Russell Kincaid

Correlation and classification of single kernel fluorescence hyperspectral data with aflatoxin concentration in corn kernels inoculated with Aspergillus flavus spores.

The objective of this study was to examine the relationship between fluorescence emissions of corn kernels inoculated with Aspergillus flavus and aflatoxin contamination levels within the kernels. Aflatoxin contamination in corn has been a long-standing problem plaguing the grain industry with potentially devastating consequences to corn growers. In this study, aflatoxin-contaminated corn kernels were produced through artificial inoculation of corn ears in the field with toxigenic A. flavus spores. The kernel fluorescence emission data were taken with a fluorescence hyperspectral imaging system when corn kernels were excited with ultraviolet light. Raw fluorescence image data were preprocessed and regions of interest in each image were created for all kernels. The regions of interest were used to extract spectral signatures and statistical information. The aflatoxin contamination level of single corn kernels was then chemically measured using affinity column chromatography. A fluorescence peak shift phenomenon was noted among different groups of kernels with different aflatoxin contamination levels. The fluorescence peak shift was found to move more toward the longer wavelength in the blue region for the highly contaminated kernels and toward the shorter wavelengths for the clean kernels. Highly contaminated kernels were also found to have a lower fluorescence peak magnitude compared with the less contaminated kernels. It was also noted that a general negative correlation exists between measured aflatoxin and the fluorescence image bands in the blue and green regions. The correlation coefficients of determination, r 2, was 0.72 for the multiple linear regression model. The multivariate analysis of variance found that the fluorescence means of four aflatoxin groups, <1, 1–20, 20–100, and ≥100 ng g−1 (parts per billion), were significantly different from each other at the 0.01 level of alpha. Classification accuracy under a two-class schema ranged from 0.84 to 0.91 when a threshold of either 20 or 100 ng g−1 was used. Overall, the results indicate that fluorescence hyperspectral imaging may be applicable in estimating aflatoxin content in individual corn kernels.

Remote Sensing of Soil Moisture Using Airborne Hyperspectral Data

Landscape assessment of soil moisture is critical to understanding the hydrological cycle at the regional scale and in broad-scale studies of biophysical processes affected by global climate changes in temperature and precipitation. Traditional efforts to measure soil moisture have been principally restricted to in situ measurements, so remote sensing techniques are often employed. Hyperspectral sensors with finer spatial resolution and narrow band widths may offer an alternative to traditional multispectral analysis of soil moisture, particularly in landscapes with high spatial heterogeneity. This preliminary research evaluates the ability of remotely sensed hyperspectral data to quantify soil moisture for the Little River Experimental Watershed (LREW), Georgia. An airborne hyperspectral instrument with a short-wavelength infrared (SWIR) sensor was flown in 2005 and 2007 and the results were correlated to in situ soil moisture values. A significant statistical correlation (R 2 value above 0.7 for both sampling dates) for the hyperspectral instrument data and the soil moisture probe data at 5.08 cm (2 inches) was determined. While models for the 20.32 cm (8 inches) and 30.48 cm (12 inches) depths were tested, they were not able to estimate soil moisture to the same degree.

Co-inoculation of aflatoxigenic and non-aflatoxigenic strains of Aspergillus flavus to study fungal invasion, colonization, and competition in maize kernels

A currently utilized pre-harvest biocontrol method involves field inoculations with non-aflatoxigenic Aspergillus flavus strains, a tactic shown to strategically suppress native aflatoxin-producing strains and effectively decrease aflatoxin contamination in corn. The present in situ study focuses on tracking the invasion and colonization of an aflatoxigenic A. flavus strain (AF70), labeled with green fluorescent protein (GFP), in the presence of a non-aflatoxigenic A. flavus biocontrol strain (AF36), to better understand the competitive interaction between these two strains in seed tissue of corn (Zea mays). Corn kernels that had been co-inoculated with GFP-labeled AF70 and wild-type AF36 were cross-sectioned and observed under UV and blue light to determine the outcome of competition between these strains. After imaging, all kernels were analyzed for aflatoxin levels. There appeared to be a population difference between the co-inoculated AF70-GFP+AF36 and the individual AF70-GFP tests, both visually and with pixel count analysis. The GFP allowed us to observe that AF70-GFP inside the kernels was suppressed up to 82% when co-inoculated with AF36 indicating that AF36 inhibited progression of AF70-GFP. This was in agreement with images taken of whole kernels where AF36 exhibited a more robust external growth compared to AF70-GFP. The suppressed growth of AF70-GFP was reflected in a corresponding (upto 73%) suppression in aflatoxin levels. Our results indicate that the decrease in aflatoxin production correlated with population depression of the aflatoxigenic fungus by the biocontrol strain supporting the theory of competitive exclusion through robust propagation and fast colonization by the non-aflatoxigenic fungus.

Spectral Angle Mapper classification of fluorescence hyperspectral image for aflatoxin contaminated corn

Aflatoxin contamination in corn is a serious problem for both producers and consumers. The present study applied the Spectral Angle Mapper classification technique to classify single corn kernels into contaminated and healthy groups. Fluorescence hyperspectral images were used in the classification. Actual corn aflatoxin concentration was chemically determined using the VICAM analytical method for quantification purpose. An obvious fluorescence peak shift was observed to be associated with the aflatoxin contaminated corn. Aflatoxin classification levels were based on Food and Drug Administrations regulation, including 20 ppb (parts per billion) for human consumption and 100 ppb for feed. Classification accuracy for the 20 ppb level is 86% with a false positive rate of 15%. For the 100 ppb level, the accuracy is 88% with a false positive rate of 16%. The results indicate that the Spectral Angle Mapper method and fluorescence hyperspectral imagery have the potential to classify aflatoxin contaminated corn kernels.

Automatic detection of aflatoxin contaminated corn kernels using dual-band imagery

Aflatoxin is a mycotoxin predominantly produced by Aspergillus flavus and Aspergillus parasitiucus fungi that grow naturally in corn, peanuts and in a wide variety of other grain products. Corn, like other grains is used as food for human and feed for animal consumption. It is known that aflatoxin is carcinogenic; therefore, ingestion of corn infected with the toxin can lead to very serious health problems such as liver damage if the level of the contamination is high. The US Food and Drug Administration (FDA) has strict guidelines for permissible levels in the grain products for both humans and animals. The conventional approach used to determine these contamination levels is one of the destructive and invasive methods that require corn kernels to be ground and then chemically analyzed. Unfortunately, each of the analytical methods can take several hours depending on the quantity, to yield a result. The development of high spectral and spatial resolution imaging sensors has created an opportunity for hyperspectral image analysis to be employed for aflatoxin detection. However, this brings about a high dimensionality problem as a setback. In this paper, we propose a technique that automatically detects aflatoxin contaminated corn kernels by using dual-band imagery. The method exploits the fluorescence emission spectra from corn kernels captured under 365 nm ultra-violet light excitation. Our approach could lead to a non-destructive and non-invasive way of quantifying the levels of aflatoxin contamination. The preliminary results shown here, demonstrate the potential of our technique for aflatoxin detection.

Detecting peanuts inoculated with toxigenic and atoxienic Aspergillus flavus strains with fluorescence hyperspectral imagery

Aflatoxin contamination in peanut products has been an important and long-standing problem around the world. Produced mainly by Aspergillus flavus and Aspergillus parasiticus, aflatoxins are the most toxic and carcinogenic compounds among toxins. This study investigated the application of fluorescence visible near-infrared (VNIR) hyperspectral images to assess the spectral difference between peanut kernels inoculated with toxigenic and atoxigenic inocula of A. flavus and healthy kernels. Peanut kernels were inoculated with NRRL3357, a toxigenic strain of A. flavus, and AF36, an atoxigenic strain of A. flavus, respectively. Fluorescence hyperspectral images under ultraviolet (UV) excitation were recorded on peanut kernels with and without skin. Contaminated kernels exhibited different fluorescence features compared with healthy kernels. For the kernels without skin, the inoculated kernels had a fluorescence peaks shifted to longer wavelengths with lower intensity than healthy kernels. In addition, the fluorescence intensity of peanuts without skin was higher than that of peanuts with skin (10 times). The fluorescence spectra of kernels with skin are significantly different from that of the control group (p<0.001). Furthermore, the fluorescence intensity of the toxigenic, AF3357 peanuts with skin was lower than that of the atoxigenic AF36 group. Discriminate analysis showed that the inoculation group can be separated from the controls with 100% accuracy. However, the two inoculation groups (AF3357 vis AF36) can be separated with only ～80% accuracy. This study demonstrated the potential of fluorescence hyperspectral imaging techniques for screening of peanut kernels contaminated with A. flavus, which could potentially lead to the production of rapid and non-destructive scanning-based detection technology for the peanut industry.

Selective principal component regression analysis of fluorescence hyperspectral image to assess aflatoxin contamination in corn

Selective principal component regression analysis (SPCR) uses a subset of the original image bands for principal component transformation and regression. For optimal band selection before the transformation, this paper used genetic algorithms (GA). In this case, the GA process used the regression correlation as its fitness function. This algorithm was used for analyzing fluorescence hyperspectral images of aflatoxin contaminated corn kernels. The results showed that SPCR could produce results similar to the standard PCR approach. However, the data dimension was much less for the SPCR process. The SPCR correlation coefficient was 0.8 when 33 of the original 74 bands were used for the SPCR transformation. The results demonstrated that SPCR could be used as a combined dimension reduction and data analysis tool for high dimensionality data processing.

Development of narrow-band fluorescence index for the detection of aflatoxin contaminated corn

Aflatoxin is produced by the fungus Aspergillus flavus when the fungus invades developing corn kernels. Because of its potent toxicity, the levels of aflatoxin are regulated by the Food and Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food, and feed intended for interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests. These tests require the destruction of samples, can be costly and time consuming, and often rely on less than desirable sampling techniques. Thus, the ability to detect aflatoxin in a rapid, non-invasive way is crucial to the corn industry in particular. This paper described how narrow-band fluorescence indices were developed for aflatoxin contamination detection based on single corn kernel samples. The indices were based on two bands extracted from full wavelength fluorescence hyperspectral imagery. The two band results were later applied to two large sample experiments with 25 g and 1 kg of corn per sample. The detection accuracies were 85% and 95% when 100 ppb threshold was used. Since the data acquisition period is significantly lower for several image bands than for full wavelength hyperspectral data, this study would be helpful in the development of real-time detection instrumentation for the corn industry.

Investigation on the Potential of using ASTER Image for Corn Plant Residue Coverage Estimation in Three Indiana Counties

The purpose of this study was to investigate the use of remotely sensed imagery from the NASA Earth Science Enterprise (ESE) suite of satellite sensors to map crop residue and tillage practices. The U.S. Department of Agricultures (USDA) Natural Resource Conservation Service (NRCS) is doing biennial national residue mapping. Due to the high costs involved, only a small number of fields could be surveyed. Also the methodology is subjective. Using remotely sensed imagery may help to reduce the costs, allow many more fields to be used in the program, as well as potentially increase accuracy. This investigation used the Advanced Spaceborne Thermal Emission and Reflection Radiometer sensor (ASTER) for large area crop residue mapping. Residue covers from 421 corn fields within three Indiana counties were estimated following a windshield survey approach. The field level residue covers were divided into three levels: fields with >30% residue cover were classified as conservation tillage (no till); fields with 16-30% residue cover as reduced tillage; and fields with <15% residue cover as conventional tillage. Field mean spectral reflectance was extracted and several indices were calculated. The field level spectra and indices were then used in a discriminant analysis process. The results indicated that the ASTER Normalized Shortwave Index (ANSI) performed the best in separating no till from the combined conventional and reduced till fields. The average accuracy was 85.5% for all three counties with Huntington County having the best accuracy at 91%. In addition, pixel based supervised classifications were also implemented. The results showed that the shortwave near-infrared (SWIR) bands produced better classification accuracies than the visible near-infrared (VNIR) bands. While the use of ASTER images for corn residue mapping shows its potential, prior knowledge of the previous crop type is needed for successful operations.

Classification of corn kernels contaminated with aflatoxins using fluorescence and reflectance hyperspectral images analysis

Aflatoxins are secondary metabolites produced by certain fungal species of the Aspergillus genus. Aflatoxin contamination remains a problem in agricultural products due to its toxic and carcinogenic properties. Conventional chemical methods for aflatoxin detection are time-consuming and destructive. This study employed fluorescence and reflectance visible near-infrared (VNIR) hyperspectral images to classify aflatoxin contaminated corn kernels rapidly and non-destructively. Corn ears were artificially inoculated in the field with toxigenic A. flavus spores at the early dough stage of kernel development. After harvest, a total of 300 kernels were collected from the inoculated ears. Fluorescence hyperspectral imagery with UV excitation and reflectance hyperspectral imagery with halogen illumination were acquired on both endosperm and germ sides of kernels. All kernels were then subjected to chemical analysis individually to determine aflatoxin concentrations. A region of interest (ROI) was created for each kernel to extract averaged spectra. Compared with healthy kernels, fluorescence spectral peaks for contaminated kernels shifted to longer wavelengths with lower intensity, and reflectance values for contaminated kernels were lower with a different spectral shape in 700-800 nm region. Principal component analysis was applied for data compression before classifying kernels into contaminated and healthy based on a 20 ppb threshold utilizing the K-nearest neighbors algorithm. The best overall accuracy achieved was 92.67% for germ side in the fluorescence data analysis. The germ side generally performed better than endosperm side. Fluorescence and reflectance image data achieved similar accuracy.