Abstract
In order to elucidated characteristics of spectrum of cotton leaf infected with Verticillium wilt and estimated its severity level (SL) to provide theoretic foundation for further monitoring cotton Verticillium wilt at large scale using airborne and airspace remote sensing. The spectrum reflectance of cotton single leaf infected with Verticillium wilt was measured in cotton disease nursery and field at different growth phases, meanwhile, SL of single leaf infected with Verticillium wilt was investigated. The methods of first derivative spectraum were used to estimate accurately disease of cotton with Verticillium wilt when compared with the reflectance spectrum of different single leaf infected of Verticillium wilt. The results indicated that Spectral characteristic of cotton leaf of Verticillium wilt had better regularity with the increase of SL in different periods and varieties. Spectral reflectance increased significantly at visible light region (400–700nm) and near -infrared region (700–1300nm) with the increase of the SL, and specially signification at blue — violet to red regions(525–680nm). when SL got 25%, cotton leaf of Verticillium wilt could be used as a watershed and diagnosed index in early time. There were evident different characteristics of first derivative spectra in these disease leave, it changed significantly in red edge ranges(680–780nm) with different disease level, derivative spectra of red edge swing decreased, and red edge position equal moved to the blue. The thesis indicated that 434–724nm and 909–1600nm were selected out as sensitive bands region to SL of single leaf. Some inversion models for estimating cotton leaf diseased level of Verticillium wilt all reached the best significantly level. The model in which the first derivative spectra at 723nm could invert accurately the cotton leaf SL, and it may be used to forecasting the position of cotton leaf infected with Verticillium wilt in quantitatively.
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References
Adams M L, Philpot W D, Norvell W A (1999) Yellowness index: An application of spectral second derivatives to estimate chlorosis of leaves in stressed vegetation. Int J Remote Sens 20: 3663–3675.
Carter G A, Cibula W G, Miller R L (1996) Narrow-band reflectance imagery compared with thermal imagery for early detection of plant stress. J Plant Physio 148: 515–522.
Chen B, LI S-k, Wang K R et al. (2007) Studies of Remote Sensing on Monitoring Crop Diseases and Pests. Cotton Sci 19:57–63.
Chen P C, Zhang J H, Li M M et al. (2007) Physiological change and hyperspectral character analysis of cotton leaves infested by Tetranychus turkestani. Chin Bull Entomol 44: 61–65.
Demetrialdes-Shan T H, Steven M D, Clark JA (1990) High resolution derivative spectra in remote sensing. Remote Sens Environ 33: 55–64.
Feng X W, Chen X, Ba0 A M et al. (2004) Analysis on the cotton physiological change and its hyperspectral response under the water stress condition. Arid Land Geogr Chin 2: 250–255.
Fitzgerald G J, Maas S J, Detar W R (2004) Spider mite detection and canopy component mapping in cotton using hyperspectral imagery and spectral mixture analysis. Prec Agric 5: 275–289.
Hamed Hamid Muhammed (2005) Hyperspectral crop reflectance data for characterising and estimating fungal disease severity in wheat. Biosyst Eng 91:9–20.
Hamid Muhammed H, Larsolle A (2003) Feature-vector based analysis of hyperspectral crop reflectance data for discrimination quantification of fungal disease severity in wheat. Biosyst Eng 86: 125–134.
Huang M Y, Huang W J, Huang Y D et al. (2004) Spectral reflectance feature of winter wheat single leaf infected with stripe rust and severity level inversion. Trans CSAS Chin 20:176–180.
Jennifer L, Fridgen, Varco J J (2004) Dependency of cotton leaf nitrogen, chlorophyll, and reflectance on nitrogen and potassium availability. Am Soc Agron 96: 63–69.
Kefyalew Girma, Mosali J, Raun W R et al. (2005) Identification of Optical Spectral Signatures for Detecting Cheat and Ryegrass in winter wheat. Crop Sci 45: 477–485.
Lan J H (1992) Ultrastructural changes in cotton leaves affected by carmine spider mite. Southweast Ageric Univ Chin 14: 528–530.
Moshoua D, Bravo C, West J et al. (2004) Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networks. Comput Electro Agric 44:173–188.
Osborne S L, Schepers J S, Francis D D et al. (2002) Remote sensing: Detection of phosphorus and nitrogen deficiencies in corn using spectral radiance easurements.Agron J 94:1215–1221.
Qin Z H, Zhang M H (2005) Detection of rice sheath blight for in-season disease management using multispectral remote sensing. Int J Appl Earth Obs Geoinf. 115–128.
Thomas J R, Oerther GF (1972). Estimating Nitrogen Content of Sweet Pepper Leaves by Reflectance Measurements. Agron J 64:11–13.
Tilling A K, O, Leary G J, Ferwerda J G et al. (2007) Remote sensing of nitrogen and water stress in wheat. Field Crops Res 104:77–85.
Wang K, Shen Z Q, Wang R C (1999) Vegetation nutrient condition and Spectrol feature. Remote Sens Land &sourse 39: 9–14.
Wang X Z, Huang J F, Li Y M et al (2003) Correlation between chemical contents of leaves and characteristic variables of hyperspectral on rice field. Transactions CSAE Chin 19:144–148.
Wang X Z, Li J L, Tang Y L (2004) Approach the action of derivative spectral for determining agronomic parameters of cotton. J Huanan Agric Univ Chin 25:17–21.
Yu G M (1995) The basic principles and methods of remote sensing application to the Identification of waterlog damage. Remote Sens Envion: 10:9–14.
Zhang M, Liu X, Oneill M (2002) Spectral Discrimination of Phytophthora infestans infection on tomatoes based on principal component and cluster analyses. Inter J Remote Sens 23:1095–1107.
Zhang M, Qin Z, Liu X et al (2003) Hyperspectral remote sensing applications in detecting late blight infection on tomatoes. Int J Appl Earth Obs Geoinf 4: 295–310.
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© 2009 Tsinghua University Press, Beijing and Springer-Verlag Berlin Heidelberg
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Chen, B., Wang, KR., Li, SK., Sui, XY., Wang, FY., Bai, JH. (2009). Spectral Characteristics of Cotton Infected with Verticillium Wilt and Severity Level of Disease Estimated Models. In: Cao, W., White, J.W., Wang, E. (eds) Crop Modeling and Decision Support. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01132-0_36
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DOI: https://doi.org/10.1007/978-3-642-01132-0_36
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