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B. Becker, D. Lusch, J. Qi (2007)
A classification-based assessment of the optimal spectral and spatial resolutions for Great Lakes coastal wetland imageryRemote Sensing of Environment, 108
Yongqin Zhang, Jing Chen, John Miller, T. Noland (2008)
Leaf chlorophyll content retrieval from airborne hyperspectral remote sensing imageryRemote Sensing of Environment, 112
J. Boardman (1993)
Automating spectral unmixing of AVIRIS data using convex geometry concepts
S. South, J. Qi, D. Lusch (2004)
Optimal classification methods for mapping agricultural tillage practicesRemote Sensing of Environment, 91
JC Zadoks (1961)
Yellow rust on wheat, studies in epidemiology and physiologic specializationEuropean Journal of Plant Pathology, 67
J. Kolmer (2005)
Tracking wheat rust on a continental scale.Current opinion in plant biology, 8 4
Haiguang Wang, Zhan-hong Ma, Tao Wang, Cheng Cai, Huang-Da An, Luda Zhang (2007)
[Application of hyperspectral data to the classification and identification of severity of wheat stripe rust].Guang pu xue yu guang pu fen xi = Guang pu, 27 9
X. Jia, J. Richards (1999)
Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classificationIEEE Trans. Geosci. Remote. Sens., 37
J. Franke, G. Menz, E. Oerke, U. Rascher (2005)
Comparison of multi- and hyperspectral imaging data of leaf rust infected wheat plants, 5976
J. Franke, G. Menz (2007)
Multi-temporal wheat disease detection by multi-spectral remote sensingPrecision Agriculture, 8
(1989)
Integrated management of wheat pests. Beijing, China: Press of Agriculture Science and Technology of China
Yu-ling Li, Y. Dong, S. Niu, Dangqun Cui (2009)
Identification of QTL for Popping Characteristics Using a BC2F2 Population and Comparison with Its F2:3 Population in PopcornAgricultural Sciences in China, 8
D. Moshou, C. Bravo, R. Oberti, J. West, L. Bodria, A. McCartney, H. Ramon (2005)
Plant disease detection based on data fusion of hyper-spectral and multi-spectral fluorescence imaging using Kohonen mapsReal Time Imaging, 11
P. Zarco-Tejada, John Miller, A. Morales, A. Berjón, J. Agüera (2004)
Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree cropsRemote Sensing of Environment, 90
S. Jacquemoud, F. Baret (1990)
PROSPECT: A model of leaf optical properties spectraRemote Sensing of Environment, 34
C. Bravo, D. Moshou, J. West, A. McCartney, H. Ramon (2003)
Early Disease Detection in Wheat Fields using Spectral ReflectanceBiosystems Engineering, 84
D. Hailu, C. Fininsa (2007)
Epidemics of stripe rust (Puccinia striiformis) on common wheat (Triticum aestivum) in the highlands of Bale, southeastern EthiopiaCrop Protection, 26
J. Zadoks (1961)
Yellow rust on wheat studies in epidemiology and physiologic specializationTijdschrift Over Plantenziekten, 67
X. Yue (2003)
Resistance to wheat stripe rust induced by avirulent race of Puccinia striiformis
D. Fu, C. Uauy, A. Distelfeld, A. Blechl, L. Epstein, Xianming Chen, Hanan Sela, T. Fahima, J. Dubcovsky (2009)
A Kinase-START Gene Confers Temperature-Dependent Resistance to Wheat Stripe RustScience, 323
Wenjiang Huang, D. Lamb, Z. Niu, Yongjiang Zhang, Liangyun Liu, Jihua Wang (2007)
Identification of yellow rust in wheat using in-situ spectral reflectance measurements and airborne hyperspectral imagingPrecision Agriculture, 8
Robert Bryson, N. Paveley, William Clark, R. Slylvester-Bradley, Richard Scott (1997)
Use of in-field measurements of green leaf area and incident radiation to estimate the effects of yellow rust epidemics on the yield of winter wheatEuropean Journal of Agronomy, 7
HG Wang, ZH Ma, T Wang, CJ Cai, H An, LD Zhang (2007)
Application of hyperspectral data to the classification and identification of severity of wheat stripe rustSpectroscopy and Spectral Analysis, 27
P. Luo, Xueyun Hu, Huai-yu Zhang, Z. Ren (2009)
Genes for resistance to stripe rust on chromosome 2B and their application in wheat breedingProgress in Natural Science, 19
GB Li, SM Zeng, ZQ Li (1989)
Integrated management of wheat pests
S. Liang, H. Fang, Mingzhen Chen (2001)
Atmospheric correction of Landsat ETM+ land surface imagery. I. MethodsIEEE Trans. Geosci. Remote. Sens., 39
C. Champagne, K. Staenz, A. Bannari, H. Mcnairn, J. Deguise (2003)
Validation of a hyperspectral curve-fitting model for the estimation of plant water content of agricultural canopiesRemote Sensing of Environment, 87
P. Goovaerts, G. Jacquez, A. Marcus (2005)
Geostatistical and local cluster analysis of high resolution hyperspectral imagery for detection of anomaliesRemote Sensing of Environment, 95
Y. Qu, Jindi Wang, Huawei Wan, Xiaowen Li, Guoqing Zhou (2008)
A Bayesian network algorithm for retrieving the characterization of land surface vegetationRemote Sensing of Environment, 112
Xuegui Yin, X. Shang, B. Pang, Jian-rong Song, S. Cao, Jinchang Li, Xue-yong Zhang (2006)
Molecular Mapping of Two Novel Stripe Rust Resistant Genes YrTp1 and YrTp2 in A-3 Derived from Triticum aestivum × Thinopyrum ponticumAgricultural Sciences in China, 5
Minghua Zhang, Zhihao Qin, Xue Liu, S. Ustin (2003)
Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensingInternational Journal of Applied Earth Observation and Geoinformation, 4
J. Gamon, L. Serrano, J. Surfus (1997)
The photochemical reflectance index: an optical indicator of photosynthetic radiation use efficiency across species, functional types, and nutrient levelsOecologia, 112
D. Moshou, C. Bravo, J. West, S. Wahlen, A. McCartney, H. Ramon (2004)
Automatic detection of ‘yellow rust’ in wheat using reflectance measurements and neural networksComputers and Electronics in Agriculture, 44
J. Moldenhauer, A. Westhuizen, Z. Pretorius, B. Moerschbacher (2007)
Microscopic studies on stripe rust-infected doubled haploid wheat lines derived from a cross Kariega × Avocet SSouth African Journal of Botany, 73
Liu Liangyun, Song Xiaoyu, Li Cunjun, Qi La, Huang Wenjiang, Wang Jihua (2009)
Monitoring and evaluation of the diseases of and yield winter wheat from multi-temporal remotely-sensed dataTransactions of the Chinese Society of Agricultural Engineering, 25
Ji-hua Wang (2008)
Quantitative identification of stripe rust and common stress on winter wheat:application of hyper-spectrumJournal of Natural Disasters
P. Zarco-Tejadaa, J. Millerb, A. Moralesc, A. Berjóna, J. Agüerad (2004)
Hyperspectral indices and model simulation for chlorophyll estimation in open-canopy tree crops
S. Liang, H. Fang, Mingzhen Chen (2001)
Atmospheric correction of Landsat ETM+ land surface imagery: Part I: Methods, 39
D Hailu, C Fininsa (2007)
Epidemics of stripe rust (Puccinia striiformis) on common wheat (Triticum aestivum) in the highlands of BaleSoutheastern Ethiopia Crop Protection, 26
Y. Qu, Su-hong Liu, Jindi Wang, Peijuan Wang, Xiang Zhao, Yanjuan Yao (2003)
The construction of J2EE-based Spectrum Knowledge Base System for Typical Object in ChinaIGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), 6
(1998)
Key technology of pushbroom hyperspectral imager (PHI)
P. Zarco-Tejada, A. Berjón, R. López‐Lozano, John Miller, Pedro Martín, V. Cachorro, M. González, Á. Frutos (2005)
Assessing vineyard condition with hyperspectral indices: Leaf and canopy reflectance simulation in a row-structured discontinuous canopyRemote Sensing of Environment, 99
In most cases, statistical models for monitoring the disease severity of yellow rust are based on hyperspectral information. The high cost and limited cover of airborne hyperspectral data make it impossible to apply it to large scale monitoring. Furthermore, the established models of disease detection cannot be used for most satellite images either because of the wide range of wavelengths in multispectral images. To resolve this dilemma, this paper presents a novel approach by constructing a spectral knowledge base (SKB) of diseased winter wheat plants, which takes the airborne images as a medium and links the disease severity with band reflectance from environment and disaster reduction small satellite images (HJ-CCD) accordingly. Through a matching process with a SKB, we estimated the disease severity with a disease index (DI) and degrees of disease severity. The proposed approach was validated against both simulated data and field surveyed data. Estimates of DI (%) from simulated data were more accurate, with a coefficient of determination (R 2) of 0.9 and normalized root mean square error (NRMSE) of 0.2. The overall accuracy of classification reached 0.8, with a kappa coefficient of 0.7. Validation of the estimates against field measurements showed that there were some errors in the DI value with the NRMSE close to 0.5. The result of the classification was more encouraging with an overall accuracy of 0.77 and a kappa coefficient of 0.58. For the matching process, Mahalanobis distance performed better than the spectral angle (SA) in all analyses in this study. The potential of SKB for monitoring the incidence and severity of yellow rust is illustrated in this study.
Precision Agriculture – Springer Journals
Published: Dec 18, 2010
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