Use of spatial structure analysis of hyperspectral data cubes for detection of insect‐induced stress in wheat plants

Nansen, Christian; Macedo, Tulio B.; Swanson, Rand; Weaver, David K.

doi:10.1080/01431160802552710

Cited by 39 publications

(35 citation statements)

References 27 publications

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“…Published studies suggest that the spatial structure of reflectance data within a given spectral band may be more consistent over space and time (and therefore less susceptible to radiometric stochasticity) than absolute reflectance values [51]. The same linear multi-regression approach applied to FTIR vibration peaks was also applied to average reflectance peaks and variogram parameters (both derived from HI data in five selected spectral bands: 664, 683, 706, 740, and 747 nm).…”

Section: Discussionmentioning

confidence: 99%

Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bioactivity of Surface Pesticide Treatment

et al. 2010

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Many food products are subjected to quality control analyses for detection of surface residue/contaminants, and there is a trend of requiring more and more documentation and reporting by farmers regarding their use of pesticides. Recent outbreaks of food borne illnesses have been a major contributor to this trend. With a growing need for food safety measures and -smart applications‖ of insecticides, it is important to develop methods for rapid and accurate assessments of surface residues on food and feed items. As a model system, we investigated detection of a miticide applied to maize leaves and its miticidal bioactivity over time, and we compared two types of reflectance data: fourier transformed infrared (FTIR) data and hyperspectral imaging (HI) data. The miticide (bifenazate) was applied at a commercial field rate to maize leaves in the field, with or without application of a surfactant, and with or without application of a simulated -rain event‖. In addition, we collected FTIR and HI from untreated control leaves (total of five treatments). Maize leaf data were collected at seven time intervals from 0 to 48 hours after application. FTIR data were analyzed using conventional analysis of variance of miticide-specific vibration peaks. Two unique FTIR vibration peaks were associated with miticide application (1,700 cm −1 and 763 cm −1 ). The integrated intensities of these two OPEN ACCESSRemote Sensing 2010, 2 909 peaks, miticide application, surfactant, rain event, time between miticide application, and rain event were used as explanatory variables in a linear multi-regression fit to spider mite mortality. The same linear multi-regression approach was applied to variogram parameters derived from HI data in five selected spectral bands (664, 683, 706, 740, and 747 nm). For each spectral band, we conducted a spatial structure analysis, and the three standard variogram parameters (-sill‖, -range‖, and -nugget‖) were examined as possible -indicators‖ of miticide bioactivity. We demonstrated that both FTIR peaks and standard variogram parameters could be used to accurately predict spider mite mortality, but linear multi-regression fits based on standard variogram parameters had the highest accuracy and were successfully validated with independent data. Based on experimental manipulation of HI data, the use of spatial structure analysis in classification of HI data was discussed.

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Section: Discussionmentioning

confidence: 99%

Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bioactivity of Surface Pesticide Treatment

et al. 2010

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“…Spatial structure analysis based on geostatistics (variogram analysis) is considered one of the most powerful and robust approaches to spatial data analysis (Isaaks and Srivastava, 1989), and recent studies have shown how variogram parameters derived from high-resolution reflectance data can be used to detect different traits in a range of target objects (Nansen, 2011(Nansen, , 2012Nansen et al, 2010a;Nansen et al, 2010b;Nansen et al, 2009;Nansen et al, 2010c). In the variogram analysis (PROC VARIOGRAM) of reflectance data at 782 nm, we used the following variogram settings: (1) lag distances = 1, and (2) number of lag intervals = 10.…”

Section: Data Processing and Analysismentioning

confidence: 99%

“…Consequently, it is possible to characterize the spatial structure of reflectance values in a single spectral band (using variogram analysis) and determine to what extent a given quality trait of the target object is associated with a particular spatial data structure. While use of variogram analysis is well-described in geosciences, landscape ecology, oceanography, and other large-scale life science applications, it is only recently that this approach has been applied to high-resolution hyperspectral imaging data (Nansen, 2011(Nansen, , 2012Nansen et al, 2010a;Nansen et al, 2009). The assumption behind variogram-based analysis is that the spatial structure of reflectance values in individual spectral bands can be described by three fitted variogram parameters, and that these variogram parameters vary significantly among classes of target objects.…”

Section: Introductionmentioning

confidence: 99%

Use of variogram analysis to classify field peas with and without internal defects caused by weevil infestation

Nansen

Zhang

Aryamanesh

et al. 2014

Journal of Food Engineering

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In this study, we acquired 72 (training data) and 30 (independent validation) high-spatial resolution (7 by 7 pixels per mm 2 ) hyperspectral imaging data [240 spectral bands from 392 to 889 nm (spectral resolution = 2.1 nm)] from samples of field peas (Pisum sativum) with and without pea weevil (Bruchus pisorum) infestation. The reflectance data were analyzed with linear discriminant analysis (LDA) or either reflectance values only or of a combination of reflectance values and variogram parameters (derived from variogram analysis) from a single spectral band (782 nm). All examined classification models were assessed based on sensitivity (ability to positively detect infestation), specificity (ability to positively detect noninfestation), and accurate classification of 30 samples of independent validation data. Highest classification performance was obtained with a combination of reflectance values in two spectral bands (641 nm and 868 nm) and variogram parameters derived from 782 nm. This classification model was associated with a sensitivity of 94.7% and a specificity of 100%. In addition, all 30 independent validation data were accurately classified (100%). For comparison, traditional linear discriminant analyses of 108,351 reflectance profiles from individual pixels in the 72 samples or average reflectance profiles from the 72 samples classified the validation data with 84.0% and 83.3% accuracy, respectively. We are unaware of any reflectance-based classification system, which -based on reflectance data acquired in only three channels (spectral bands) -can provide this level of sensitivity and specificity and classification of independent validation data of internal defects in food products. Accurate and reliable classification of food objects based reflectance values in only a few spectral bands would likely imply low computer processing requirements and rapid data analysis. Thus, we believe that the current classification method may be useful for quality control systems of a wide range of food products.

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“…There are numerous studies of stress detection in crop leaves based on reflectance data acquired with either single sensor devices or imaging devices, including: biotic stress [1][2][3][4], salinity stress [5], nutrient deficiency [6], and drought stress [3,7]. Carter and Knapp [8] provided a thorough review of reflectance based detection of abiotic and biotic stressors (including dehydration, flooding, freezing, ozone, herbicides, competition, disease, insects, and deficiencies in ectomycorrhizal development and N fertilization) within the 400-850 nm wavelength range when imposed on a wide range of plant species (grasses, conifers, and deciduous trees).…”

Section: Introductionmentioning

confidence: 99%

“…In other words, the true potential of reflectance based detection is associated with an ability to detect subtle/emerging stress levels, so that management practices can be adjusted before significant crop yield losses have occurred. In addition, several studies have described reflectance based detection of emerging spider mite infestations [1,3,4].…”

Section: Introductionmentioning

confidence: 99%

Use of Variogram Parameters in Analysis of Hyperspectral Imaging Data Acquired from Dual-Stressed Crop Leaves

Nansen

2012

Remote Sensing

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Abstract:A detailed introduction to variogram analysis of reflectance data is provided, and variogram parameters (nugget, sill, and range values) were examined as possible indicators of abiotic (irrigation regime) and biotic (spider mite infestation) stressors. Reflectance data was acquired from 2 maize hybrids (Zea mays L.) at multiple time points in 2 data sets (229 hyperspectral images), and data from 160 individual spectral bands in the spectrum from 405 to 907 nm were analyzed. Based on 480 analyses of variance (160 spectral bands × 3 variogram parameters), it was seen that most of the combinations of spectral bands and variogram parameters were unsuitable as stress indicators mainly because of significant difference between the 2 data sets. However, several combinations of spectral bands and variogram parameters (especially nugget values) could be considered unique indicators of either abiotic or biotic stress. Furthermore, nugget values at 683 and 775 nm responded significantly to abiotic stress, and nugget values at 731 nm and range values at 715 nm responded significantly to biotic stress. Based on qualitative characterization of actual hyperspectral images, it was seen that even subtle changes in spatial patterns of reflectance values can elicit several-fold changes in variogram parameters despite non-significant changes in average and median reflectance values and in width of 95% confidence limits. Such scattered stress expression is in accordance with documented within-leaf variation in both mineral content and chlorophyll concentration and therefore supports the need for reflectance-based stress detection at a high spatial resolution (many hyperspectral reflectance profiles acquired from a single leaf) and may be used to explain or characterize within-leaf foraging patterns of herbivorous arthropods.

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Use of spatial structure analysis of hyperspectral data cubes for detection of insect‐induced stress in wheat plants

Cited by 39 publications

References 27 publications

Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bioactivity of Surface Pesticide Treatment

Using Spatial Structure Analysis of Hyperspectral Imaging Data and Fourier Transformed Infrared Analysis to Determine Bioactivity of Surface Pesticide Treatment

Use of variogram analysis to classify field peas with and without internal defects caused by weevil infestation

Use of Variogram Parameters in Analysis of Hyperspectral Imaging Data Acquired from Dual-Stressed Crop Leaves

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