ABSTRACT:
Quality is a key issue for modern food industry because the high-quality of product is the basis for
success in today’s highly competitive market. The first human perception on the quality of food is
usually judged by the physical appearance. Good physical appearance normally relates to the good
quality of the inner properties. Since the visual appearance of food and other biological products is a
major factor in the judgment of quality, visual inspection is an important part of quality control in these
industries. In the food industry, the quality evaluation still heavily depends on manual inspection, which
is tedious, arduous, and costly, and is easily influenced by physiological factors, including subjective
and inconsistent evaluation results. The inspection has historically been performed by use of only the
human eye whose assessment furnishes above all qualitative but hardly quantitative data. The tools of
data acquisition, automatic evaluation and direct control of the ambient conditions are very limited
hence it is necessary to improve quality evaluation of food to satisfy the increased awareness,
sophistication and greater expectation of consumers [1].
Most of the applications of learning algorithms in computer vision for food quality evaluation are for
classification and prediction; nevertheless, there are also some for image segmentation and feature
selection [2]. The need for visual quality control in combination with the increasing automation in all
fields of production leads to a demand for automatic and objective evaluation of visual parameters like
size, form, structure, etc. Machine vision systems in the form of cameras, illumination, frame grabbers
and computers provide a solution that may satisfy this demand, typically using gray scale imaging.
While objects that are relatively simple in texture may be separated into their subcomponents using
gray scale image analysis while for more complicated objects with varying colour composition, it is
necessary to involve the colour characteristics of the objects, in order to obtain a proper classification
and to perform subsequent analysis.
Learning techniques have been applied increasingly for food quality evaluation using computer vision,
which include artificial neural network, statistical learning, fuzzy logic, genetic algorithm, and decision
tree. Along with the measurement of physical parameters such as temperature and humidity equipped
with electronic feedback to the control system a suitably designed image analysis system may, based on
fuzzy logic, be a major factor in providing the guarantee for a high product quality and process control
or understanding in a wide segment of the food processing industry [1,2].