Skip to main content

Advertisement

SpringerLink
Log in

Recent advances in sensing plant diseases for precision crop protection

  • Published:
European Journal of Plant Pathology Aims and scope Submit manuscript

Abstract

Near-range and remote sensing techniques have demonstrated a high potential in detecting diseases and in monitoring crop stands for sub-areas with infected plants. The occurrence of plant diseases depends on specific environmental and epidemiological factors; diseases, therefore, often have a patchy distribution in the field. This review outlines recent insights in the use of non-invasive optical sensors for the detection, identification and quantification of plant diseases on different scales. Most promising sensor types are thermography, chlorophyll fluorescence and hyperspectral sensors. For the detection and monitoring of plant disease, imaging systems are preferable to non-imaging systems. Differences and key benefits of these techniques are outlined. To utilise the full potential of these highly sophisticated, innovative technologies and high dimensional, complex data for precision crop protection, a multi-disciplinary approach—including plant pathology, engineering, and informatics—is required. Besides precision crop protection, plant phenotyping for resistance breeding or fungicide screening can be optimized by these innovative technologies.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

Abbreviations

GIS:

Geographic Information System

VIS:

Visible Region

NIR:

Near Infrared

SWIR:

Shortwave Infrared

TIR:

Thermal Infrared

SVI:

Spectral Vegetation Index

PCA:

Principle Component Analysis

SAM:

Spectral Angle Mapper

ANN:

Artificial Neural Networks

SVM:

Support Vector Machines

References

  • Bauriegel, E., Giebel, A., Geyer, M., Schmidt, U., & Herppich, W. B. (2011). Early detection of Fusarium infection in wheat using hyper-spectral imaging. Computer and Electronics in Agriculture, 75, 304–312.

    Article  Google Scholar 

  • Blackburn, G. A. (2007). Hyperspectral remote sensing of plant pigments. Journal of Experimental Botany, 58, 844–867.

    Google Scholar 

  • Bock, C. H., Poole, G. H., Parker, P. E., & Gottwald, T. R. (2010). Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Critical Reviews in Plant Science, 29, 59–107.

    Article  Google Scholar 

  • Bongiovanni, R., & Lowenberg-Deboer, J. (2004). Precision agriculture and sustainability. Precision Agriculture, 5, 359–387.

    Article  Google Scholar 

  • Boquete, L., Ortega, S., Miguel-Jienez, J. M., Rodriguez-Ascariz, J. M., & Blanco, R. (2010). Automated detection of breast cancer in thermal infrared images, based on independent component analysis. Journal of Medical Systems, doi:10.1007/s10916-010-9450-y

  • Bravo, C., Moushou, D., West, J., McCartney, A., & Ramon, H. (2003). Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering, 84, 137–145.

    Article  Google Scholar 

  • Bürling, K., Hunsche, M., & Noga, G. (2011). Use of blue-green and chlorophyll fluorescence measurements for differentiation between nitrogen deficiency and pathogen infection in wheat. Journal of Plant Physiology, doi:10.1016/j.jplph.2011.03.016

  • Carrol, M. W., Glaser, J. A., Hellmich, R. L., Hunt, T. E., Sappington, T. W., 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. Journal of Economic Entomology, 101, 1614–1623.

    Article  Google Scholar 

  • Carter, G. A., & Knapp, A. K. (2001). Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. American Journal of Botany, 88, 677–684.

    Article  PubMed  CAS  Google Scholar 

  • Chaerle, L., & Van der Straeten, D. (2000). Imaging techniques and the early detection of plant stress. Trends in Plant Science, 5, 495–501.

    Article  PubMed  CAS  Google Scholar 

  • Chaerle, L., Leinonen, I., Jones, H. G., & Van der Straeten, D. (2007). Monitoring and screening plant populations with combined thermal and chlorophyll fluorescence imaging. Journal of Experimental Botany, 58, 773–784.

    Article  PubMed  CAS  Google Scholar 

  • Chaerle, L., Lenk, S., Leinonen, I., Jones, H. G., Van der Straeten, D., & Buschmann, C. (2009). Multi-sensor plant imaging: towards the development of a stress-catalogue. Biotechnology Journal, 4, 1152–1167.

    Article  PubMed  CAS  Google Scholar 

  • 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. European Journal of Plant Pathology, 125, 291–302.

    Article  CAS  Google Scholar 

  • 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. European Journal of Agronomy, 27, 130–143.

    Article  Google Scholar 

  • Franke, J., & Menz, G. (2007). Multi-temporal wheat disease detection by multi-spectral remote sensing. Precison Agriculture, 8, 161–172.

    Article  Google Scholar 

  • Galvao, L. S., Roberts, D. A., Formaggio, A. R., Numata, I., & Breunig, F. M. (2009). View angle effects on the discrimination of soybean varieties and on the relationships between vegetation indices and yield using off-nadir Hyperion data. Remote Sensing of Environment, 113, 846–856.

    Article  Google Scholar 

  • Gebbers, R., & Adamchuk, V. I. (2010). Precision agriculture and food security. Science, 327, 828–831.

    Article  PubMed  CAS  Google Scholar 

  • Hillnhütter, C., & Mahlein, A.-K. (2008). Neue Ansätze zur frühzeitigen Erkennung und Lokalisierung von Zuckerrübenkrankheiten. Gesunde Pflanzen, 60, 143–149.

    Article  Google Scholar 

  • Hillnhütter, C., Mahlein, A.-K., Sikora, R. A., & Oerke, E.-C. (2011a). Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields. Field Crops Research, 122, 70–77.

    Article  Google Scholar 

  • Hillnhütter, C., Mahlein, A.-K., Sikora, R. A., & Oerke, E.-C. (2011b). Use of imaging spectroscopy to discriminate symptoms caused by Heterodera schachtii and Rhizoctonia solani on sugar beet. Precision Agriculture, doi:10.1007/s11119-011-9237-2

  • Jacquemoud, S., & Ustin, S. L. (2001). Leaf optical properties: A state of the art. In Proceedings 8th International Symposium Physical Measurements & Signatures in Remote Sensing, 8–12 January 2001, CNES, Aussois (France), 223–232.

  • Jones, H. G., & Schofield, P. (2008). Thermal and other remote sensing of plant stress. General and Applied Plant Physiology, 34, 19–32.

    Google Scholar 

  • 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, 316–323.

    Article  PubMed  Google Scholar 

  • 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 Agriculture, 10, 34–44.

    Article  Google Scholar 

  • Lenthe, J.-H. (2006). Erfassung befallsrelevanter Klimafaktoren in Weizenbeständen mit Hilfe digitaler Infrarot-Thermographie. Dissertation, University of Bonn.

  • Lenthe, J.-H., Oerke, E.-C., & Dehne, H.-W. (2007). Digital thermography for monitoring canopy health of wheat. Precision Agriculture, 8, 15–26.

    Article  Google Scholar 

  • Lindenthal, M. (2005). Visualisierung der Krankheitsentwicklung von Falschem Mehltau an Gurken durch Pseudoperonospora cubensis mittels Thermography. Dissertation, University of Bonn.

  • Lindenthal, M., Steiner, U., Dehne, H.-W., & Oerke, E.-C. (2005). Effect of downey mildew development on transpiration of cucumber leaves visualized by digital thermography. Phytopathology, 95, 233–240.

    Article  PubMed  Google 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.

    Article  Google Scholar 

  • Mewes, T., Fanke, J., & Menz, G. (2011). Spectral requirements on airborne hyperspectral remote sensing data for wheat disease detection. Precision Agriculture, doi:10.1007/s111190-011-9222-9

  • Montes, J. M., Melchinger, A. E., & Reif, J. C. (2007). Novel troughput phenotyping platforms in plant genetic studies. Trends in Plant Science, 12, 433–436.

    Article  PubMed  CAS  Google Scholar 

  • 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. Computers and Electronics in Agriculture, 44, 173–188.

    Article  Google Scholar 

  • Naidu, R. A., Perry, E. M., Pierce, F. J., & 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. Computers and Electronic in Agriculture, 66, 38–45.

    Article  Google Scholar 

  • Nutter, F., van Rij, N., Eggenberger, S. K., & Holah, N. (2010). Spatial and temporal dynamics of plant pathogens. In E. C. Oerke, R. Gerhards, G. Menz, & R. A. Sikora (Eds.), Precision crop protection—the challenge and use of heterogeneity (pp. 27–50). Dordrecht, Netherlands: Springer.

    Chapter  Google Scholar 

  • Oerke, E.-C., & Steiner, U. (2010). Potential of digital thermography for disease control. In E. C. Oerke, R. Gerhards, G. Menz, & R. A. Sikora (Eds.), Precision crop protection—the challenge and use of heterogeneity (pp. 167–182). Dordrecht, Netherlands: Springer.

    Chapter  Google Scholar 

  • Oerke, E.-C., Fröhling, P., & Steiner, U. (2011). Thermographic assessment of scab disease on apple leaves. Precision Agriculture, doi:10.1007/s11119-010-9212-3

  • Oerke, E.-C., Steiner, U., Dehne, H.-W., & Lindenthal, M. (2006). Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions. Journal of Experimental Botany, 57, 2121–2132.

    Article  PubMed  CAS  Google Scholar 

  • Oerke, E.-C., & Dehne, H.-W. (2004). Safeguarding production—losses in major crops and the role of crop protection. Crop Protection, 23, 275–285.

    Article  Google Scholar 

  • Oppelt, N., & Mauser, W. (2004). Hyperspectral monitoring of physiological parameters of wheat during a vegetation period using AVIS data. International Journal of Remote Sensing, 25, 145–159.

    Article  Google Scholar 

  • Peñuelas, J., & Filella, I. (1998). Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends in Plant Science, 3, 151–156.

    Article  Google Scholar 

  • Pietrzykowski, E., Stone, C., Pinkard, E., & Mohammed, C. (2006). Effects of Mycosphaerella leaf disease on the spectral reflectance properties of juvenile Eucalyptus globules foliage. Forrest Pathology, 36, 334–348.

    Article  Google Scholar 

  • Plaza, A., Benediktsson, J. A., Boardman, J. W., Brazile, J., Bruzzone, L., Camps-Valls, G., et al. (2009). Recent advances in techniques for hyperspectral image processing. Remote Sensing of Environment, 113, 110–122.

    Article  Google Scholar 

  • Quin, J., Burks, T. F., Ritenour, M. A., & Bonn, W. G. (2009). Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. Journal of Food Engineering, 93, 183–191.

    Article  Google Scholar 

  • Rascher, U., Liebig, M., & Lüttge, U. (2000). Evaluation of instant light-response curves of chlorophyll fluorescence fluorometer on site in the field. Plant, Cell and Environment, 23, 1397–1405.

    Article  CAS  Google Scholar 

  • Reichardt, M., Jürgens, C., Klöble, U., Hüter, J., & Moser, K. (2009). Dissemination of precision farming in Germany: acceptance, adoption, obstacles, knowledge transfer and training activities. Precision Agriculture, 10, 525–545.

    Article  Google Scholar 

  • Rumpf, T., Mahlein, A. K., Steiner, U., Oerke, E. C., Dehne, H. W., & Plümer, L. (2010). Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance. Computers and Electronics in Agriculture, 74, 91–99.

    Article  Google Scholar 

  • Schellberg, J., Hill, M. J., Gerhards, R., Rothmund, M., & Braun, M. (2009). Precision agriculture on grassland: applications, perspectives and constraints. European Journal of Agronomy, 29, 59–71.

    Article  Google Scholar 

  • Schmitz, A., Kiewnick, S., Schlang, J., & Sikora, R. A. (2004). Use of high resolutional digital thermography to detect Heterodera schachtii infestation in sugar beets. Communications in Agriculture and Applied Biological Sciences, 69, 359–363.

    CAS  Google Scholar 

  • Scholes, J. D., & Rolfe, S. A. (2009). Chlorophyll fluorescence imaging as tool for understanding the impact of fungal diseases on plant performance: a phenomics perspective. Functional Plant Biology, 36, 880–892.

    Article  Google Scholar 

  • Scotford, I. M., & Miller, P. C. H. (2005). Applications of spectral reflectance techniques in northern European cereal production: a review. Biosystems Engineering, 90, 235–250.

    Article  Google Scholar 

  • Stafford, J. V. (2000). Implementing precision agriculture in the 21st Century. Journal of Agricultural Engineering Research, 76, 267–275.

    Article  Google Scholar 

  • Steddom, K., Bredehoeft, M. W., Khan, M., & Rush, C. M. (2005). Comparison of visual and multispectral radiometric disease evaluations of Cercospora leaf spot of sugar beet. Plant Disease, 89, 153–158.

    Article  Google Scholar 

  • Steiner, U., Bürling, K., & Oerke, E.-C. (2008). Sensorik für einen präzisierten Pflanzenschutz. Gesunde Pflanzen, 60, 131–141.

    Article  Google Scholar 

  • Stenzel, I., Steiner, U., Dehne, H.-W., & Oerke, E.-C. (2007). Occurrence of fungal leaf pathogens in sugar beet fields monitored with digital infrared thermography. In Stafford J. V. (Ed.), Precision agriculture’07. Papers presented at the 6th European Conference on Precision Agriculture. Wageningen Academic Publishers, pp 529–535.

  • Stoll, M., Schultz, H. R., 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 Agriculture, 9, 407–417.

    Article  Google Scholar 

  • Thenkabail, P. S., Smith, R. B., & De Pauw, E. (2000). Hyperspectral vegetation indices and their relationship with agricultural crop characteristics. Remote Sensing of Environment, 71, 158–182.

    Article  Google Scholar 

  • Thoren, D., & Schmidhalter, U. (2009). Nitrogen status and biomass determination of oilseed rape by laser-induced chlorophyll fluorescence. European Journal of Agronomy, 30, 238–242.

    Article  CAS  Google Scholar 

  • Ustin, S. L., Gitelson, A. A., Jaquemoud, S., Schaepman, M., Asner, G. P., Gamon, J. A., et al. (2009). Retrieval of foliar information about plant pigment systems from high resolution spectroscopy. Remote Sensing of Environment, 113, 67–77.

    Article  Google Scholar 

  • Vadivambal, R., & Jayas, D. S. (2011). Applications of thermal imaging in agriculture and food industry—a review. Food and Bioprocess Technology, 4, 186–199.

    Article  Google Scholar 

  • Von Witzke, H., Noleppa, S., & Schwarz, G. (2008). Global agricultural market trends and their impacts on European agriculture. Working Paper 84, Humboldt Universitity Berlin. http://www.agrar.hu-berlin.de/struktur/institute/wisola/publ/wp (Stand 28.6.2011).

  • Voss, K., Franke, J., Mewes, T., Menz, G., & Kühbauch, W. (2010). Remote sensing for precision crop protection—a matter of scale. In E. C. Oerke, R. Gerhards, G. Menz, & R. A. Sikora (Eds.), Precision crop protection—the challenge and use of heterogeneity (pp. 101–118). Dordrecht, Netherlands: Springer.

    Chapter  Google Scholar 

  • Waggoner, P. E., & Aylor, D. E. (2000). Epidemiology, a science of patterns. Annual Review of Phytopathology, 38, 1–24.

    Article  Google Scholar 

  • West, J. S., Bravo, C., Oberti, R., Lemaire, D., Moshou, D., & McCartney, H. A. (2003). The potential of optical canopy measurement for targeted control of field crop diseases. Annual Review of Phytopathology, 41, 593–614.

    Article  PubMed  CAS  Google Scholar 

  • West, S. J., Bravo, C., Oberti, R., Moshou, D., Ramon, H., & McCartney, H. A. (2010). Detection of fungal diseases optically and pathogen inoculum by air sampling. In E. C. Oerke, R. Gerhards, G. Menz, & R. A. Sikora (Eds.), Precision crop protection—the challenge and use of heterogeneity (pp. 135–150). Dordrecht, Netherlands: Springer.

    Chapter  Google Scholar 

  • 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. Applied Earth Observation and Geoinformation, 4, 295–310.

    Article  Google Scholar 

Download references

Acknowledgement

This review is based on activities of research training group ‘Use of information systems for precision crop protection’ (RTG 722) at the Faculty of Agricultural Sciences, Rheinische Friedrich-Wilhelms-University of Bonn, funded by the German Research Foundation (DFG).

Author information

Authors and Affiliations

  1. Institute of Crop Science and Resource Conservation (INRES)—Phytomedicine, University of Bonn, Nussallee 9, 53115, Bonn, Germany

    Anne-Katrin Mahlein, Erich-Christian Oerke, Ulrike Steiner & Heinz-Wilhelm Dehne

Authors
  1. Anne-Katrin Mahlein
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Erich-Christian Oerke
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ulrike Steiner
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Heinz-Wilhelm Dehne
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Anne-Katrin Mahlein.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Mahlein, AK., Oerke, EC., Steiner, U. et al. Recent advances in sensing plant diseases for precision crop protection. Eur J Plant Pathol 133, 197–209 (2012). https://doi.org/10.1007/s10658-011-9878-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10658-011-9878-z

Keywords

Search

Navigation