Theses and Dissertations at Montana State University (MSU)
Permanent URI for this collectionhttps://scholarworks.montana.edu/handle/1/733
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Item Calibration and characterization of a VNIR hyperspectral imager for produce monitoring(Montana State University - Bozeman, College of Engineering, 2020) Logan, Riley Donovan; Chairperson, Graduate Committee: Joseph A. Shaw; Joseph A. Shaw was a co-author of the article, 'Measuring the polarization response of a VNIR hyperspectral imager' in the journal 'SPIE proceedings' which is contained within this thesis.; Bryan Scherrer, Jacob Senecal, Neil S. Walton, Amy Peerlinck, John W. Sheppard, and Joseph A. Shaw were co-authors of the article, 'Hyperspectral imaging and machine learning for monitoring produce ripeness' in the journal 'SPIE proceedings' which is contained within this thesis.Hyperspectral imaging is a powerful remote sensing tool capable of capturing rich spectral and spatial information. Although the origins of hyperspectral imaging are in terrestrial remote sensing, new applications are emerging rapidly. Owing to its non-destructive nature, hyperspectral imaging has become a useful tool for monitoring produce ripeness. This paper describes the process of characterizing and calibrating a visible near-infrared (VNIR) hyperspectral imager for obtaining accurate images of produce to be used in machine learning algorithms for analysis. In this work, many calibrations and characterization are outlined, including: a radiance calibration, the process of calculating reflectance, pixel uniformity and image stability testing, spectral characterization, illumination source analysis, and measurement of the polarization response. The images obtained by the calibrated hyperspectral imager were converted to reflectance across a spectral range of 387.12 nm to 1023.5 nm, with a spectral resolution of 2.12 nm. A convolutional neural network was used to perform age classification for Yukon Gold potatoes, bananas, and green peppers. Additionally, a genetic algorithm was used to determine the wavelengths carrying the most useful information for age classification. Experiments were run using red green blue (RGB) images, full-spectrum hyperspectral images, and the wavelengths selected by the genetic algorithm feature selection method. Preliminary data from these analyses show promising results at accurately classifying produce age. The genetic algorithm feature selection method is being used to develop a low-cost multispectral imager for use in monitoring produce in grocery stores.Item Development of a smart camera system using a system on module FPGA(Montana State University - Bozeman, College of Engineering, 2017) Dack, Connor Aquila; Chairperson, Graduate Committee: Ross K. SniderImaging systems can now produce more data than conventional PCs with frame grabbers can process in real-time. Moving real-time custom computation as close as possible to the image sensor will alleviate the bandwidth bottle-neck of moving data multiple times through buffers in conventional PC systems, which are also computation bottlenecks. An example of a high bandwidth, high computation application is the use of hyperspectral imagers for sorting applications. Hyperspectral imagers capture hundreds of colors ranging from the visible spectrum to the infrared. This masters thesis continues the development of the hyperspectral smart camera by integrating the image sensor with a field programmable gate array (FPGA) and by developing an object tracking algorithm for use during the sorting process, with the goal of creating a single compact embedded solution. An FPGA is a hardware programmable integrated circuit that can be reprogrammed depending on the application. The prototype integration involves the development of a custom printed circuit board to connect the data and control lines between the sensor, the FPGA, and the control code to read data from the sensor. The hyperspectral data is processed on the FPGA and is combined with the object edges to make a decision on the quality of the object. The object edges are determined using a line scan camera, which provides data via the Camera Link interface, and a custom object tracking algorithm. The object tracking algorithm determines the horizontal edges and center of the object while also tracking the vertical edges and center of the object. The object information is then passed to the air jet sorting subsystem which ejects bad objects. The solution is to integrate the hyperspectral image sensor, the two processing algorithms, and Camera Link interface into a single, compact unit by implementing the design on the Intel Arria 10 System on Module with custom printed circuit boards.Item Hyper-spectral microscope: auto-focusing(Montana State University - Bozeman, College of Engineering, 2018) Lozano, Kora Michelle; Chairperson, Graduate Committee: Ross K. SniderThis thesis is part of a larger project to develop a hyper-spectral microscope, to be used to find the optimal growing conditions for human inducible pluripotent stem cells. The hyper-spectral microscope is being developed by the Department of Chemistry and Biochemistry at Montana State University (MSU). Specifically, the hyper-spectral microscope is being developed to aide in live cell imaging, reduce cell stress from laser excitation, increase the number of markers possible at once, and keep costs down compared to non-hyper-spectral set-ups of similar capability. To the knowledge of those involved in this project it is the first of its kind. The scope of this thesis centers on implementing an auto-focusing algorithm for the hyper-spectral imager.