Aflatoxins are among the most carcinogenic mycotoxins and are known to contaminate a wide variety of agricultural and food commodities. This study aims to explore the effectiveness of Raman hyperspectral imaging in detecting aflatoxin contamination in corn kernels in a rapid and non-destructive manner. Four hundred kernels were used with 2 treatments, namely, 200 kernels inoculated with the AF13 fungus (aflatoxigenic), and 200 kernels inoculated with sterile distilled water as control. One hundred kernels from each treatment were incubated at 30 °C for 5 and 8 days, separately. On the specified post-inoculation day, the kernels were dried and wiped free of surface mold prior to imaging. The Raman images of kernels were acquired over the endosperm side over the 103-2831 cm-1 wavenumber range. The standard aflatoxin concentration in each kernel was determined by the VICAM AflaTest method. The original mean spectra of single kernels were extracted and preprocessed by adaptive iteratively reweighted penalized least squares, Savitzky Golay smoothing and min-max normalization. On basis of the calculated “reference” mean spectra of the aflatoxin negative and -positive categories, 14 and 17 local peaks were determined, separately. After removing the identical peaks from both peak sets, a total of 24 unique peaks were extracted and used as inputs for further discriminant model development. With 20 ppb and 100 ppb as the classification thresholds, the 2-class discriminant models established with the principal component analysis-linear discriminant analysis and partial least-squares discriminant analysis methods, obtained mean overall prediction accuracies between 77.9% and 82.0%. Further investigation is ongoing to include more diverse samples and execute different types of computation algorithms, seeking solutions to improve the discriminant models in identifying aflatoxin contamination in corn kernels.
The potential of near infrared hyperspectral imaging over the spectral range of 900 - 2500 nm was investigated for identification of aflatoxin contamination on corn kernels. A total of 600 kernels were used with 3 treatments, namely, 200 kernels inoculated with the AF13 fungus (aflatoxigenic), 200 kernels inoculated with the AF36 fungus (nonaflatoxigenic), and 200 kernels inoculated with sterile distilled water as control. One hundred kernels from each treatment were incubated at 30 °C for 5 and 8 days, separately, and then the kernels were dried and surface wiped to remove exterior signs of mold prior to imaging. The mean spectra including mean reflectance and absorbance, and the textural features consisting of contrast, correlation, energy and homogeneity, were extracted separately from the endosperm regions of single kernels. The partial least-squares discriminant analysis (PLS-DA) models were established using extracted mean spectra or textural features as individual inputs. The full spectral PLS-DA modeling results indicate that the mean spectra including both reflectance and absorbance spectra performed significantly better than using the textural features in identifying aflatoxin contamination on corn kernels. Using the mean reflectance and absorbance spectra between 925 and 2484 nm, the full spectral PLS-DA models achieved mean overall prediction accuracies of 88.3% and 86.3% when taking 20 ppb as the classification threshold. The corresponding means of overall prediction accuracies were 85.5% and 85.6% when 100 ppb was applied as the classification threshold. The extracted textural features were not found to be useful in identifying aflatoxin contamination.
The potential of line-scan hyperspectral Raman imaging system equipped with a 785 nm line laser was examined for discrimination of healthy, AF36-inoculated and AF13-inoculated corn kernels in this study. The AF36 and AF13 strains were used as representatives for the aflatoxigenic and non-aflatoxigenic A. flavus fungal varieties. A total of 300 kernels were used with 3 treatments, namely, 100 kernels inoculated with the AF13 fungus, 100 kernels inoculated with the AF36 fungus, and 100 kernels inoculated with sterile distilled water as control. The kernels were all incubated at 30 °C for 8 days and then dried and surface wiped to remove exterior signs of mold. The kernels were imaged from endosperm side over the wavenumber range of 103-2831 cm-1. The mean spectrum was extracted from the Raman image of each kernel, and preprocessed with adaptive iteratively reweighted penalized least squares, Savitzky-Golay smoothing and min-max normalization. Based upon the preprocessed group mean spectra, a total of 35 local Raman peaks were identified. With the spectral variables at the identified local peak locations as inputs of discriminant models, the 3-class principal component analysis-linear discriminant analysis (PCA-LDA) models ran 20 random times, achieved a mean overall prediction accuracy of 91.13% along with a standard deviation value of 3.36%.
The potential of near infrared (NIR) hyperspectral imaging over the 900-2500 nm spectral range was examined for discrimination of artificially-inoculated corn kernels with aflatoxigenic and non-aflatoxigenic strains of Aspergillus flavus in this study. The two A. flavus strains, aflatoxigenic AF13 and non-aflatoxigenic AF36 were used for inoculation on corn kernels. Four treatments were included, with each treatment consisting of 100 kernels. Each treatment of 100 kernels were artificially inoculated with AF13 or AF36 strain and incubated at 30 °C for 3 and 8 days, separately. The mean spectra were extracted from the collected NIR hyperspectral images for individual corn kernels, and then based on the mean spectra, the principal component analysis combined with linear discriminant analysis (PCA-LDA) method was employed to establish the classification models. The pairwise classification models were established by the PCA-LDA method to discriminate the AF36-inoculated and the AF13-inoculated kernels at different incubation days. All the overall accuracies obtained by the pairwise models were ≥98.0%. A common model that takes the AF13-inoculated kernels at different incubation days as one class and the AF36-inoculated kernels at different incubation days as the second class, achieved an overall accuracy of 99.0% for the prediction samples. This indicates a great potential of using NIR hyperspectral imaging to classify corn kernels infected by aflatoxigenic and non-aflatoxigenic A. flavus regardless of infection time.
Aflatoxins are fungal toxins produced by Aspergillus flavus. Food and feed crops get contaminated with carcinogenic aflatoxins, which often results in economic losses as well as serious health issues. Grain elevators need to unload, on average, one 50,000-pound truckload every two minutes. Current chemical and optical methods for aflatoxin detection cannot meet the screening requirements. Therefore, a high speed batch screening system with reliable accuracy is necessary. The contaminated corn kernels were prepared in our laboratory by artificial inoculation of corn ears. One hundred 200g samples were selected for analysis. To develop a high speed multispectral screening system, two high performance cameras in conjunction with dual UV excitation sources and novel image processing software were utilized to collect fluorescence images of each sample. Each camera simultaneously captures a single band fluorescence image (436 nm and 532 nm) from corn samples, and the detection software processes the images to automatically detect contaminated kernels by using a normalized difference fluorescence index. Each sample was imaged/screened four times, and screened samples were chemically analyzed for aflatoxin content. All samples were shuffled between imaging repetitions to increase the likelihood of screening both sides of every kernel. Processing time for each screening was about 0.7s, and an optimal result of 98.65% was achieved for sensitivity and 96.6% for specificity.
Aflatoxin contamination can occur in a wide variety of agricultural products pre- and post-harvest, posing potential severe health hazards to human and livestock. However, current methods for detecting aflatoxins are generally based on wet chemical analyses, which are time-consuming, destructive to test samples and require skilled personnel to perform, making them impossible for large-scale non-destructive screening and on-site detection. In this study, we utilized the visible/near-infrared (Vis/NIR) spectroscopy over the spectral range of 400-2500 nm to detect contamination of shelled commercial peanut kernels with the predominant aflatoxin B1 (AFB1). Our results indicated the usefulness of Vis/NIR spectroscopy combined with the chemometric techniques of partial least squares discriminant analysis (PLS-DA) and least squares support vector machine (LS-SVM) in identifying the AFB1 contamination of peanut kernels. Both PLS-DA and LS-SVM methods provided satisfactory classification results using the full spectral information over the ranges of 410-1070 (I), 1120-2470 nm (II) and I+II. Based on the classification threshold of 20 ppb, the best PLS-DA prediction results using the full spectra yielded the average accuracy of 87.9% and overall accuracy of 88.6%. With 100 ppb as the classification threshold, the best PLS-DA model using the full spectra achieved the average accuracy of 94.0% and overall accuracy of 91.4%. Using the full spectra, the best average accuracies recorded by LS-SVM were 90.9% and 98.0%, with the classification thresholds of 20 and 100 ppb, respectively. Correspondingly, the best overall accuracies by LS-SVM were 90.0% and 97.1%. In addition, the simplified models of CARS-PLS-DA and CARS-LS-SVM also demonstrated good prediction capability in identifying the AFB1 contamination from peanut surface. Based on both classification thresholds of 20 and 100 ppb, the best CARS-PLS-DA and CARS-LS-SVM prediction results were ≥ 90.0% in both average accuracy and overall accuracy. Most importantly, the computation complexity and the employed data dimensionality were significantly reduced by using the simplified models.
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.
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.
The food industry is always on the lookout for sensing technologies for rapid and nondestructive inspection of food products. Hyperspectral imaging technology integrates both imaging and spectroscopy into unique imaging sensors. Its application for food safety and quality inspection has made significant progress in recent years. Specifically, hyperspectral imaging has shown its potential for surface contamination detection in many food related applications. Most existing hyperspectral imaging systems use pushbroom scanning which is generally used for flat surface inspection. In some applications it is desirable to be able to acquire hyperspectral images on circular objects such as corn ears, apples, and cucumbers. Past research describes inspection systems that examine all surfaces of individual objects. Most of these systems did not employ hyperspectral imaging. These systems typically utilized a roller to rotate an object, such as an apple. During apple rotation, the camera took multiple images in order to cover the complete surface of the apple. The acquired image data lacked the spectral component present in a hyperspectral image. This paper discusses the development of a hyperspectral imaging system for a 3-D surface scan of biological samples. The new instrument is based on a pushbroom hyperspectral line scanner using a rotational stage to turn the sample. The system is suitable for whole surface hyperspectral imaging of circular objects. In addition to its value to the food industry, the system could be useful for other applications involving 3-D surface inspection.
Aflatoxin is a mycotoxin produced mainly by Aspergillus flavus (A.flavus) and Aspergillus parasitiucus fungi that grow naturally in corn. Very serious health problems such as liver damage and lung cancer can result from exposure to high toxin levels in grain. Consequently, many countries have established strict guidelines for permissible levels in consumables. Conventional chemical-based analytical methods used to screen for aflatoxin such as thin-layer chromatography (TLC) and high performance liquid chromatography (HPLC) are time consuming, expensive, and require the destruction of samples as well as proper training for data interpretation. Thus, it has been a continuing effort within the research community to find a way to rapidly and non-destructively detect and possibly quantify aflatoxin contamination in corn. One of the more recent developments in this area is the use of spectral technology. Specifically, fluorescence hyperspectral imaging offers a potential rapid, and non-invasive method for contamination detection in corn infected with toxigenic A.flavus spores. The current hyperspectral image system is designed for scanning flat surfaces, which is suitable for imaging single or a group of corn kernels. In the case of a whole corn cob, it is preferred to be able to scan the circumference of the corn ear, appropriate for whole ear inspection. This paper discusses the development of a hyperspectral imaging system for whole corn ear imaging. The new instrument is based on a hyperspectral line scanner using a rotational stage to turn the corn ear.
Naturally occurring Aspergillus flavus strains can be either toxigenic or atoxigenic, indicating their ability to produce
aflatoxin or not, under specific conditions. Corn contaminated with toxigenic strains of A. flavus can result in great
losses to the agricultural industry and pose threats to public health. Past research showed that fluorescence hyperspectral
imaging could be a potential tool for rapid and non-invasive detection of aflatoxin contaminated corn. The objective of
the current study was to assess, with the use of a hyperspectral sensor, the difference in fluorescence emission between
corn kernels inoculated with toxigenic and atoxigenic inoculums of A. flavus. Corn ears were inoculated with AF13, a
toxigenic strain of A. flavus, and AF38, an atoxigenic strain of A. flavus, at dough stage of development and harvested 8
weeks after inoculation. After harvest, single corn kernels were divided into three groups prior to imaging: control,
adjacent, and glowing. Both sides of the kernel, germplasm and endosperm, were imaged separately using a fluorescence
hyperspectral imaging system. It was found that the classification accuracies of the three manually designated groups
were not promising. However, the separation of corn kernels based on different fungal inoculums yielded better results.
The best result was achieved with the germplasm side of the corn kernels. Results are expected to enhance the potential
of fluorescence hyperspectral imaging 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.
Fluorescence hyperspectral imaging is increasingly being used for food quality inspection and detection of potential food
safety concerns. The flexible nature of a self-scanning pushbroom hyperspectral imager lends itself to these kinds of
applications, among others. To increase the use of this technique there has been a tendency to use low cost off-the-shelf
hyperspectral sensors which are typically not radiometrically calibrated. To ensure that these systems are optimized for
response and repeatability, it is imperative that the systems be both radiometrically and spectrally calibrated specifically
for fluorescence imaging. Fluorescence imaging provides several challenges such as low signal, stray light and a low
signal dynamic range that are improved with careful radiometric calibration. A radiometric and spectral approach that includes flat fielding and the conversion of digital number responses to radiance for calibrating this imaging system and other types of hyperspectral imagers is described in this paper. Results show that this method can be adopted for calibrating fluorescence and reflective hyperspectral imaging systems in the visible and near infra-red domains.
Aflatoxins are toxic secondary metabolites of the fungi Aspergillus flavus and Aspergillus parasiticus, among others.
Aflatoxin contaminated corn is toxic to domestic animals when ingested in feed and is a known carcinogen associated
with liver and lung cancer in humans. Consequently, aflatoxin levels in food and feed are regulated by the Food and
Drug Administration (FDA) in the US, allowing 20 ppb (parts per billion) limits in food and 100 ppb in feed for
interstate commerce. Currently, aflatoxin detection and quantification methods are based on analytical tests including
thin-layer chromatography (TCL) and high performance liquid chromatography (HPLC). These analytical tests require
the destruction of samples, and are costly and time consuming. Thus, the ability to detect aflatoxin in a rapid, nondestructive
way is crucial to the grain industry, particularly to corn industry. Hyperspectral imaging technology offers a
non-invasive approach toward screening for food safety inspection and quality control based on its spectral signature.
The focus of this paper is to classify aflatoxin contaminated single corn kernels using fluorescence hyperspectral
imagery. Field inoculated corn kernels were used in the study. Contaminated and control kernels under long wavelength
ultraviolet excitation were imaged using a visible near-infrared (VNIR) hyperspectral camera. The imaged kernels were
chemically analyzed to provide reference information for image analysis. This paper describes a procedure to process
corn kernels located in different images for statistical training and classification. Two classification algorithms,
Maximum Likelihood and Binary Encoding, were used to classify each corn kernel into "control" or "contaminated" through pixel classification. The Binary Encoding approach had a slightly better performance with accuracy equals to 87% or 88% when 20 ppb or 100 ppb was used as classification threshold, respectively.
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.
Aflatoxin contaminated corn poses a serious threat to both domestic animals and humans, because of its carcinogenic
properties. Traditionally, corn kernels have been examined for evidence of bright greenish-yellow fluorescence (BGYF),
which is an indication of possible presence of Aspergillus flavus, one of the aflatoxin producing strains of fungi, when
illuminated with a high-intensity ultra-violet light. The BGYF test is typically the first step that leads to an in-depth
chemical analysis for possible aflatoxin contamination. The objective of the present study was to analyze hyperspectral
BGYF response of corn kernels under UVA excitation. The target corn samples were collected from a commercial corn
field in 2005 and showed abundant BGYF response. The BGYF positive kernels were manually picked out and imaged
under a visible near-infrared hyperspectral imaging system under UV radiation with excitation wavelength centered at
365 nm. Initial results exhibited strong emission spectra with peaks centered from 500 nm to 515 nm wavelength range
for BGYF positive kernels. Aflatoxin levels on the BGYF positive and negative corn kernels (used as control) were
measured subsequently with high performance liquid chromatography. The mean aflatoxin concentration level was 5114
ppb for the BGYF positive and undetectable for the normal kernels.
Soil erosion and its related runoff is a serious problem in U.S. agriculture. USDA has classified 27% of U.S. agricultural land as being highly erodible. Because of the erosion, rivers, lakes, and water table are contaminated due to the agriculture chemicals such as nitrogen, phosphorus, and pesticides contained in the runoff water. This is a serious environmental problem nationwide. It is well recognized that residue coverage on the soil surface can reduce soil erosion. The objective of this paper was to explore the potential of using ASTER data for soybean plant residue cover estimation. In the spring of 2004, personnel from Natural Resource Conservation Service (NRCS) and Institute for Technology Development (ITD) did a traditional windshield survey in three Indiana Counties, Wabash, Huntington, and Grant. Fields with greater than 30% residue cover were classified as conservation tillage (no till); those with 16-30% residue cover as reduced tillage; and those with less than 15% residue cover as traditional tillage. ASTER data was collected over the study sites on April 14, 2004. Spectral information was extracted from the ASTER image for statistical analysis. Field values for various indices were calculated from the reflectance data. Residue coverage estimation from the survey was used as the ground truth for the field. Analysis was performed to determine the capability of ASTER data to identify crop residue coverage. The initial results indicated that ASTER imagery has moderate capability to identify residue coverage - or tillage practice within the soybean fields.
Aflatoxin contaminated corn is dangerous for domestic animals when used as feed and cause liver cancer when consumed by human beings. Therefore, the ability to detect A. flavus and its toxic metabolite, aflatoxin, is important. The objective of this study is to measure A. flavus growth using hyperspectral technology and develop spectral signatures for A. flavus. Based on the research group's previous experiments using hyperspectral imaging techniques, it has been confirmed that the spectral signature of A. flavus is unique and readily identifiable against any background or surrounding surface and among other fungal strains. This study focused on observing changes in the A. flavus spectral signature over an eight-day growth period. The study used a visible-near-infrared hyperspectral image system for data acquisition. This image system uses focal plane pushbroom scanning for high spatial and high spectral resolution imaging. Procedures previously developed by the research group were used for image calibration and image processing. The results showed that while A. flavus gradually progressed along the experiment timeline, the day-to-day surface reflectance of A. flavus displayed significant difference in discreet regions of the wavelength spectrum. External disturbance due to environmental changes also altered the growth and subsequently changed the reflectance patterns of A. flavus.
The present report evaluated ultraviolet radiation (UVR) effects on the spectral signature of mycotoxin producing fungus Aspergillus flavus (A. flavus). Ultraviolet radiation has long been used to reduce microbe contamination and to inactivate mold spores. In view of the known effects of UVR on microorganisms, and because certain spectral bands in the signature of some fungi may be in the UV range, it is important to know the maximum acceptable limit of UVR exposure that does not significantly alter the fungal spectral signature and affect detection accuracy. A visible-near-infrared (VNIR) hyperspectral imaging system using focal plane pushbroom scanning for high spatial and spectral resolution imaging was utilized to detect any changes. A. flavus cultures were grown for 5 days and imaged after intermittent or continuous UVR treatment. The intermittent group was treated at 1-minute intervals for 10 minutes, and VNIR images were taken after each UVR treatment. The continuous group was irradiated for 10 minutes and imaged before and after treatment. A control sample group did not undergo UVR treatment, but was also imaged at 1-minute intervals for 10 minutes in the same manner as the intermittent group. Before and after UVR treatment, mean fungal sample reflectance was obtained through spatial subset of the image along with standard deviation and pre- and post-treatment reflectance was compared for each sample. Results show significant difference between the reflectances of treated and control A. flavus cultures after 10 min of UV radiation. Aditionally, the results demonstrate that even lethal doses of UVR do not immediately affect the spectral signature of A. flavus cultures suggesting that the excitation UV light source used in the present experiment may be safe to use with the UV hyperspectral imaging system when exposure time falls below 10 min.
USDA and the Institute for Technology Development are currently collaborating on a project using hyperspectral imagery to detect pathogens such as mycotoxin producing molds in grain products. The initial experiments are being implemented on corn kernels. When molds appear on corn, reflectance spectra from the molds and corn are mixed. Therefore, it is important to characterize the corn reflectance, which is the background reflectance in the image. The objective of this study was to qualitatively identify and quantify kernel signatures of several corn genotypes. Four different corn genotypes (genetically distinct corn lines) and four near isogenic corn lines were prepared at the USDA laboratory. The study used a visible-near-infrared hyperspectral imaging system for data acquisition. The imaging system utilizes focal plane pushbroom scanning for high spatial and high spectral resolution imaging. Procedures were developed for optimum image calibration and image processing. It was expected that the results would be useful for reducing the background influence of corn in mold detection and would also be applicable in corn genotype identification, especially among corn lines with different resistance levels to molds.
Aerial hyperspectral imagery has been used to find the temporal relationship between image and corn yield. A total of
five hyperspectral images were taken during the growing season. For each image, the optimal vegetation index was
selected among many candidate vegetation indices. At the same time, the optimal band subset was selected to calculate
the vegetation index. The optimal band subset has the minimum number of bands and represents the most significant
image bands (or wavelength) for yield prediction. The optimization process used the EAVI (Evolutionary Algorithm
based Vegetation Index generation) algorithm. Results showed that the EAVI algorithm generated the best vegetation
index among many comparison indices for yield estimation. For image taken at different date, the algorithm selected a
different optimal vegetation index and image bands. The most common sensitive wavelength identified was in the red
edge at 700 nm and in the NIR region at 826 nm. This study showed that images taken from the beginning of full canopy
coverage to the corn ear formation period provided the best and stable result for corn yield estimation. It is suggested
that this period of time during the growing season would have great potential for remote sensing based corn yield
prediction.
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