Abstract
An intelligent control system for thermal processing of natural biomaterials, based on machine vision, sensor fusion and neural network was developed. Experiments with ginseng drying showed advantages of machine vision for real-time imaging of morphological, colour and texture attributes, providing sufficient discriminatory information about biomaterial moisture and quality in the range of 3.2-0.1 g/g and temperatures from 30 to 50degC. Both moisture and quality was estimated by using neural network models: moisture with 6-8% error and quality with 10-16% error. Online estimates of moisture and quality were used for temperature control in pilot batch dryer. Testing of the intelligent control system with embedded machine-vision observer (IMAQ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TM</sup> Vision Builder) and controller (Lab View 7.0) showed stability and robustness, combined with high accuracy of temperature control. Multi-stage optimization of temperature with respect to quality allowed decrease of drying time from 240 to 90-110 hours with appropriate final quality.
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