Wheat grain quality is defined by several parameters, of which insect and fungal damage and sprouting are considered important degrading factors. At present, Canadian wheat is inspected and graded manually by Canadian Grain Commission (CGC) inspectors at grain handling facilities or in the CGC laboratories. Visual inspection methods are time consuming, less efficient, subjective, and require experienced personnel. Therefore, an alternative, rapid, objective, accurate, and cost effective technique is needed for grain quality monitoring in real-time which can potentially assist or replace the manual inspection process. Insect-damaged wheat samples by the species of rice weevil (Sitophilus oryzae), lesser grain borer (Rhyzopertha dominica), rusty grain beetle (Cryptolestes ferrugineus), and red flour beetle (Tribolium castaneum); fungal-damaged wheat samples by the species of storage fungi namely Penicillium spp., Aspergillus glaucus, and Aspergillus niger; and artificially sprouted wheat kernels were obtained from the Cereal Research Centre (CRC), Agriculture and Agri-Food Canada, Winnipeg, Canada. Field damaged sprouted (midge-damaged) wheat kernels were procured from five growing locations across western Canada. Healthy and damaged wheat kernels were imaged using a long-wave near-infrared (LWNIR) and a short-wave near-infrared (SWNIR) hypersprctral imaging systems and an area scan color camera. The acquired images were stored for processing, feature extraction, and algorithm development. The LWNIR classified 85-100% healthy and insect-damaged, 95-100% healthy and fungal-infected, and 85-100% healthy and sprouted/midge-damaged kernels. The SWNIR classified 92.7-100%, 96-100% and 93.3-98.7% insect, fungal, and midge-damaged kernels, respectively (up to 28% false positive error). Color imaging correctly classified 93.7-99.3%, 98-100% and 94-99.7% insect, fungal, and midge-damaged kernels, respectively (up to 26% false positive error). Combined the SWNIR features with top color image features correctly classified 91-100%, 99-100% and 95-99.3% insect, fungal, and midge- damaged kernels, respectively with only less than 4% false positive error.

The agriculture industry experiences severe economic losses each year when wheat crops become infected with Fusarium and the mycotoxin Deoxynivalenol (DON). This research investigated the feasibility of using near infrared hyperspectral imaging to detect Fusarium damage and DON in Canadian Western Red Spring wheat. Four samples were selected from each grain grade resulting in 16 samples and 240 hyperspectral data cubes. The data cubes were calibrated to the system, the consistent spectra was found and then a 1- nearest neighbour classifier was generated. Grade percentages were computed and used to generate two 3- nearest neighbour classifiers, one for identifying Fusarium damage and the other for identifying DON content. The Fusarium damage classifier had an accuracy of 85% and the DON content classifier had an accuracy of 80%. While a single sample image classification will not replace manual testing, the use of multiple samples from one harvest could reduce manual inspections.

Dissertation (Ph.D.)--Auburn University, / Abstract. Vita. Includes bibliographic references (p.102-108).

Hyperspectral imaging is the collection, processing and analysis of spectral data in numerous contiguous wavelength bands while also providing spatial context. Some of the commonly used instruments for hyperspectral imaging are pushbroom scanning imaging systems, grating based imaging spectrometers and more recently electronically tunable filters. Electronically tunable filters offer the advantages of compactness and absence of mechanically movable parts. Electronically tunable filters have the ability to rapidly switch between wavelengths and provide spatial and spectral information over a large wavelength range. They involve the use of materials whose response to light can be altered in the presence of an external stimulus. While these filters offer some unique advantages, they also present some equally unique challenges. This research work involves the design and development of a multichannel imaging system using electronically tunable Liquid Crystal Fabry-Perot etalons. This instrument is called the Liquid Crystal Hyperspectral Imager (LiCHI). LiCHI images four spectral regions simultaneously and presents a trade-off between spatial and spectral domains. This simultaneity of measurements in multiple wavelengths can be exploited for dynamic and ephemeral events. LiCHI was initially designed for multispectral imaging of space plasmas but its versatility was demonstrated by testing in the field for multiple applications including landscape analysis and anomaly detection. The results obtained after testing of this instrument and analysis of the images are promising and demonstrate LiCHI as a good candidate for hyperspectral imaging. The challenges posed by LiCHI for each of these applications have also been explored.

This thesis focuses on developing a nondestructive strategy for measuring the quality of food using hyperspectral imaging. The specific focus is to develop a classification methodology for detecting bruised/unbruised areas in hyperspectral images of fruits such as strawberries through the classification of pixels containing the edible portion of the fruit. A multiband segmentation algorithm is formulated to generate a mask for extracting the edible pixels from each band in a hypercube. A key feature of the segmentation algorithm is that it makes no prior assumptions for selecting the bands involved in the segmentation. Consequently, different bands may be selected for different hypercubes to accommodate the intra-hypercube variations. Gaussian univariate classifiers are implemented to classify the bruised-unbruised pixels in each band and it is shown that many band classifiers yield 100% classification accuracies. Furthermore, it is shown that the bands that contain the most useful discriminatory information for classifying bruised-unbruised pixels can be identified from the classification results. The strategy developed in this study will facilitate the design of fruit sorting systems using NIR cameras with selected bands.

No description available.

Quantification of spectral data is of great interest in many fields of science, since it can provide further insight into other properties of an object. However, traditional cameras are usually made to image the world in a similar fashion as to how we see it, wherefore they are usually not fit to record nor measure further spectral information. To get a better insight into the spectral properties of an object, a hyperspectral camera might be of use, since those can often identify and measure hundreds of different spectral bands. In this study we look at the construction and further development of an existing design of a push broom hyperspectral imaging system, built with optics for a fraction of the cost of commercial ones. With developed software and objects at hand a spectral calibration was performed, showing a possible spectral range of 184(2)-918(11) nm, but the use of the whole spectral range was however not possible due to limitations in the transmissivity of the lenses below 350 nm. A shift of the spectral range towards longer wavelengths is proposed, which would give further insight into the near infrared spectrum without any information losses. It was found that the spectral calibration of the imager was the main limiting factor of the system, since inaccuracies up to ±11 nm were identified, while the resolution has been found to be 1.4 nm in previous studies, proving that better calibrations are of essence. In good operating conditions, the resolution in the angle of view of the imager was found to be 0.55 mdeg. If the measurement conditions are not as good, or if such kind of spatial resolution is not required, a camera with a smaller detector size and larger pixels could be used to lower the cost of the system without a deterioration in image quality, since the uncertainties in the calibrations and measurement conditions were found to be the limiting factor.

Hyperspectral imagery is a highly dimensional type of data resulting in high computational costs during analysis. Band selection aims to reduce the original hyperspectral image to a smaller subset that reduces these costs while preserving the maximum amount of spectral information within the data. This thesis explores various types of band selection techniques used in hyperspectral image processing. Modifying Neural network-based techniques and observing the effects on the band selection process due to the change in network architecture or objective are of particular focus in this thesis. Herein, a generalized neural network-based band selection technique is developed and compared to state-of-the-art algorithms that are applied to a unique dataset and the Pavia City Center dataset where the subsequent selected bands are fed into a classifier to gather comparison results.

No description available.

Over the past decade, graphene/nanomaterial hybrids have gained a great interest in various applications due to their unique optical properties. This work explores lanthanide doped upconverting nanoparticles (UCNPs)/graphene hybrid nanomaterials. Here, core/shell structures comprising β-NaGdF4:Y b3+(20%),Er3+(2%)@NaGdF4 and α-NaGdF4:Y b3+(20%), Er3+(2%)@NaGdF4 with oleate as capping agent were synthesized and characterized. The choice of lanthanide ions (Yb3+ and Er3+) and their concentrations plays an important role to make these nanoparticles undergo two optical processes (upcoversion and downshifting) capable to convert near-infrared excitation to visible and near-infrared emission. In order to make hybrid systems, these nanoparticles were combined with graphene films. The morphology and the optical behavior of the hybrid samples were studied by microscope and hyperspectral imaging. The multi-energy sublevels from the 4f electronic configuration of lanthanides, their long excited state lifetime and the high carrier mobility of the graphene expected to open an exciting possibility of interaction, however, UCNPs/Graphene hybrid nanomaterial exhibits a minimal response when subjected to 980 nm laser illumination.