Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective
Tóm tắt
Từ khóa
Tài liệu tham khảo
Aasen H, Burkhart A, Bolten A, Bareth G (2015) Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: from camera calibration to quality assurance. ISPRS J Photogramm Remote Sens 108:245–259
Arens N, Backhaus A, Döll S, Fischer S, Seiffert U, Mock H-P (2016) Non-invasive presymptomatic detection of Cercospora beticola infection and identification of early metabolic responses in sugar beet. Front Plant Sci 7:1377
Asner GP, Nepstad D, Cardinot G, Ray D (2004) Drought stress and carbon uptake in an Amazon forest measured with spaceborne imaging spectroscopy. Proc Natl Acad Sci 101:6039–6044
Baranowski P, Jedryczka M, Mazurek W, Babula-Skowronska D, Siedliska A, Kaczmarek J (2015) Hyperspectral and thermal imaging of oilseed rape (Brassica napus) response to fungal species of the genus Alternaria. PLoS ONE. doi: 10.1371/journal.pone.0122913
Bauriegel E, Giebel A, Herppisch WB (2011) Hyperspectral and chlorophyll fluorescence imaging to analyse the impact of Fusarium culmorum on the Photosynthetic integrity of infected wheat ears. Sensors 11:3765–3779
Behmann J, Steinrücken J, Plümer L (2014) Detection of early plant stress responses in hyperspectral images. ISPRS J Photogramm Remote Sens 93:98–111
Behmann J, Mahlein A-K, Rumpf T, Römer C, Plümer L (2015a) A review of advanced machine learning methods for the detection of biotic stress in precision crop protection. Precis Agric 16:239–260
Behmann J, Mahlein A-K, Paulus S, Kuhlmann H, Oerke E-C, Plümer L (2015b) Calibration of hyperspectral close-range pushbroom cameras for plant phenotyping. ISPRS J Photogramm Remote Sens 106:172–182
Behmann J, Mahlein A-K, Paulus S, Kuhlmann H, Oerke E-C, Plümer L (2016) Generation and application of hyperspectral 3D plant models: methods and challenges. Mach Vis Appl 27:611–624
Ben-Dor E, Chabrillat S, Demattê JAM, Taylor GR, Hill J, Whiting ML, Sommer S (2009) Using imaging spectroscopy to study soil properties. Remote Sens Environ 113:38–55
Berdugo CA, Mahlein A-K, Steiner U, Dehne H-W, Oerke E-C (2013) Sensors and imaging techniques for the assessment of the delay of wheat senescence induced by fungicides. Funct Plant Biol 40:677–689
Berdugo CA, Zito R, Paulus S, Mahlein A-K (2014) Fusion of sensor data for the detection and differentiation of plant diseases in cucumber. Plant Pathol 63:1344–1356
Bergsträsser S, Fanourakis D, Schmittgen S, Cendrero-Mateo MP, Jansen M, Scharr H, Rascher U (2015) HyperART: non-invasive quantification of leaf traits using hyperspectral absorption-reflectance-transmittance imaging. Plant Methods 11:1–17
Bishop CM (2006) Pattern recognition and machine learning. Springer, New York
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
Bravo C, Moshou D, West J, McCartney A, Ramon H (2003) Early disease detection in wheat fields using spectral reflectance. Biosys Eng 84:137–145
Bravo C, Moshou D, Oberti R, West J, McCartney A, Bodria L, Ramon H (2004) Foliar disease detection in the field using optical sensor fusion. International Commission of Agricultural Engineering, Vol. VI Manuscript FP 04 008
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
Carocho M, Ferreira ICFR (2013) A review on antioxidants, prooxidants and related controversy: natural and synthetic compounds, screening and analysis methodologies and future perspectives. Food Chem Toxicol 51:15–25
Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20:273–297
Damm A, Guanter L, Verhoef W, Schläpfer D, Garbari S, Schaepman ME (2015) Impact of varying irradiance on vegetation indices and chlorophyll fluorescence derived from spectroscopy data. Remote Sens Environ 156:202–215
Deery D, Jimenez-Berni J, Jones H, Sirault X, Furbank R (2014) Proximal remote sensing buggies and potential applications for field-based phenotyping. Agronomy 4(3):349–379
Delalieux S, Somers B, Verstaeten WW, Keulemans W, Coppin P (2008) Hyperspectral canopy measurements under artificial illumination. Int J Remote Sens 29(20):6051–6058
Delalieux S, Somers B, Verstaeten WW, Vanaardt JAN, Keulemans W, Coppin P (2009) Hyperspectral indices to diagnose leaf biotic stress on apple plants, considering leaf phenology. Int J Remote Sens 30(8):1887–1912
Demattê JAM, Demattê JLI, Camargo WP, Fiorio PR, Nanni MR (2001) Remote sensing in the recognition and mapping of tropical soils developed on topographic sequences. Mapp Sci Remote Sens 38:79–102
Devadas R, Lamb DW, Simpfendorfer S, Backhouse D (2009) Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precis Agric 10:459–470
Elvidge CD, Keith DM, Tuttle BT, Baugh KE (2010) Spectral identification of lighting type and character. Sensors 10(4):3961–3988
Furbank RT, Tester M (2011) Phenomics–technologies to relieve the phenotyping bottleneck. Trends Plant Sci 16:635–644
Govender M, Dye PJ, Weiersbye IM, Witkowski ETF, Ahmed F (2009) Review of commonly used remote sensing and ground-based technologies to measure plant water stress. Water SA 35:741–752
Granier C, Vile D (2014) Phenotyping and beyond: modelling the relationships between traits. Curr Opin Plant Biol 18:96–102
Grieve B, Hammersley S, Mahlein A-K, Oerke E-C, Goldbach H (2015) Localized multispectral crop imaging sensors: engineering and validation of cost effective plant stress and disease sensors. In: IEEE sensors applications symposium (SAS), Zadar, pp 1–6
Großkinsky DK, Svengaard J, Christensen S, Roitsch T (2015) Plant phenomics and the need for physiological phenotyping across scales to narrow the genotype-to-phenotype knowledge gap. J Exp Bot 66(18):5429–5440
Hbirkou C, Pätzhold S, Mahlein A-K, Welp G (2012) Airborne hyperspectral imaging of spatial soil organic carbon heterogeneity at the field-scale. Geoderma 175–176:21–28
Hillnhütter C, Mahlein A-K, Sikora RA, Oerke E-C (2011) Remote sensing to detect plant stress induced by Heterodera schachtii and Rhizoctonia solane in sugar beet fields. Field Crops Res 122:70–77
Hillnhütter C, Mahlein A-K, Sikora RA, Oerke E-C (2012) Use of imaging spectroscopy to discriminate symptoms caused Heterodera schachtii and Rhizoctonia solane on sugar beet. Precis Agric 13:17–32
Huang J-F, Apan A (2006) Detection of Sclerotinia rot disease on celery using hyperspectral data and partial least squares regression. J Spat Sci 51:129–142
Huang W, Lamb DW, Niu Z, Zhang Y, Liu L, Wang J (2007) Identification of yellow rust in wheat using in situ spectral reflectance measurements and airborne hyperspectral imaging. Precis Agric 8:187–197
Jacquemoud S, Verhoef W, Baret F, Bacour C, Zarco-Tejada PJ, Asner GP, François C, Ustin SL (2009) PROSPECT + SAIL models: a review of use for vegetation characterization. Remote Sens Environ 113:56–66
Jensen JR (2006) Remote sensing of the environment: an earth resource perspective. 2nd end. Prentice-Hall, Upper Saddle River
Kersting K, Wahabzada M, Römer C, Thurau C, Ballvora A, Rascher U, Leon J, Bauckhage C, Plümer L (2012a) Simplex distributions for embedding data matrices over time. In: Proceedings of the 2012 SIAM international conference on data mining, pp 295–306
Kersting K, Xu Z, Wahabzada M, Bauckhage C, Thurau C, Römer C, Ballvora A, Rascher U, Leon J, Plümer L (2012b) Pre–symptomatic prediction of plant drought stress using dirichlet–aggregation regression on hyperspectral images. In: Proceedings of the tetny-sixth AAAI conference on artificial intelligence, pp 302–308
Kim DM, Zhang H, Zhou H, Du T, Wu Q, Mockler TC, Berezin MY (2015) Highly sensitive image-derived indices of water-stressed plants using hyperspectral imaging in SWIR and histogram analysis. Sci Rep 5:15919
Kruse FA, Lefkoff AB, Boardman JW, Heidebrecht KB, Shapiro AT, Barloon PJ, Goetz AFH (1993) The spectral image processing system (SIPS)-interactive visualization and analysis of imaging spectrometer data. Remote Sens Environ 44:145–163
Kuska M, Wahabzada M, Leucker M, Dehne H-W, Kersting K, Oerke E-C, Steiner U, Mahlein A-K (2015) Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions. Plant Methods 11:28
Kuska MT, Brugger A, Thomas S, Wahabzada M, Kersting K, Oerke EC, Steiner U, Mahlein AK (2017) Spectral patterns reveal early resistance reactions of barley against Blumeria graminis f. sp. hordei. Phytopathology. doi: 10.1094/PHYTO-04-17-0128-R
Leucker M, Mahlein A-K, Steiner U, Oerke E-C (2016) Improvement of lesion phenotyping in Cercospora beticola-sugar beet interaction by hyperspectral imaging. Phytopathology 2:177–184
Leucker M, Wahabzada M, Kersting K, Peter M, Beyer W, Steiner U, Mahlein A-K, Oerke E-C (2017) Hyperspectral imaging reveals the effect of sugar beet quantitative trait loci on Cercospora leaf spot resistance. Funct Plant Biol 44:1–9
Li H, Lee WS, Wang K, Ehsani R, Yang C (2014) ‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging. Precis Agric 15:162–183
Oerke EC, Mahlein AK, Steiner U (2014) Proximal sensing of plant diseases. In: Gullino ML, Bonants PJM (eds) Detection and diagnostics of plant pathogens. Springer, Dordrecht, pp 55–68. doi: 10.1007/978-94-017-9020-8_4
Mahlein A-K (2016) Plant disease detection by imaging sensors—parallels and specific demands for precision agriculture and plant phenotyping. Plant Dis 100:241–251
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. Precis Agric 11:413–431
Mahlein A-K, Steiner U, Hillnhütter C, Dehne H-W, Oerke E-C (2012) Hyperspectral imaging for small-scale analysis of symptoms caused by different sugar beet disease. Plant Methods 8(1):3
Mahlein A-K, Rumpf T, Welke P, Dehne H-W, Plümer L, Steiner U, Oerke E-C (2013) Development of spectral indices for detecting and identifying plant diseases. Remote Sens Environ 128:21–30
Mahlein A-K, Hammersley S, Oerke E-C, Dehne H-W, Goldbach H, Grieve B (2015) Supplemental blue LED lighting array to improve the signal quality in hyperspectral imaging of plants. Sensors 15(6):12834–12840
Mahoney M, Drineas P (2009) CUR matrix decompositions for improved data analysis. Proc Nat Acad Sci 106:697–702
Martinelli F, Scalenghe R, Davino S, Panno S, Scuderi G, Ruisi P, Villa P, Stroppiana D, Boschetti M, Guolart LR, Davis CE, Dandekar AM (2014) Advanced methods for plant disease detection. A rev Agron Sustain Dev 35:1–25
Melgani F, Bruzzone L (2004) Classification of hyperspectral remote sensing images with support vector machines. IEEE Trans Geosci Remote Sens 42:1778–1790
Milton EJ, Shaepmann ME, Anderson K, Kneubühler M, Fox N (2009) Progress in field spectroscopy. Remote Sens Environ 113:92–109
Mirik M, Michels GJ, Kassymzhanova-Mirik S, Elliot NC, Bowling R (2006) Hyperspectral spectrometry as a means to differenciate uninfested and infested winter wheat by greenbug (Hemiptera: Aphididae). J Econ Entomol 99(5):1682–1690
Montes JM, Technow F, Dhillon BS, Mauch F, Melchinger AE (2011) High-throughput non-destructive biomass determination during early plant development in maize under field conditions. Field Crops Res 121:268–273
Moshou D, Bravo C, Oberti R, West J, Bodria L, McCartney A, Ramon H (2005) Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen maps. Real Time Imaging 11:75–83
Mutka AM, Bart RS (2014) Image-based phenotyping of plant disease symptoms. Front Plant Sci 5:734
Nevalainen O, Honkavaara E, Touminen S, Viljanen N, Hakala T, Yu X, Hyyppä J, Saari H, Pölönen I, Imai NN, Tommaselli AMG (2017) Individual tree detection and classification with UAV-based photogrammetric point clouds and hyperspectral imaging. Remote Sens 9:185
Pinto F, Damm A, Schickling A, Panigada C, Cogliati S, Müller-Linow M, Balcora A, Rascher U (2016) Sun-induced chlorophyll fluorescence from high-resolution imaging spectroscopy data to dandify spatio-temporal patterns of photosynthetic function in crop canopies. Plant Cell Environ 39:1500–1512
Plaza A, Benediktsson JA, Boardman JW, Brazlie J, Bruzzone L, Camps-Valls G, Chanussot J, Fauvel M, Gamba P (2009) Recent advances in techniques for hyperspectral image processing. Remote Sens Environ 113:110–122
Polder G, van der Heijden GWAM, van Doorn J, Baltissen TAHMC (2014) Automatic detection of tulip breaking virus (TBV) in tulip fields using machine vision. Biosys Eng 117:35–42
Rodionov A, Welp G, Damerow L, Berg T, Amelung W, Pätzold S (2015) Towards on-the-go field assessment of soil organic carbon using Vis-NIR diffuse reflectance spectroscopy: developing and testing a novel tractor-driven measuring chamber. Soil Tillage Res 145:93–102
Römer C, Wahabzada M, Ballvora A, Pinto F, Rossini M, Panigada C, Behmann J, Léon J, Thurau C, Bauckhage C, Kersting K, Rascher U, Plümer L (2012) Early drought stress detection in cereals: simplex volume maximization for hyperspectral image analysis. Funct Plant Biol 39(11):878–890
Roscher R, Behmann J, Mahlein A-K, Dupuis J, Kuhlmann H, Plümer L (2016) Detection of disease symptoms on hyperspectral 3D plant models. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 3(7):89–96
Rouse JW, Haas RH, Schell JA, Deering DW (1974) Monitoring vegetation systems in the great plains with ERTS. In Freden SC, Mercanti EP, Becker M (ed), Third earth resources technology satellite-1 symposium. Volume I: technical presentations (pp 309–317), Washington DC: NASA SP-351
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. Comput Electron Agric 74:91–99
Sankaran S, Khot LR, Espinoza CZ, Jarolmasjed S, Sathuvalli VR, Vandemark GJ, Miklas PN, Carter AH, Pumphrey MO, Knowles NR, Pavek KJ (2015) Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: a review. Eur J Agron 70:112–123
Simko I, Jiminez-Berni JA, Sirault XRR (2017) Phenomic approaches and tool for phytopathologists. Phytopathology 107:6–17
Tackenberg M, Volkmar C, Dammer K-H (2016) Sensor-based variable-rate fungicide application in winter wheat. Pest Manag Sci 72(10):1888–1896
Thomas S, Wahabzada M, Kuska M, Rascher U, Mahlein A-K (2017) Observation of plant–pathogen interaction by simultaneous hyperspectral imaging reflection and transmission measurements. Funct Plant Biol 44:23–34
Thurau C, Kersting K, Wahabzada M, Bauckhage C (2012) Descriptive matrix factorization for sustainability: adopting the principle of opposites. J Data Min Knowl Discov 24:325–354
Vigneau N, Ecarnot M, Rabatel G, Roumet P (2011) Potential of field hyperspectral imaging as a non destructive method to assess leaf nitrogen content in wheat. Field Crops Res 122:25–31
Virlet N, Sabermanesh K, Sadeghi-Tehran P, Hawkesford MJ (2017) Field scanalyzer: an automated robotic field phenotyping platform for detailed crop monitoring. Funct Plant Biol 44:143–153
Wahabzada M, Kersting K, Bauckhage C, Römer C, Ballvora A, Pinto F, Rascher U, Léon J, Plümer L (2012) Latent dirichlet allocation uncovers spectral characteristics of drought stressed plants. arXiv preprint arXiv, 1210.4919
Wahabzada M, Mahlein A-K, Bauckhage C, Steiner U, Oerke E-C, Kersting K (2015a) Metro maps of plant disease dynamics - automated mining of differences using hyperspectral images. PLoS ONE. doi: 10.1371/journal.pone.0116902
Wahabzada M, Paulus S, Kersting K, Mahlein A-K (2015b) Automated interpretation of 3D laserscanned point clouds for plant organ segmentation. BMC Bioinform 16:248
Wahabzada M, Mahlein A-K, Bauckhage C, Steiner U, Oerke E-C, Kersting K (2016) Plant phenotyping using probabilistic topic models: uncovering the hyperspectral language of plants. Sci Rep 6:22482. doi: 10.1038/srep22482
Walter A, Liebisch F, Hund A (2015) Plant phenotyping: from bean weighting to image analysis. Plant Methods 11:14
West JS, Bravo C, Oberti R, Moshou D, Ramon H, McCartner HA (2010) Detection of fungal disease optically and pathogen inoculums by air sampling. In: Oerke E-C, Gerhards R, Menz G, Sikora RA (eds) Precision crop protection-the challenge and use of heterogeneity. Springer, Dordrecht, pp 135–149
Winterhalter L, Mistele B, Jampatong S, Schmidhalter U (2011) High throughput phenotyping of canopy water mass and canopy temperature in well-watered and drought stressed tropical maize hybrids in the vegetative stage. Eur J Agron 35:22–32
Yeh YH, Chung WC, Liao JY, Chung CL, Kuo YF, Lin TT (2016) Strawberry foliar anthracnose assessment by hyperspectral imaging. Comput Electron Agric 122:1–9
Yin X, Struik PC, Kropf MJ (2004) Role of crop physiology in predicting gene-to-phenotype relationships. Trends Plant Sci 9(9):426–432