Characterizing and Estimating Fungal Disease Severity of Rice Brown Spot with Hyperspectral Reflectance Data

Abstract

Abstract: Large-scale farming of agriculture crops requires real-time detection of disease for field pest management. Hyperspectral remote sensing data generally have high spectral resolution, which could be very useful for detecting disease stress in green vegetation at the leaf and canopy levels. In this study, hyperspectral reflectances of rice in the laboratory and field were measured to characterize the spectral regions and wavebands, which were the most sensitive to rice brown spot infected by Bipolaris oryzae (Helminthosporium oryzae Breda. de Hann). Leaf reflectance increased at the ranges of 450 to 500 nm and 630 to 680 nm with the increasing percentage of infected leaf surface, and decreased at the ranges of 520 to 580 nm, 760 to 790 nm, 1550 to 1750 nm, and 2080 to 2350 nm with the increasing percentage of infected leaf surface respectively. The sensitivity analysis and derivative technique were used to select the sensitive wavebands for the detection of rice brown spot infected by B. oryzae. Ratios of rice leaf reflectance were evaluated as indicators of brown spot. R669/R746 (the reflectance at 669 nm divided by the reflectance at 746 nm, the following ratios may be deduced by analogy), R702/R718, R692/R530, R692/R732, R535/R746, R521/R718, and R569/R718 increased significantly as the incidence of rice brown spot increased regardless of whether it’s at the leaf or canopy level. R702/R718, R692/R530, R692/R732 were the best three ratios for estimating the disease severity of rice brown spot at the leaf and canopy levels. This result not only confirms the capability of hyperspectral remote sensing data in characterizing crop disease for precision pest management in the real world, but also testifies that the ratios of crop reflectance is a useful method to estimate crop disease severity.

Key words: derivative spectrum, hyperspectral reflectance, ratio of spectral reflectance, rice brown spot, dise

LIU Zhan-yu, HUANG Jing-feng, TAO Rong-xiang. Characterizing and Estimating Fungal Disease Severity of Rice Brown Spot with Hyperspectral Reflectance Data[J]. RICE SCIENCE, 2008, 15(3): 232-242 .

References

1 Bryant R B, Moran M S. Determining crop water stress from crop temperature variability. In: Proceedings of the Fourth International Airborne Remote Sensing Conference and Exhibition/The 21st Canadian Symposium on Remote Sensing, Ottawa, Canada, 21–24 June 1999. Ann Arbor, MI: ERIM International Inc, 1999: 289–296.
2 West J S, Bravo C, Oberit R, Lemaire D, Moshou D, McCartney H A. The potential of optical canopy measurement for targeted control of field crop diseases. Annu Rev Phytopathol, 2003, 41: 593–614.
3 Jackson R D. Remote sensing of biotic and abiotic plant stress. Annu Rev Phytopathol, 1986, 24: 265–287.
4 Kobayashi T, Kanda E, Kitada K, Ishiguro K, Torigoe Y. Detection of rice panicle blast with multispectral radiometer and the potential of using airborne multispectral scanners. Phytopathology, 2001, 91:316–323.
5 Nilsson H E. Remote sensing and image analysis in plant pathology. Annu Rev Phytopathol, 1995, 15: 489–527.
6 Penuelas J, Filella L. Visible and near-infrared reflectance techniques for diagnosing plant physiological status. Trends Plant Sci, 1998, 3(4): 151–156.
7 Colwell R N. Determining the prevalence of certain cereal crop diseases by means of aerial photography. Hilgardia, 1956, 26: 223–286.
8 Brenchley G H. Aerial photography for the study of plant diseases. Annu Rev Phytopathol, 1968, 6: 1–22.
9 Kanemasu E T, Niblett C L, Manges H, Lenhert D, Newman M A. Wheat: Its growth and disease severity as deduced from ERTS-1. Remote Sensing Environ, 1974, 3: 255–260.
10 Kanemasu E T, Schimmelpfennig H, Choy E C, Eversmeyer M G, Lenhert D. ERTS-1 data collection systems used to predict wheat disease severities. Remote Sensing Environ, 1974, 3: 93–97.
11 Coops N, Stanford M, Old K, Dudzinski M, Culvenor D, Stone C. Assessment of Dothistroma needle blight of Pinus radiate using airborne hyperspectral imagery. Phytopathology, 2003, 93: 1524–1532.
12 Everiu J H, Escobar D E, Villarreal R, Noricga J R, Davis M R. Airborne video systems for agricultural assessment. Remote Sensing Environ, 1991, 35: 231–242.
13 Jackson H R, Wallen V R. Microdensitometer measurements of sequential aerial photographs of field beans infected with bacterial blight. Phytopathology, 1975, 65: 961–968.
14 Nagarajan S, Seibold G, Kranz J, Saari E E, Joshi L M. Monitoring wheat rust epidemics with the Landsat-2 satellite. Phytopathology, 1984, 74: 585–587.
15 Malthus T J, Madeira A C. High resolution spectro-radiometry: Spectral reflectance of field bean leaves infected by Botrytis fabae. Remote Sensing Environ, 1993, 45: 107–116.
16 Steddom K, Heidel G, Gones D, Rush C M. Remote detection of Rhizomania in sugar beets. Phytopathology, 2003, 93: 720– 726.
17 Muhammed H H, Larsolle A. Feature vector based analysis of hyperspectral crop reflectance data for discrimination and quantification of fungal disease severity in wheat. Biosystems Eng, 2003, 86(2): 125–134.
18 Abou-ismail O, Huang J F, Wang R C. Rice yield estimation by integrating remote sensing with rice growth simulation model. Pedosphere, 2004, 14(4): 519–526.
19 Tang Y L, Wang R C, Huang J F. Relations between red edge characteristics and agronomic parameters of crops. Pedosphere, 2004, 14(4): 467–474.
20 Carter G A. Ratios of leaf reflectance in narrow wavebands as indicators of plant stress. Int J Remote Sensing, 1994, 15: 697–703.
21 Carter G A. Primary and secondary effects of water content on the spectral reflectance of leaves. Am J Bot, 1991, 78: 916–924.
22 Huang W J, Liu L Y, Huang M Y, Wang J H, Wang J D, Wan H W. Monitoring of wheat yellow rust with dynamic hyperspectral data. IEEE International Geoscience and Remote Sensing Symposium Proceedings. Vol. 6. Anchorage, Alaska, USA, 20–24 September, 2004. Piscataway, NJ: IEEE, 2004: 4056– 4058. [2007-04-25]. http://ieeexplore.ieee.org/Xplore/login.jsp?url=/ iel5/9436/29950/01370021.pdf?arnumber=1370021.
23 Demetriades-Shah T H, Steven M D, Clark J A. High resolution derivative spectra in remote sensing. Remote Sensing Environ, 1990, 33: 55–64.
24 Riedell W E, Blackmer T M. Leaf reflectance spectra of cereal aphid-damaged wheat. Crop Sci, 1999, 39: 1835–1840.
25 Iqbal N, Ashraf M Y, Javed F, Ashraf M, Hameed S. Cotton leaf curl virus: Ionic status of leaves and symptom development. J Integ Plant Biol, 2006, 48: 558–562.
26 Zhang M, Liu X, O’Neill M. Spectral discrimination of Phytophthora infestans infection on tomatoes based on principal component and cluster analyses. Int J Remote Sensing, 2002, 23: 1095–1107.
27 Horler D N H, Dockray M, Barber J. The red edge of plant reflectance. Int J Remote Sensing, 1983, 4: 273–288.
28 Li Y, Demetriades-Shah T H, Kanemasu E T, Shutis J K, Kirkham M B. Use of second derivatives for monitoring prairie vegetation over different soil backgrounds. Remote Sensing Environ, 1993, 44: 81–87.
29 Philpot W D. The derivative ratio algorithm: Avoiding atmospheric effects in remote sensing. IEEE Trans Geosci Remote Sensing, 1991, 43: 1541–1552.
30 Steven M D, Malthus T H, Demetriades-Shah T H, Danson F M, Clark J A. High spectral resolution indices for crop stress. In: Steven M D, Clark J A. Application of Remote Sensing in Agriculture. London: Butterworths, 1990: 209–228.
31 Chappelle E W, Kim M S, McMurtrey J E. Ratio analysis of reflectance spectra (RARS): An algorithm for the remote estimation of the concentrations of chlorophyll a, chlorophyll b, and carotenoids in soybean leaves. Remote Sensing Environ, 1992, 39: 239–247.
32 Liu Z Y, Huang J F. Wang F M, Wang Y. Adjusted-normalized difference vegetation index for estimating leaf area index of rice. Acta Agron Sin. (in press) (in Chinese with English abstract)

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