Evaluation of Sensing and Machine Vision Techniques in Stress Detection and Quality Evaluation of Turfgrass Species
Narra, Siddhartha
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https://hdl.handle.net/2142/83121
Description
Title
Evaluation of Sensing and Machine Vision Techniques in Stress Detection and Quality Evaluation of Turfgrass Species
Author(s)
Narra, Siddhartha
Issue Date
2007
Doctoral Committee Chair(s)
Fermanian, Thomas W.
Department of Study
Natural Resrouces and Environmental Sciences
Discipline
Natural Resrouces and Environmental Sciences
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Environmental Sciences
Language
eng
Abstract
The utility of different image sensing and non-image sensing techniques was also studied in objectively evaluating turfgrass quality parameters like, color, density and texture from National Turfgrass Evaluation Program trials. Image sensing took the greatest amount of time, while non-image sensing techniques were the fastest among the evaluated methods. All methods showed significant differences in cultivars for color in different trials. The quantified hue values from the multispectral camera were the least correlated with other evaluation techniques. Both chlorophyll meter and turf color meter showed potential in quantifying turfgrass color with greater consistency. However, the narrow separation obtained using turf color meter may not allow cultivar differentiation from species with less genetic color variation. Texture evaluation of turfgrasses was done after developing and implementing the run length encoding algorithm (RLE) on simulated turf built using twist ties in both planar and turf-type arrangements. Significant relationship was observed between manual measurements of twist ties and RLE-derived values. The algorithm implementation on true turfgrass images collected under greenhouse and field conditions from Kentucky bluegrass showed significantly positive relationship between RLE values and visual evaluation ratings. The possibility of collecting and analyzing images from multiple plots for color quantification was also evaluated successfully using an elevated platform from both Kentucky bluegrass and fairway bentgrass trials.
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