Skip to main content Accessibility help
×
Home

Plant disease detection by hyperspectral imaging: from the lab to the field

  • A-K. Mahlein (a1), M. T. Kuska (a1), S. Thomas (a1), D. Bohnenkamp (a1), E. Alisaac (a1), J. Behmann (a1), M. Wahabzada (a1) and K. Kersting (a2)...

Abstract

The detection and identification of plant diseases is a fundamental task in sustainable crop production. An accurate estimate of disease incidence, disease severity and negative effects on yield quality and quantity is important for precision crop production, horticulture, plant breeding or fungicide screening as well as in basic and applied plant research. Particularly hyperspectral imaging of diseased plants offers insight into processes during pathogenesis. By hyperspectral imaging and subsequent data analysis routines, it was possible to realize an early detection, identification and quantification of different relevant plant diseases. Depending on the measuring scale, even subtle processes of defence and resistance mechanism of plants could be evaluated. Within this scope, recent results from studies in barley, wheat and sugar beet and their relevant foliar diseases will be presented.

Copyright

Corresponding author

E-mail: amahlein@uni-bonn.de

References

Hide All
Apan, A, Held, A, Phinn, S and Markley, J 2004. Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery. International Journal of Remote Sensing 25, 489498.
Ashourloo, D, Mobasheri, MR and Huete, A 2014. Developing two spectral disease indices for detection of wheat leaf rust (Puccinia triticina). Remote Sensing 6, 47234740.
Behmann, J, Mahlein, AK, Paulus, S, Kuhlmann, H, Oerke, EC and Plümer, L 2016. Generation and application of hyperspectral 3D plant models: methods and challenges. Machine Vision and Applications 27, 611624.
Behmann, J, Mahlein, AK, Paulus, S, Kuhlmann, H, Oerke, EC and Plümer, L 2015. Calibration of hyperspectral close-range pushbroom cameras for plant phenotyping. ISPRS Journal of Photogrammetry and Remote Sensing 106, 172182.
Bock, CH, Poole, GH, Parker, PE and Gottwald, TR 2010. Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Critical Reviews in Plant Science 29, 59107.
Bravo, C, Moshou, D, West, J, McCartney, A and Ramon, H 2003. Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering 84, 137145.
Delalieux, S, van Aardt, J, Keulemans, W and 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, 130143.
Hillnhütter, C, Mahlein, AK, Sikora, RA and Oerke, E-C 2011. Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solani in sugar beet fields. Field Crops Research 122, 7077.
Huang, W, Lamb, DW, Niu, Z, Zhang, Y, Liu, L and Wang, J 2007. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture 8, 187197.
Kuska, M, Wahabzada, M, Leucker, M, Dehne, HW, Kersting, K, Oerke, EC, Steiner, U and Mahlein, AK 2015. Hyperspectral phenotyping on microscopic scale – towards automated characterization of plant-pathogen interactions. Plant Methods 11, 28.
Leucker, M, Wahabzada, M, Kersting, K, Peter, M, Beyer, W, Steiner, U, Mahlein, AK and Oerke, EC 2016. Hyperspectral imaging reveals the effect of sugar beet quantitative trait loci on Cercospora leaf spot resistance. Functional Plant Biology doi: 10.1071/FP16121.
Mahlein, AK, Rumpf, T, Welke, P, Dehne, HW, Plümer, L, Steiner, U and Oerke, EC 2013. Development of spectral vegetation indices for detecting and identifying plant diseases. Remote Sensing of Environment 128, 2130.
Mahlein, AK, Steiner, U, Hillnhütter, C, Dehne, HW and Oerke, EC 2012. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet disease. Plant Methods 8 (1), 3.
Mahlein, AK 2016. Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease 2, 241251.
Oerke, EC, Herzog, K and Töpfer, R 2016. Hyperspectral phenotyping of the reaction of grapevine genotypes to Plasmopara viticola . Journal of Experimental Botany 67, 55295543.
Oerke, EC, Mahlein, AK and Steiner, U 2014. Proximal sensing of plant diseases. In Detection and Diagnostic of Plant Pathogens, Plant Pathology in the 21st Century, p 55-68, eds. Gullino M. L. and Bonants, P. J. M., Springer Science and Business Media Dordrecht.
Paulus, S, Dupuis, J, Mahlein, AK and Kuhlmann, H 2013. Surface feature based classification of plant organs from 3D laserscanned point clouds for plant phenotyping. BMC Bioinformatics 14, 238.
Polder, G, van der Heijden, GWAM, van Doorn, J and Baltissen, TAHMC 2014. Automatic detection of tulip breaking virus (TBV) in tulip fields using machine vision. Biosystems Engineering 117, 3542.
Roscher, R, Behmann, J, Mahlein, AK, Dupuis, J, Kuhlmann, H and Plümer, L 2016. Detection of Disease Symptoms on Hyperspectral 3D Plant Models, in ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, pp. 89-96.
Sankaran, S, Mishra, A, Ehsani, R and Davis, C 2010. A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture 72, 113.
Savitzky, A and Golay, JME 1964. Smoothing and differentiation of data by simplified least squares procedures. Annals of Chemistry 36, 16271639.
Wahabzada, M, Mahlein, AK, Bauckhage, C, Steiner, U, Oerke, EC and Kersting, K 2016. Plant phenotyping using probabilistic topic models: Uncovering the hyperspectral language of plants. Scientific Reports 6, 22482.
Wahabzada, M, Mahlein, AK, Bauckhage, C, Steiner, U, Oerke, EC and Kersting, K 2015a. Metro maps of plant disease dynamics - automated mining of differences using hyperspectral images. PLOS One 10, 120.
Wahabzada, M, Paulus, S, Kersting, K and Mahlein, AK 2015b. Automated interpretation of 3D laserscanned point clouds for plant organ segmentation. BMC Bioinformatics 16, 248.
Walter, A, Liebisch, F and Hund, A 2015. Plant phenotyping: from bean weighing to image analysis. Plant Methods 11, 14.
West, JS, Bravo, C, Oberti, R, Lemaire, D, Moshou, D and McCartney, HA 2003. The potential of optical canopy measurement for targeted control of field crop diseases. Annual Review of Phytopathology 41, 593614.
Virlet, N, Sabermanesh, K, Sadeghi-Tehran, P and Hawkesford, MJ 2016. Field scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring. Functional Plant Biology http://dx.doi.org/10.1071/FP16163

Keywords

Metrics

Full text views

Total number of HTML views: 0
Total number of PDF views: 0 *
Loading metrics...

Abstract views

Total abstract views: 0 *
Loading metrics...

* Views captured on Cambridge Core between <date>. This data will be updated every 24 hours.

Usage data cannot currently be displayed