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.
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References
Bauriegel E, Giebel A, Geyer M, Schmidt U, Herppich WB (2011) Early detection of Fusarium infection in wheat using hyperspectral imaging. Comput Electron Agric 75:304–312
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 64:333–341
Blackburn GA (2007) Hyperspectral remote sensing of plant pigments. J Exp Bot 58:844–867
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 29:59–107
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 36:103–111
Bravo C, Moushou D, West J, McCartney A, Ramon H (2003) Early disease detection in wheat fields using spectral reflectance. Biosyst Eng 84:137–145
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 168:1641–1648
Buerling K, Hunsche M, Noga G (2012) Presymptomatic detection of powdery mildew infection in winter wheat cultivars by laser-induced fluorescence. Appl Spectrosc 66:1411–1419
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 45:124–131
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 101:1614–1623
Carter GA, Knapp AK (2001) Leaf optical properties in higher plants: linking spectral characteristics to stress and chlorophyll concentration. Am J Bot 88:677–684
Chaerle L, Van der Straeten D (2000) Imaging techniques and the early detection of plant stress. Trends Plant Sci 5:495–501
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 58:773–784
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 4:1152–1167
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 125:291–302
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 27:130–143
Dudareva N, Negre F, Nagegowda DA, Orlova I (2006) Plant volatiles: recent advances and future perspectives. Crit Rev Plant Sci 25:417–440
Franke J, Menz G (2007) Multi-temporal wheat disease detection by multi-spectral remote sensing. Precison Agric 8:161–172
Gebbers R, Adamchuk VI (2010) Precision agriculture and food security. Science 327:828–831
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, 1–5
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 122:70–77
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 13:17–32
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 63:319–327
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 14:697–704
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 223–232
Jones HG, Schofield P (2008) Thermal and other remote sensing of plant stress. Gen Appl Plant Physiol 34:19–32
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
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 10:34–44
Kushalappa AC, Lui LH, Chen CR, Lee B (2002) Volatile fingerprinting (SPMEGCFID) to detect and discriminate diseases of potato tubers. Plant Dis 86:131–137
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 42:8433–8439
Lenthe J-H (2006) Erfassung befallsrelevanter Klimafaktoren in Weizenbeständen mit Hilfe digitaler Infrarot-Thermographie. PhD thesis, University of Bonn
Lenthe J-H, Oerke E-C, Dehne H-W (2007) Digital thermography for monitoring canopy health of wheat. Precision Agric 8:15–26
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, 2009
Lindenthal M (2005) Visualisierung der Krankheitsentwicklung von Falschem Mehltau an Gurken durch Pseudoperonospora cubensis mittels Thermography. PhD thesis, University of Bonn
Lindenthal M, Steiner U, Dehne HW, Oerke EC (2005) Effect of downy mildew development on transpiration of cucumber leaves visualized by digital thermography. Phytopathology 95:233–240
Mahlein AK, Steiner U, Dehne HW, Oerke EC (2010) Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agric 11:413–431
Mahlein AK, Oerke EC, Steiner U, Dehne HW (2012a) Recent advances in sensing plant diseases for precision crop protection. Eur J Plant Pathol 133:197–203
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
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 128:21–30
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 66:140–146
Montes JM, Melchinger AE, Reif JC (2007) Novel throughput phenotyping platforms in plant genetic studies. Trends Plant Sci 12:433–436
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 44:173–188
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 66:38–45
Narvankar DS, Singh CB, Jayas DS, White NDG (2009) Assessment of soft X-ray imaging for detection of fungal infection in wheat. Biosyst Eng 103:49–56
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 27–50
Oerke EC (2006) Crop losses to pests. J Agric Sci 144:31–43
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 167–182
Oerke EC, Steiner U, Dehne HW, Lindenthal M (2006) Thermal imaging of cucumber leaves affected by downy mildew and environmental conditions. J Exp Bot 57:2121–2132
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, Dordrecht
Oerke EC, Fröhling P, Steiner U (2011) Thermographic assessment of scab disease on apple leaves. Precision Agric 12:699–715
Pearson TC, Wicklow DT (2006) Detection of kernels infected by fungi. Trans ASABE 49(4):1235–1245
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 36:334–348
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 113:110–122
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 110:371–377
Quin J, Burks TF, Ritenour MA, Bonn WG (2009) Detection of citrus canker using hyperspectral reflectance imaging with spectral information divergence. J Food Eng 93:183–191
Reynolds GJ, Windels CE, MacRae IV, Laguette S (2012) Remote sensing for assessing Rhizoctonia crown and root rot severity in sugar beet. Plant Dis 96:497–505
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)
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 74:91–99
Sankaran S, Mishraa A, Ehsania R, Davis C (2010) A review of advanced techniques for detecting plant diseases. Comput Electron Agric 72:1–13
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 69:359–363
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 36:880–892
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 704:87–90
Stafford JV (2000) Implementing precision agriculture in the 21st century. J Agric Eng Res 76:267–275
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 89:153–158
Steiner U, Buerling K, Oerke EC (2008) Sensorik für einen präzisierten Pflanzenschutz. Gesunde Pflanzen 60:131–141
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 529–535
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 9:407–417
Thenkabail PS, Smith RB, De Pauw E (2000) Hyperspectral vegetation indices and their relationship with agricultural crop characteristics. Remote Sens Environ 71:158–182
Vadivambal R, Jayas DS (2011) Applications of thermal imaging in agriculture and food industry – a review. Food Bioprocess Technol 4:186–199
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
Waggoner PE, Aylor DE (2000) Epidemiology, a science of patterns. Annu Rev Phytopathol 38:1–24
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 41:593–614
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 135–150
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 4:295–310
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Oerke, EC., Mahlein, AK., Steiner, U. (2014). Proximal Sensing of Plant Diseases. In: Gullino, M., Bonants, P. (eds) Detection and Diagnostics of Plant Pathogens. Plant Pathology in the 21st Century, vol 5. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-9020-8_4
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