Machine vision sensing of Ajuga plant cell suspension cultures
Li, Zhiwei
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Permalink
https://hdl.handle.net/2142/19872
Description
Title
Machine vision sensing of Ajuga plant cell suspension cultures
Author(s)
Li, Zhiwei
Issue Date
1996
Doctoral Committee Chair(s)
Reid, John F.
Department of Study
Agricultural and Biological Engineering
Discipline
Agricultural and Biological Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
Ph.D.
Degree Level
Dissertation
Keyword(s)
Biology, Cell
Engineering, Agricultural
Engineering, Biomedical
Engineering, Electronics and Electrical
Artificial Intelligence
Language
eng
Abstract
A computer controlled machine vision sensing system for in vitro production of plant pigments was designed and implemented. The system consisted of a machine vision microscopic sensing subsystem and a computer controlled automatic sampling/delivery subsystem.
The machine vision microscopic sensing system included a central control computer, color CCD camera, microscope, and a machine vision system. The central control computer synchronized with the other subsystem, sending commands to the sampling control computer to start the sampling process. It also performed the functions of acquiring color images and analyzing images. The sampling system performed the functions of diluting samples, delivering samples and sampling loop and flow-cell cleaning. The communication between the central control computer and the sampling control computer was through serial ports on both computers.
Microscopic color images from suspension cultures of anthocyanin-producing Ajuga cells were analyzed with machine vision to estimate cell culture mass and pigment productivity. For suspension culture analysis, the detailed RGB (red-green-blue) information extracted in each of the color microscopic images did not allow separation of pigmented cells and cell aggregates from non-pigmented entities and background in the medium, however, conversion of RGB data to the HSI (hue-saturation-intensity) coordinate system permitted clear separation of object classes based on a combination of dimensional and photometric information. Further segmentation using saturation and intensity characteristics of the HSI values allowed categorization of low, medium, and highly-pigmented cells and aggregates in the mixed suspension culture, and both machine vision on-line and off-line data were able to track both biomass accumulation and anthocyanin pigment formation over time as verified by conventional (mass and spectrophotometric) analysis of the same culture.
The degree of Ajuga cell aggregation was shown to correlate with cell anthocyanin formation. With machine vision, a mathematical methodology was proposed to evaluate the degree of cell aggregation for Ajuga plant cells. Machine vision estimation and manual estimation of aggregates was compared.
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