Establish And Application Research Of Vegetable Color Quantitative System

A lot of nutrition needed by human come from the vegetable. Through analysing the nutrition composition of vegetable, scientists discover that the nutrition value and its color have certain relations. It has very important meaning to grasp and control the color change in course of vegetable processing, and quantify the color. At present the analysis course of pigment composition in color research is not only fussy but also can't give person actual color which is felt by eye, it is impossible to satisfy people. But the appraisement for product color using computer visual system not only can conquer the difference caused by the exhausted eye and the limitation of appraisement, but also can make judgment correspondingly using the color difference of each part. Because of the restriction of hardware condition, now the commercial online inspecting system still has not appearance in our country, the color classification of manpower is adopted. Therefore, the computer visual system was established in this paper, which can carry through collection, management, display, and appraisement of vegetable color. This study is a part of the Quartermaster ministry project "Warship Vegetable Fresh-Keeping Technology".Several crucial aspects are discussed as followed in this paper: 1) A suit of computer vision system used in color quantification measurement was established on the basis of knowing the composition of human vision and the course of information processing. Measuring the color of object surface must carry through under certain light source; the spectrum power distribution of different light source is different, therefore, the color presenting on object surface is different under their shining. The color toleration quantum range of standard illumination body A and D65 recommend by CIE is near round, therefore, the position and distance of two color points can reflect accurately consciousness discrepancy between them on this chromaticity figure, and the color consciousness discrepancy can be judged rightly by the conception color difference. Through the experiment we can discover that D65 light is more suited to test the color change of green vegetable, therefore, D65 light is used in the color quantitative system in this paper.2) Using the 1980A color luminance meter to mark the color quantification system, the color luminance meter and CCD camera synchronally measure the same color card, the output of CCD camera is RGB value, and the color luminance meter is XYZ value. The measure value of standard color card with 1980A color luminance meter is regarded as the real color value of target card for the computer image collection system, and that CCD camera is a non-linear respond, the conversion relation between RGB and CIEXYZ color space is also a complex non-linear relation, we suggest to complete this conversion by neural network.3) On the basis of color theory, the software system is established. All the image files in this system are BMP image format. Because the color card quantities of full chromatogram are too many, the training time of neural network is long, convergence speed is slow, and error is difficult to control. Therefore, for raising the forecast precision, this paper has divided all the sample-collections of full chromatogram into several independent subclasses, each subclass applies its own independence neural network to train, and has given the training course of the green neural network.4) The conversion relation between RGB color space of computer visual system and CIEXYZ color space is realized by three-layer BP network, which hide layer has 10 node number and its transfer function adopts tansig function; transfer function of output layer selects purelin function; the training error is 3.50324×10-5, the testing error is 1.4063×10-4, which satisfies the preconcerted demand, so the neural network can be used in realizing this relation mapping. We find the BP network structure used in the conversion between RGB and CIEXYZ color space, gain the weight and threshold value of the neural...