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

Abstract

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.

Reflectance-based identification of parasitized host eggs and adult Trichogramma specimens

“…Compared with classical taxonomy (under the microscope) or molecular-based classification of minute and closely related animals, plant seeds and growing plants, a reflectance-based method may be of considerable relevance to a wide range of biological studies. There are numerous approaches to classification of hyperspectral imaging data, and only recently has the approach (a combination of variogram analysis and linear discriminant analysis) used in this study been described and successfully applied (Nansen, 2012;Nansen et al, 2010a;Nansen et al, 2009;Nansen et al, 2014). However, this analysis represents the first application of this classification method to the identification of animals.…”

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

“…This low spider mite infestation level was intentional, as the objective was to test variogram based analysis on a challenging model system, and variogram parameters did respond significantly despite the fact that analysis of average reflectance values in the same spectral bands yielded no consistent trends. A recent study involving experimental manipulation of reflectance data (adding 2.5% or 5.0% to reflectance values in all or random subsets of pixels) showed that increase in reflectance especially affected sill values and to some extent also effected nugget values but had negligible effect on range values [24]. Thus, if mainly sill values respond to increases in reflectance values, it is not surprising that this study showed negligible increase in average reflectance and non-consistent response by sill values to the 2 stressors.…”

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

“…Similar to methodology used in previously published studies (Nansen et al, 2010a;Nansen et al, 2010b), we used a hyperspectral spectral camera (PIKA II, Resonon Inc., Bozeman, MT) mounted 40 cm above target objects (field peas). The main specifications of the spectral camera are as follows: interface, Firewire (IEEE 1394b); output, digital (12 bit); 240 bands from 392 to 889 nm (spectral resolution = 2.1 nm) (spectral) by 640 pixels (spatial); angular field of view of 7°.…”

“…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.…”

“…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.…”