of plant pathogens. In: Oerke EC, Gerhards R, Menz G, Sikora RA (eds) Precision crop protection – the challenge and use of heterogeneity. Springer, Dordrecht, pp 27–50CrossRefGoogle Scholar
  46. Oerke EC (2006) Crop losses to pests. J Agric Sci 144:31–43CrossRefGoogle Scholar
  47. Oerke EC, Steiner U (2010) Potential of digital thermography for disease control. In: Oerke EC, Gerhards R, Menz G, Sikora RA (eds) Precision crop protection – the challenge and use of heterogeneity. Springer, Dordrecht, pp 167–182CrossRefGoogle Scholar
  48. Oerke EC, Steiner U, Dehne HW, Lindenthal M (2006) Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions. J Exp Bot 57:2121–2132PubMedCrossRefGoogle Scholar
  49. Oerke EC, Gerhards R, Menz G, Sikora RA (2010) Preface. In: Oerke EC, Gerhards R, Menz G, Sikora RA (eds) Precision crop protection – the challenge and use of heterogeneity. Springer, DordrechtCrossRefGoogle Scholar
  50. Oerke EC, Fröhling P, Steiner U (2011) Thermographic assessment of scab disease on apple leaves. Precision Agric 12:699–715CrossRefGoogle Scholar
  51. Pearson TC, Wicklow DT (2006) Detection of kernels infected by fungi. Trans ASABE 49(4):1235–1245CrossRefGoogle Scholar
  52. Pietrzykowski E, Stone C, Pinkard E, Mohammed C (2006) Effects of Mycosphaerella leaf disease on the spectral reflectance properties of juvenile Eucalyptus globules foliage. Forest Pathol 36:334–348CrossRefGoogle Scholar
  53. Plaza A, Benediktsson JA, Boardman JW, Brazile J, Bruzzone L, Camps-Valls G et al (2009) Recent advances in techniques for hyperspectral image processing. Remote Sens Environ 113:110–122CrossRefGoogle Scholar
  54. Prithiviraj B, Vikram A, Kushalappa AC, Yaylayam V (2004) Volatile metabolite profiling for the discrimination of onion bulbs infected by Erwinia carotovora ssp. carotovora, Fusarium oxysporum and Botrytis allii. Eur J Plant Physiol 110:371–377Google Scholar
  55. Quin J, Burks TF, Ritenour MA, Bonn WG (2009) Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. J Food Eng 93:183–191CrossRefGoogle Scholar
  56. Reynolds GJ, Windels CE, MacRae IV, Laguette S (2012) Remote sensing for assessing Rhizoctonia crown and root rot severity in sugar beet. Plant Dis 96:497–505CrossRefGoogle Scholar
  57. Rousseau C, Belin E, Bove E, Fabre F, Berruyer R, Guillaumes J et al. (2013) High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis. Plant Methods 9:17 (http://www.plantmethods.com/content/9/1/17)
  58. Rumpf T, Mahlein AK, Steiner U, Oerke EC, Dehne HW, Plümer L (2010) Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Comput Electron Agric 74:91–99CrossRefGoogle Scholar
  59. Sankaran S, Mishraa A, Ehsania R, Davis C (2010) A review of advanced techniques for detecting plant diseases. Comput Electron Agric 72:1–13CrossRefGoogle Scholar
  60. Schmitz A, Kiewnick S, Schlang J, Sikora RA (2004) Use of high resolutional digital thermography to detect Heterodera schachtii infestation in sugar beets. Commun Agric Appl Biol Sci 69:359–363PubMedGoogle Scholar
  61. Scholes JD, Rolfe SA (2009) Chlorophyll fluorescence imaging as tool for understanding the impact of fungal diseases on plant performance: a phenomics perspective. Funct Plant Biol 36:880–892CrossRefGoogle Scholar
  62. Spinelli F, Noferini M, Costa G (2006) Near infrared spectroscopy (NIRs): perspective of fire blight detection in asymptomatic plant material. In: Proceeding of 10th international workshop on fire blight. Acta Horticult 704:87–90Google Scholar
  63. Stafford JV (2000) Implementing precision agriculture in the 21st century. J Agric Eng Res 76:267–275CrossRefGoogle Scholar
  64. Steddom K, Bredehoeft MW, Khan M, Rush CM (2005) Comparison of visual and multispectral radiometric disease evaluations of Cercospora leaf spot of sugar beet. Plant Dis 89:153–158CrossRefGoogle Scholar
  65. Steiner U, Buerling K, Oerke EC (2008) Sensorik für einen präzisierten Pflanzenschutz. Gesunde Pflanzen 60:131–141CrossRefGoogle Scholar
  66. Stenzel I, Steiner U, Dehne HW, Oerke EC (2007) Occurrence of fungal leaf pathogens in sugar beet fields monitored with digital infrared thermography. In: Stafford JV (ed) Precision Agriculture ’07. Papers presented at the 6th European conference on precision agriculture. Wageningen Academic Publishers, pp 529–535Google Scholar
  67. Stoll M, Schultz HR, Baecker G, Berkelmann- Loehnertz B (2008) Early pathogen detection under different water status and the assessment of spray application in vineyards through the use of thermal imagery. Precision Agric 9:407–417CrossRefGoogle Scholar
  68. Thenkabail PS, Smith RB, De Pauw E (2000) Hyperspectral vegetation indices and their relationship with agricultural crop characteristics. Remote Sens Environ 71:158–182CrossRefGoogle Scholar
  69. Vadivambal R, Jayas DS (2011) Applications of thermal imaging in agriculture and food industry – a review. Food Bioprocess Technol 4:186–199CrossRefGoogle Scholar
  70. Von Witzke H, Noleppa S, Schwarz G (2008) Global agricultural market trends and their impacts on European agriculture. Working paper 84, Humboldt University Berlin. http://www.agrar.hu-berlin.de/struktur/institute/wisola/publ/wp. Accessed 28 June 2011
  71. Waggoner PE, Aylor DE (2000) Epidemiology, a science of patterns. Annu Rev Phytopathol 38:1–24CrossRefGoogle Scholar
  72. West JS, Bravo C, Oberti R, Lemaire D, Moshou D, McCartney HA (2003) The potential of optical canopy measurement for targeted control of field crop diseases. Annu Rev Phytopathol 41:593–614PubMedCrossRefGoogle Scholar
  73. West SJ, Bravo C, Oberti R, Moshou D, Ramon H, McCartney HA (2010) Detection of fungal diseases optically and pathogen inoculum by air sampling. In: Oerke EC, Gerhards R, Menz G, Sikora RA (eds) Precision crop protection – the challenge and use of heterogeneity. Springer, Dordrecht, pp 135–150CrossRefGoogle Scholar
  74. Zhang M, Qin Z, Liu X, Ustin S (2003) Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. Appl Earth Observation Geoinf 4:295–310CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2014

Authors and Affiliations

  1. 1.INRES – PhytomedicineUniversity of BonnBonnGermany

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