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

Published online by Cambridge University Press:  01 June 2017

A-K. Mahlein ,
M. T. Kuska ,
S. Thomas ,
D. Bohnenkamp ,
E. Alisaac ,
J. Behmann ,
M. Wahabzada  and
K. Kersting
A-K. Mahlein*
Affiliation:
INRES-Phytomedicine, University of Bonn, Germany
M. T. Kuska
Affiliation:
INRES-Phytomedicine, University of Bonn, Germany
S. Thomas
Affiliation:
INRES-Phytomedicine, University of Bonn, Germany
D. Bohnenkamp
Affiliation:
INRES-Phytomedicine, University of Bonn, Germany
E. Alisaac
Affiliation:
INRES-Phytomedicine, University of Bonn, Germany
J. Behmann
Affiliation:
INRES-Phytomedicine, University of Bonn, Germany
M. Wahabzada
Affiliation:
INRES-Phytomedicine, University of Bonn, Germany
K. Kersting
Affiliation:
Department of Informatics, TU Dortmund, Germany
*
†E-mail: [email protected]
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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.

Keywords

Plant diseasescrop protectionresistancehyperspectral imagingmachine learning
Type
Crop Protection
Information
Advances in Animal Biosciences , Volume 8 , Special Issue 2: Papers presented at the 11th European Conference on Precision Agriculture (ECPA 2017), John McIntyre Centre, Edinburgh, UK, July 16–20 2017 , July 2017 , pp. 238 - 243
Copyright
© The Animal Consortium 2017 

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References

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, 489–498.CrossRefGoogle Scholar
Ashourloo, D, Mobasheri, MR and Huete, A 2014. Developing two spectral disease indices for detection of wheat leaf rust (Puccinia triticina). Remote Sensing 6, 4723–4740.CrossRefGoogle Scholar
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, 611–624.CrossRefGoogle Scholar
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, 172–182.CrossRefGoogle Scholar
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, 59–107.CrossRefGoogle Scholar
Bravo, C, Moshou, D, West, J, McCartney, A and Ramon, H 2003. Early disease detection in wheat fields using spectral reflectance. Biosystems Engineering 84, 137–145.CrossRefGoogle Scholar
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, 130–143.CrossRefGoogle Scholar
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, 70–77.CrossRefGoogle Scholar
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, 187–197.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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, 21–30.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
Mahlein, AK 2016. Plant disease detection by imaging sensors – parallels and specific demands for precision agriculture and plant phenotyping. Plant Disease 2, 241–251.CrossRefGoogle Scholar
Oerke, EC, Herzog, K and Töpfer, R 2016. Hyperspectral phenotyping of the reaction of grapevine genotypes to Plasmopara viticola . Journal of Experimental Botany 67, 5529–5543.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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, 35–42.CrossRefGoogle Scholar
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.CrossRefGoogle Scholar
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, 1–13.CrossRefGoogle Scholar
Savitzky, A and Golay, JME 1964. Smoothing and differentiation of data by simplified least squares procedures. Annals of Chemistry 36, 1627–1639.CrossRefGoogle Scholar
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.CrossRefGoogle ScholarPubMed
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, 1–20.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Walter, A, Liebisch, F and Hund, A 2015. Plant phenotyping: from bean weighing to image analysis. Plant Methods 11, 14.CrossRefGoogle ScholarPubMed
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, 593–614.CrossRefGoogle ScholarPubMed
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 CrossRefGoogle Scholar