Identification of Fungal Infection and Nitrogen Deficiency in Wheat Crop using Remote Sensing

Jacobi, J.

doi:10.1109/igarss.2006.174

Cited by 6 publications

(6 citation statements)

References 9 publications

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“…Therefore, VIS/ NIR-vegetation indices have limited potential for early detection of pathogen infections. Apan et al (2004) as well as Jacobi and Kühbauch (2005) confirm this assumption, as they only detected crop infections with the sole use of vegetation indices at severe infection stages. Hence, an analysis of all available data bands is probably more suitable for early detection of crop diseases than the sole use of vegetation indices.…”

Section: Pre-processingsupporting

confidence: 77%

“…A successful citrus pest stress detection, using a linear spectral unmixing method for multispectral and hyper-spectral airborne data was achieved by Du et al (2004). Jacobi and Kühbauch (2005) distinguished between infected and non-infected wheat plots using the Normalized Difference Vegetation Index (NDVI). Detection of soybean anomalies caused by iron chlorosis, via supervised classification analysis of multi-spectral aerial images was done by Shaw and Kelley (2005).…”

Section: Introductionmentioning

confidence: 99%

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Multi-temporal wheat disease detection by multi-spectral remote sensing

2007

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For the implementation of site-specific fungicide applications, the spatiotemporal dynamics of crop diseases must be well known. Remote sensing can be a useful tool to monitor the heterogeneity of crop vitality within agricultural sites. However, the identification of fungal infections at an early growth stage is essential. This study examines the potential of multi-spectral remote sensing for a multi-temporal analysis of crop diseases. Within an experimental field, a 6 ha plot of winter wheat was grown, containing all possible infective stages of the powdery mildew (Blumeria graminis) and leaf rust (Puccinia recondita) pathogens. Three high-resolution remote sensing images were used to execute a spatio-temporal analysis of the infection dynamics. A decision tree, using mixture tuned matched filtering (MTMF) results and the Normalized Difference Vegetation Index (NDVI), was applied to classify the data into areas showing different levels of disease severity. Classification results were compared to ground truth data. The classification accuracy of the first scene was only 56.8%, whereas the scenes from May 28 th and June 20 th achieved considerably higher accuracies of 65.9% and 88.6% respectively. The results showed that high-resolution multi-spectral data are generally suitable to detect in-field heterogeneities of crop vigour but are only moderately suitable for early detection of crop infections.

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Section: Pre-processingsupporting

confidence: 77%

Section: Introductionmentioning

confidence: 99%

Multi-temporal wheat disease detection by multi-spectral remote sensing

2007

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“…Therefore, when the ground is totally covered, the information that NDVI provides is more limited, whereas RGB-indices have proved to be even better predictors with dense canopies. NDVI was previously employed to successfully distinguish between infected, non-infected and N-deficient wheat plots [47]. However, it was mostly used in combined multi-spectral methods with other spectral indices where NDVI acted as a first level biomass sensor in order to discard non-plant spectra [31,32,48] and subsequently the analysis proceeded with the use of other indices.…”

Section: Performance Of Vegetation Indicesmentioning

confidence: 99%

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

et al. 2015

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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 severity.RGB-based indices proved to be accurate predictors of grain yield and grain yield losses associated with yellow rust (R 2 = 0.581 and R 2 = 0.536, respectively), far surpassing the predictive ability of NDVI (R 2 = 0.118 and R 2 = 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.(http://creativecommons.org/licenses/by-nc-nd/4.0/). Keywords:Wheat yellow rust Field phenotyping NDVI Phenology, Puccinia striiformis RGB-based indices Triticum durumT H E C R O P J O U R N A L 3 ( 2 0 1 5 ) 2 0 0 -2 1 0

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“…Hence, a sensorbased detection and mapping of crop stresses is fundamental. Remote sensing may be an appropriate tool to detect the heterogeneity of crops vitality within agricultural sites, which was already demonstrated in previous studies 2, 3,4,5,6,7,8,9,10,11 . This study focuses on the detection of fungal infection in wheat by the use of multispectral remote sensing data.…”

Section: Introductionmentioning

confidence: 77%

Detection of fungal infection in wheat with high-resolution multispectral data

Franke¹,

Menz²

2006

Remote Sensing and Modeling of Ecosystems for Sustainability III

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The exact knowledge of the spatiotemporal dynamics of crop diseases for an implementation of a site-specific fungicide application is fundamental. Remote sensing is an appropriate tool to monitor the heterogeneity of fungal diseases within agricultural sites. However, the identification of an infection at an early growth stage is essential. This study assesses the potential of multispectral remote sensing for multitemporal analyses of crop diseases. Within an experimental test site near Bonn (Germany) a 6-ha sized plot with winter wheat was created, containing crops with each possible infection stage of three different pathogens. Two multispectral QuickBird images (04/22/2005 and 06/20/2005) and a spectrally resampled HyMap image (05/28/2005) were used to analyse the spatiotemporal dynamic of infection. The data preprocessing comprised a radiometric and a precise geometric correction by using DGPS-measurements that is an important requirement for Precision Agriculture applications. Ground truth data, in particular infection severity, growth stage/height, and spectroradiometer measurements were collected. A decision tree, using mixture tuned matched filtering results and a vegetation index was applied to classify the data (infected and non-infected areas). Classification results were compared to ground truth data. The classification accuracy of the first scene was only 56.8% whereas the scene of 28 May (65.9%) and the scene of 20 June (88.6%) achieved considerably higher accuracies. The results showed that high-resolution multispectral data are generally suitable to detect in-field heterogeneities of vegetation vitality though they are only moderately suitable for early detection of stress factors.

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Identification of Fungal Infection and Nitrogen Deficiency in Wheat Crop using Remote Sensing

Cited by 6 publications

References 9 publications

Multi-temporal wheat disease detection by multi-spectral remote sensing

Multi-temporal wheat disease detection by multi-spectral remote sensing

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

Detection of fungal infection in wheat with high-resolution multispectral data

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