Monitoring powdery mildew of winter wheat by using moderate resolution multi-temporal satellite imagery

PLoS ONE

Volumn 9, Issue 4, 2014, Pages

  Zhang, Jingcheng ^a,b,c   Pu, Ruiliang ^b   Yuan, Lin ^a,c   Wang, Jihua ^a,c   Huang, Wenjiang ^a,d   Yang, Guijun ^a  

^a INSTITUTE OF PLANT AND ENVIRONMENT PROTECTION   (China)

^b UNIVERSITY OF SOUTH FLORIDA   (United States)

^c ZHEJIANG UNIVERSITY   (China)

^d CENTER FOR EARTH OBSERVATION AND DIGITAL EARTH   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; BIOSENSOR; CHINA; CONTROLLED STUDY; DISEASE SEVERITY; MAHALANOBIS DISTANCE; MAXIMUM LIKELIHOOD METHOD; MEASUREMENT ACCURACY; MEASUREMENT ERROR; MIXTURE TUNED MATCHED FILTERING; NEAR INFRARED IMAGING SYSTEM; NONHUMAN; PARTIAL LEAST SQUARES REGRESSION; POWDERY MILDEW; SATELLITE IMAGERY; STATISTICAL ANALYSIS; SUBURBAN AREA; WINTER WHEAT; ALGORITHM; ASCOMYCETES; MICROBIOLOGY; NEAR INFRARED SPECTROSCOPY; PHYSIOLOGY; PLANT DISEASE; QUESTIONNAIRE; WHEAT;

ALGORITHMS; ASCOMYCOTA; BEIJING; PLANT DISEASES; SATELLITE IMAGERY; SPECTROSCOPY, NEAR-INFRARED; SURVEYS AND QUESTIONNAIRES; TRITICUM;

EID: 84898889523     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0093107     Document Type: Article

References (57)

1. Christou P, Twyman RM (2004) The potential of genetically enhanced plants to address food insecurity. Nutrition Research Reviews 17: 23-42. (Pubitemid 38901938)
2. Strange RN, Scott PR (2005) Plant Disease: A Threat to Global Food Security. Annual review of Phytopathology 43:83-116. (Pubitemid 41318866)
3. West JS, Bravo C, Oberti R, Lemaire D, Moshou D, et al. (2003) The potential of optical canopy measurement for targeted control of field crop diseases. Annual review of Phytopathology 41:593-614. (Pubitemid 37399694)
4. Sankaran S, Mishra A, Ehsani R, Davis C (2010) A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture 72: 1-13.
5. Zhang MH, Qin ZH, Liu X, Ustin SL (2003) Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. International Journal of Applied Earth observation and Geo information 4: 295-310. (Pubitemid 38242216)
6. Moshou D, Bravo C, West J, Wahlen S, McCartney A, et al. (2004) Automatic detection of 'yellow rust' in wheat using reflectance measurements and neural networks. Computers and Electronics in Agriculture 44:173-188. (Pubitemid 39270536)
7. Muhammed HH (2005) Hyperspectral Crop Reflectance Data for characterising and estimating Fungal Disease Severity in Wheat. Biosystems Engineering 91: 9-20.
8. Huang WJ, David WL, Niu Z, Zhang YJ, Liu LY, et al. (2007) Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imaging. Precision Agriculture 8: 187-197. (Pubitemid 350067880)
9. Devadas R, Lamb DW, Simpfendorfer S, Backhouse D (2009) Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves. Precision Agriculture 10: 459-470.
10. Zhang JC, Huang WJ, Li JY, Yang GJ, Luo JH, et al. (2011a) Development, evaluation and application of a spectral knowledge base to detect yellow rust in winter wheat. Precision Agriculture 12: 716-731.
11. Nilsson HE (1995) Remote sensing and image analysis in plant pathology. Canadian Journal of Plant Pathology 17: 154-166.
12. Bauriegel E, Giebel A, Geyer M, Schmidt U, Herppich WB (2011) Early detection of Fusarium infection in wheat using hyper-spectral imaging. Computers and Electronics in Agriculture 75: 304-312
13. Zhang JC, Pu RL, Wang JH, Huang WJ, Yuan L, et al. (2012) Detecting powdery mildew of winter wheat using leaf level hyperspectral measurements. Computers and Electronics in Agriculture 85: 13-23.
14. Hahn F (2009) Actual Pathogen Detection: Sensors and Algorithms - a Review. Algorithms 2: 301-338.
15. Zhang JC, Luo JH, Huang WJ, Wang JH (2011b) Continuous wavelet analysis based spectral feature selection for winter wheat yellow rust detection. Intelligent Automation and Soft Computing 17: 531-540.
16. Qin ZH, Zhang MH (2005) Detection of rice sheath blight for in-season disease management using multispectral remote sensing. International journal of applied earth observation 7:115-128.
17. Zhang MH, Qin ZH, Liu X (2005) Remote sensed spectral imagery to detect late blight in field tomatoes. Precision Agriculture 6: 489-508. (Pubitemid 41712733)
18. Franke J, Menz G (2007) Multi-temporal wheat disease detection by multispectral remote sensing. Precision Agriculture 8:161-172. (Pubitemid 47039080)
19. Skakun RS, Wulder MA, Franklin SE (2003) Sensitivity of the thematic mapper enhanced wetness difference index to detect mountain pine beetle red-attack damage. Remote Sensing of Environment 86:433-443. (Pubitemid 37143500)
20. Chen X, Ma J, Qiao H, Cheng D, Xu Y, et al. (2007) Detecting infestation of take-all disease in wheat using Landsat Thematic Mapper imagery. International Journal of Remote Sensing 28:5183-5189. (Pubitemid 351454131)
21. Liu DS, Kelly M, Gong P (2006) A spatial-temporal approach to monitoring forest disease spread using multi-temporal high spatial resolution imagery. Remote sensing of environment 101:167-180. (Pubitemid 43343900)
22. Goodwin NR, Coops NC, Wulder MA, Gillanders S, Schroeder TA, et al. (2008) Estimation of insect infestation dynamics using a temporal sequence of Landsat data. Remote sensing of environment 112: 3680-3689.
23. Eklundh L, Johansson T, Solberg S (2009) Mapping insect defoliation in Scots pine with MODIS time-series data. Remote sensing of environment 113:1566-1573.
24. Cooke BM, Jnoes DG, Kaye B (2006) The Epidemiology of Plant Diseases. Springer, Netherland.
25. Olsen M, Rasmussen S, Nischwitz C (2003) Effect of powdery mildew of pecan shucks on nut weight and quality and relevance to fungicide application. Crop Protection 22: 679-682. (Pubitemid 36499830)
26. Sharma AK, Sharma RK, Srinivasa Babu K (2004) Effect of planting options and irrigation schedules on development of powdery mildew and yield of wheat in the North Western plains of India. Crop Protection 23: 249-253. (Pubitemid 38106129)
27. Nofal MA, Haggag WM (2006) Integrated management of powdery mildew of mango in Egypt. Crop Protection 25: 480-486.
28. Rémus-Borel W, Menzies JG, Bélanger RR (2005) Silicon induces antifungal compounds in powdery mildew-infected wheat. Physiological and Molecular Plant Pathology 66:108-115.
29. Lorenzen B, Jensen A (1989) Changes in leaf spectral properties induced in barley by cereal powdery mildew. Remote Sensing of Environment 27: 201-209. (Pubitemid 19305043)
30. Cao XR, Luo Y, Zhou YL, Duan XY, Cheng DF (2013) Detection of powdery mildew in two winter wheat cultivars using canopy hyperspectral reflectance. Crop Protection 45:124-131.
31. Liang SL, Senior Member, IEEE, Fang HL, Chen MZ (2001) Atmospheric correction of Landsat ETM+ Land surface Imagery-Part1: Methods. IEEE Transactions on Geoscience and Remote Sensing 39: 2490-2498.
32. Townshend JRG, Justice CO, Gurney C, McManus J (1992) The impact of misregistration on change detection. IEEE Transactions on Geoscience and Remote Sensing 30: 1056-1060.
33. Pu R, Landry S (2012) A comparative analysis of high resolution ikonos and worldview-2 imagery for mapping urban tree species, Remote Sensing of Environment 124: 516-533.
34. Stohlgren TJ, Barnett DT, Crosier CS (2005) The North American Weed Management Association Standards, http://www.nawma.org/documents/ Mapping%20Standards/BEYOND%20NAWMA%20STANDARDS.pdf.
35. Li GB, Zeng SM, Li ZQ (1989) Integrated Management of Wheat Pests. Beijing, China: Press of Agriculture Science and Technology of China.
36. Zhao CJ, Huang MY, Huang WJ, Liu LY, Wang JH (2004) Analysis of winter wheat stripe rust characteristic spectrum and establishing of inversion models. IGARSS '03 Proceedings, 6:4318-4320.
37. Naidu RA, Perry EM, Pierce FJ, Mekuria T (2009) The potential of spectral reflectance technique for the detection of Grapevine leafroll-associated virus-3in two red-berried wine grape cultivars. Computers and Electronics in Agriculture 66: 38-45.
38. Veraverbeke S, Gitas I, Katagis T, Polychronaki A, Somers B, et al. (2012) Assessing post-fire vegetation recovery using red-near infrared vegetation indices: Accounting for background and vegetation variability. ISPRS Journal of Photogrammetry and Remote Sensing 68: 28-39.
39. Sims DA, Gamon JA (2002) Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages. Remote Sensing of Environment 81: 337-354. (Pubitemid 34853603)
40. Yang CM, Cheng CH, Chen RK (2007) Changes in spectral characteristics of rice canopy infested with brown planthopper and leaffolder. Crop Science 47: 329-335. (Pubitemid 46327869)
41. Zhang JC (2012) Methods for information extraction of wheat disease based on multi-source remote sensing data. Zhejiang University, Doctoral dissertation.
42. Lu D, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. International journal of remote sensing, 25: 2365-2401.
43. Kennedy RE, Townsend PA, Gross JE, Cohen WB, Bolstad P, et al. (2009) Remote sensing change detection tools for natural resource managers: Understanding concepts and tradeoffs in the design of landscape monitoring projects. Remote sensing of environment, 113: 1382-1396.
44. Richards JA (1999) Remote Sensing Digital Image Analysis BerlinSpringer-Verlag240.
45. Pu R (2012) Comparing canonical correlation analysis with partial least square regression in estimating forest leaf area index with multitemporal landsat TM imagery. GIScience & Remote Sensing 49: 92-116.
46. Jones CD, Jones JB, Lee WS (2010) Diagnosis of bacterial spot of tomato using spectral signatures. Computers and Electronics in Agriculture 74: 329-335.
47. Song SL, Gong W, Zhu B, Huang X (2011) Wavelength selection and spectral discrimination for paddy rice, with laboratory measurements of hypserspectral leaf reflectance. ISPRS Journal of Photogrammetry and Remote Sensing 66: 672-682.
48. Andrew ME, Ustin SL (2008) The role of environmental context in mapping invasive plants with hyperspectral image data. Remote Sensing of the Environment 112: 4301-4317.
49. Mitchell JJ, Glenn NF (2009) Subpixel abundance estimates in mixture-tuned matched filtering classifications of leafy spurge (Euphorbia esula L.). International Journal of Remote Sensing 30: 6099-6119.
50. Boardman JW (1998) Leveraging the high dimensionality of AVIRIS data for improved sub-pixel target unmixing and rejection of false positives: mixture tuned matched filtering. In Proceedings of the 5th JPL Geoscience Workshop, R.O. Green (Ed.) Pasadena, California: NASA Jet Propulsion Laboratory.
51. Gonsamo A, Pellikka P (2012) The sensitivity based estimation of leaf area index from spectral vegetation indices. ISPRS Journal of Photogrammetry and Remote Sensing 70:15-25.
52. Eklundh L, Johansson T, Solberg S (2009) Mapping insect defoliation in Scots pine with MODIS time-series data. Remote sensing of environment 113:1566-1573.
53. Rabbinge R, Jorritsma ITM, Schans J (1985) Damage components of powdery mildew in winter wheat. European Journal of Plant Pathology 91: 235-247.
54. Li B, Morris J, Martin EB (2002) Model selection for partial least squares regression. Chemometrics and Intelligent Laboratory Systems 64: 79-89. (Pubitemid 35304561)
55. Faber NM, Rajkó R (2007) How to avoid over-fitting in multivariate calibration- The conventional validation approach and an alternative. Analytica Chimica Acta 595: 98-106. (Pubitemid 46990314)
56. Cooke BM, Jones DG, Kaye B (2006) The Epidemiology of Plant Diseases. NetherlandsSpringer
57. Coops NC, Wulder MA, White JC (2006) Integrating remotely sensed and ancillary data sources to characterize a mountain pine beetle infestation. Remote Sensing of Environment, 105: 83-97. (Pubitemid 44634587)