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Impact of spectral interval on wavelet features for detecting wheat yellow rust with hyperspectral data

International Journal of Agricultural and Biological Engineering 2018年第6期11卷 138-144页

作者： Jingcheng Zhang Bin Wang Xuexue Zhang Peng Liu Yingying Dong Kaihua Wu Wenjiang Huang College of Life Information Science and Instrument Engineering Hangzhou Dianzi UniversityHangzhou 310018China Key laboratory of Digital Earth Science Institute of Remote Sensing and Digital EarthChinese Academy of SciencesBeijing 100094 China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China

Detection of yellow rust using hyperspectral data is of practical importance for disease control and *** an emerging spectral analysis method,continuous wavelet analysis(CWA)has shown great potential for the detection of plant diseases and *** the spectral interval of airborne or spaceborne hyperspectral sensor data differ greatly,it is important to understand the impact of spectral interval on the performance of CWA in detecting yellow rust in winter wheat.A field experiment was conducted which obtained spectral measurements of both healthy and disease-infected *** impacts of the mother wavelet type and spectral interval on disease detection were *** results showed that spectral features derived from all four mother wavelet types exhibited sufficient sensitivity to the occurrence of yellow *** Mexh wavelet slightly outperformed the others in estimating disease *** the detecting accuracy generally declined with decreasing of spectral interval,relatively high accuracy levels were maintained(R^(2)>0.7)until a spectral interval of 16 ***,it is recommended that the spectral interval of hyperspectral data should be no larger than 16 nm for the detection of yellow *** relatively loose spectral interval requirement permits extensive applications for disease detection with hyperspectral imagery.

关键词： continuous wavelet analysis spectral interval hyperspectral data wheat yellow rust

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Grain yield losses in yellow-rusted durum wheat estimated using digital and conventional parameters under field conditions

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The Crop Journal 2015年第3期3卷 200-210页

作者： Omar Vergara-Diaz Shawn C.Kefauver Abdelhalim Elazab Maria Teresa Nieto-Taladriz Jose Luis Araus Unit of Plant Physiology Department of Plant BiologyFaculty of BiologyUniversity of BarcelonaDiagonal 64508028 BarcelonaSpain National Institute for Agricultural and Food Research and Technology(INIA) Ctra de la Coruna 7.528040Madrid Spain

The biotrophic fungus Puccinia striiformis f. sp. tritici is the causal agent of the yellow rust in wheat. Between the years 2010–2013 a new strain of this pathogen(Warrior/Ambition),against which the present cultivated wheat varieties have no resistance, appeared and spread rapidly. It threatens cereal production in most of Europe. The search for sources of resistance to this strain is proposed as the most efficient and safe solution to ensure high grain production. This will be helped by the development of high performance and low cost techniques for field phenotyping. In this study we analyzed vegetation indices in the Red,Green, Blue(RGB) images of crop canopies under field conditions. We evaluated their accuracy in predicting grain yield and assessing disease severity in comparison to other field measurements including the Normalized Difference Vegetation Index(NDVI), leaf chlorophyll content, stomatal conductance, and canopy temperature. We also discuss yield components and agronomic parameters in relation to grain yield and disease ***-based indices proved to be accurate predictors of grain yield and grain yield losses associated with yellow rust(R2= 0.581 and R2= 0.536, respectively), far surpassing the predictive ability of NDVI(R2= 0.118 and R2= 0.128, respectively). In comparison to potential yield, we found the presence of disease to be correlated with reductions in the number of grains per spike, grains per square meter, kernel weight and harvest index. Grain yield losses in the presence of yellow rust were also greater in later heading varieties. The combination of RGB-based indices and days to heading together explained 70.9% of the variability in grain yield and 62.7% of the yield losses.

关键词： wheat yellow rust Field phenotyping NDVI Phenology,Puccinia striiformis RGB-based indices Triticum durum

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