Scholarship & Research
Permanent URI for this communityhttps://scholarworks.montana.edu/handle/1/1
Browse
2 results
Search Results
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 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.